Graph Representation Learning

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Overview

Graph representation learning studies how to turn relational data into useful representations while preserving information about connectivity, node and edge attributes, and sometimes time or geometry. Common approaches include message-passing graph neural networks, spectral and positional encodings, graph Transformers, and self-supervised methods such as contrastive learning and masked prediction. These representations support tasks including node classification, link prediction, graph comparison, clustering, and molecular or social prediction; newer work also explores higher-order structure and foundation models that transfer across graphs. A central challenge is balancing expressive, scalable models with reliable generalization across graph sizes, domains, feature types, and structural shifts, while establishing when graph structure helps beyond simpler baselines.

Latest papers

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305 papers33 papers in the last four weeks, up 3% on the four weeks before. 0.2% of all new papers.

May 4, 2026

AdNGCL: Adaptive Negative Scheduling for Graph Contrastive Learning

Negative samples play a crucial role in graph contrastive learning (GCL), yet existing methods mainly focus on which negatives should be selected, mined, filtered, or progressively introduced. Less attention has been given to separating the negative candidates available to an anchor from the subset that actively participates in optimization, and to adapting this exposure according to training behavior. To address this gap, we propose the Adaptive Negative Scheduling for Graph Contrastive Learning (AdNGCL) framework, which maintains a bounded per-anchor candidate reservoir and periodically reassesses candidate hardness as representations evolve. Its core component, Hardness-Aware Negative Scheduling (HANS), uses a category-specific training-response signal to determine whether exposure to easy, intermediate, or hard negatives should increase at each scheduling opportunity. Furthermore, reliability-aware candidate refresh handles potentially positive-like candidates, while within-category diversity refresh changes active identities without altering HANS-controlled exposure. AdNGCL retains a standard symmetric InfoNCE objective. Experiments on ten benchmark datasets against state-of-the-art self-supervised graph learning methods show that AdNGCL achieves the highest mean Micro-F1 among the evaluated methods on eight datasets, with improvements of up to 1.30 percentage points over the second-best method, ranks second on Actor, and remains competitive on Coauthor-CS. Ablation, sensitivity, scheduler and reliability diagnostics, and computational analyses further examine the behavior and contribution of the proposed mechanisms.

Adnan Ali, Jinlong Li, Syed Muhammad Israr +3

School of Computer Science and Technology, University of Science and Technology of China, Hefei, China · Hainan University, Haikou, Hainan, China · Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom

Graph Representation LearningGraph SSLGraph Contrastive Learning
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May 2, 2026

PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies

Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial resolutions, ranging from atomic interactions to global fold topology. However, existing protein representation learning methods typically operate at a single structural level or treat different sources of structural information as parallel modalities, without explicitly modeling their hierarchical relationships. We introduce PRIME (Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies), a unified framework that models proteins as a nested family of five physically grounded structural graphs spanning surface, atomic, residue, secondary-structure, and protein levels. Adjacent levels are connected through deterministic, physics-informed assignment operators, enabling bidirectional information exchange via bottom-up aggregation and top-down contextual refinement. Experiments on standard protein representation learning benchmarks demonstrate strong and competitive performance across diverse tasks, with particularly notable gains on the Fold Classification benchmark, where PRIME outperforms the strongest geometric GNN baseline by margins of 13.80 and 18.30 points on the harder Superfamily and Fold splits, and achieves a state-of-the-art accuracy of 84.10% on Reaction Class prediction, surpassing all baseline methods, including ESM. Ablation studies confirm that each structural level contributes complementary and non-redundant information, and adaptive cross-attention analysis reveals that PRIME autonomously identifies the most task-relevant structural resolutions at prediction time. Our source code is publicly available at https://github.com/HySonLab/PRIME

Viet Thanh Duy Nguyen, John K. Johnstone, Truong-Son Hy

University of Alabama at Birmingham Birmingham, Alabama, USA

Graph Representation LearningHierarchical Representation LearningEquivariant GNNsProtein Representation LearningMolecular Representation Learning
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May 2, 2026

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning

Graph self-supervised learning typically relies on large-scale unlabeled datasets, heavily inflating computational costs. However, empirical evidence suggests that these datasets contain substantial redundancy-our analysis reveals that uniformly subsampling 50% of graphs retains over 96% of downstream performance. To exploit this redundancy, we introduce GraphSculptor for pre-training coreset construction. Unlike methods dependent on additional training-time signals or limited solely to topological statistics, GraphSculptor provides a label-free solution that constructs coresets via two complementary perspectives: intrinsic structure and contextual semantics. Concretely, structural diversity is quantified using intrinsic graph statistics, yielding a structural feature vector for each graph, while semantic diversity is captured by utilizing a pre-trained language model to encode descriptions generated via graph-to-text. GraphSculptor integrates these signals into a unified metric space and performs cluster-aware selection to preserve joint structural-semantic diversity. We further derive a theoretical bound on the loss gap between coreset and full-data pre-training, offering theoretical motivation for our selection formulation. Extensive experiments demonstrate that GraphSculptor effectively sculpts the dataset: a 10% coreset achieves 99.6% of full-data performance while reducing pre-training time by nearly 90%, offering a scalable solution for data-efficient graph pre-training.

Chuang Liu, Zelin Yao, Xueqi Ma +4

Sangfor Technologies Inc. · Wuhan University · The University of Melbourne +2

Graph Representation LearningSelf-Supervised Pre-TrainingGraph SSLCoreset SelectionData-Efficient Learning
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May 1, 2026

Aitchison Embeddings for Learning Compositional Graph Representations

Representation learning is central to graph machine learning, powering tasks such as link prediction and node classification. However, most graph embeddings are hard to interpret, offering limited insight into how learned features relate to graph structure. Many networks naturally admit a role-mixture view, where nodes are best described as mixtures over latent archetypal factors. Motivated by this structure, we propose a compositional graph embedding framework grounded in Aitchison geometry, the canonical geometry for comparing mixtures. Nodes are represented as simplex-valued compositions and embedded via isometric log-ratio (ILR) coordinates, which preserve Aitchison distances while enabling unconstrained optimization in Euclidean space. This yields intrinsically interpretable embeddings whose geometry reflects relative trade-offs among archetypes and supports coherent behavior under component restriction; we consider both fixed and learnable ILR bases. Across node classification and link prediction, our method achieves competitive performance with strong baselines while providing explainability by construction rather than post-hoc. Finally, subcompositional coherence enables principled component restriction: removing and renormalizing subsets preserves a well-defined geometry, which we exploit via subcompositional dimensionality removal to probe how archetype groups influence representations and predictions.

Nikolaos Nakis, Chrysoula Kosma, Panagiotis Promponas +2

Human Nature Lab, Yale University, New Haven, USA · Universit´e Paris Saclay, Universit´e Paris Cit´e, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Gif-sur-Yvette, France · Yale Institute for Network Science, Yale University, New Haven, USA +2

Neural Representation GeometryGraph Representation LearningCompositional Representation Learning
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Apr 30, 2026

LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis

Electroencephalogram (EEG) signals are vital for automated seizure detection, but their inherent noise makes robust representation learning challenging. Existing graph construction methods, whether correlation-based or learning-based, often generate redundant or irrelevant edges due to the noisy nature of EEG data. This significantly impairs the quality of graph representation and limits downstream task performance. Motivated by the remarkable reasoning and contextual understanding capabilities of large language models (LLMs), we explore the idea of using LLMs as graph edge refiners. Specifically, we propose a two-stage framework: we first verify that LLM-based edge refinement can effectively identify and remove redundant connections, leading to significant improvements in seizure detection accuracy and more meaningful graph structures. Building on this insight, we further develop a robust solution where the initial graph is constructed using a Transformer-based edge predictor and multilayer perceptron, assigning probability scores to potential edges and applying a threshold to determine their existence. The LLM then acts as an edge set refiner, making informed decisions based on both textual and statistical features of node pairs to validate the remaining connections. Extensive experiments on TUSZ dataset demonstrate that our LLM-refined graph learning framework not only enhances task performance but also yields cleaner and more interpretable graph representations.

Lincan Li, Zheng Chen, Yushun Dong

Department of Computer Science, Florida State University · 2SANKEN, The University of Osaka

Graph Structure LearningElectroencephalographyEEG Seizure DetectionGraph Representation Learning
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Apr 30, 2026

A Unified Framework of Hyperbolic Graph Representation Learning Methods

Hyperbolic geometry has emerged as an effective latent space for representing complex networks, owing to its ability to capture hierarchical organization and heterogeneous connectivity patterns using low-dimensional embeddings. As a result, numerous hyperbolic graph representation learning methods have been proposed in recent years. However, their practical adoption and systematic comparison remain challenging, as implementations are fragmented and shared tools for reproducible and fair evaluation are lacking. In this work, we introduce a unified open-source framework for hyperbolic graph representation learning that integrates several widely used embedding methods under a common optimization interface. The novel framework enables consistent training, visualization, and evaluation of hyperbolic embeddings, and interfaces seamlessly with standard network analysis tools. Leveraging this unified setup, we conduct an experimental study of hyperbolic embedding methods on real-world networks, focusing on two canonical downstream tasks: link prediction and node classification. Beyond predictive accuracy, the study offers practical insights into the strengths and limitations of existing approaches, thereby facilitating informed method selection and fostering reproducible research in hyperbolic graph representation learning.

Sofía Pérez Casulo, Marcelo Fiori, Bernardo Marenco +1

Facultad de Ingenier´ıa, Universidad de la Rep´ublica, Uruguay · Centro Interdisciplinario en Ciencia de Datos y Aprendizaje Autom´atico (CICADA), Universidad de la Rep´ublica, Uruguay

Graph Representation LearningGraph EmbeddingHyperbolic Representation Learning
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Apr 30, 2026

Learning-Based Hierarchical Scene Graph Matching for Robot Localization Leveraging Prior Maps

Accurate localization is a fundamental requirement for autonomous robots operating in indoor environments. Scene graphs encode the spatial structure of an environment as a hierarchy of semantic entities and their relationships, and can be constructed both online from robot sensor data and offline from architectural priors such as Building Information Models (BIM). Matching these two complementary representations enables drift correction in SLAM by grounding robot observations against a known structural prior. However, establishing reliable node-to-node correspondences between them remains an open challenge: existing combinatorial methods are prohibitively expensive at scale, and prior learned approaches address only flat graph matching, ignoring the multi-level semantic structure present in both representations. Here we present a learned, end-to-end differentiable pipeline that augments both graphs with semantically motivated edge types encoding intra- and inter- level relationships, explicitly exploiting this hierarchy to enable simultaneous matching from high-level room concepts down to low-level wall surfaces. Trained exclusively on floor plans, the proposed method outperforms the combinatorial baseline in F1 on real LiDAR environments while running an order of magnitude faster, demonstrating viable zero-shot generalization for BIM-assisted robot localization.

Nimrod Millenium Ndulue, Jose Andres Millan-Romera, Matteo Giorgi +2

Authors are with the Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg. Holger Voos is also associated with the Faculty of Science, Technology and Medicine, University of Luxembourg, Luxembourg. · Author is with the University of Pisa.

Simultaneous Localization and MappingGraph Representation LearningGraph MatchingMap-Based Localization
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Apr 30, 2026

Robust Learning on Heterogeneous Graphs with Heterophily: A Graph Structure Learning Approach

Heterogeneous graphs with heterophily have emerged as a powerful abstraction for modeling complex real-world systems, where nodes of different types and labels interact in diverse and often non-homophilous ways. Despite recent advances, robust representation learning for such graphs remains largely unexplored, particularly in the presence of noisy or misleading connectivity. In this work, we investigate this problem and identify structural noise as a critical challenge that significantly degrades model performance. To address this issue, we propose a unified framework, Heterogeneous Graph Unified Learning (HGUL), which jointly handles heterophily and noisy graph structures. The framework consists of three complementary modules: a kNN-based graph construction module that recovers reliable local neighborhoods, a graph structure learning module that adaptively refines the adjacency by filtering noisy edges, and a heterogeneous affinity learning module that captures class-level relationships via an extended affinity matrix derived from a polynomial graph kernel. Extensive experiments on multiple datasets demonstrate that HGUL consistently outperforms existing methods on clean graphs and maintains strong robustness under varying levels of structural noise. The results further underscore the importance of jointly modeling heterophily and noise in heterogeneous graph learning.

Yihan Zhang, Ercan E. Kuruoglu

Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

Graph Structure LearningGraph Representation LearningHeterogeneous GNNsGraph Heterophily
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Apr 30, 2026

TypeBandit: Type-Level Context Allocation and Reweighting for Effective Attribute Completion in Heterogeneous Graph Neural Networks

Heterogeneous graphs are widely used to model multi-relational systems, but missing node attributes remain a major bottleneck for downstream learning. In this paper, we identify and formalize type-dependent information asymmetry: the phenomenon that different node types provide substantially different levels of useful signal for attribute completion. Motivated by this observation, we propose TypeBandit, a lightweight, model-agnostic methodology for heterogeneous attribute completion. TypeBandit combines topology-aware initialization, type-level bandit sampling, and joint representation learning. It allocates a finite global sampling budget across node types, samples representative nodes within each type, and uses the resulting sampled type summaries as shared contextual signals during representation construction. By operating at the type level rather than over each target node's local neighborhood, TypeBandit keeps the adaptive state compact and practical for large heterogeneous graphs. A key advantage of TypeBandit is architectural flexibility. Rather than requiring a new heterogeneous graph neural network architecture, TypeBandit acts as a type-aware front end for representative heterogeneous GNN backbones, including R-GCN, HetGNN, HGT, and SimpleHGN. We further introduce a hybrid pretraining scheme that combines structural degree priors with feature propagation, yielding a more reliable initializer than degree-only pretraining. Under a fixed-split protocol on DBLP, IMDB, and ACM, TypeBandit provides dataset-dependent but practically meaningful gains. Additional ablation, stability, efficiency, semantic-propagation, and sampled OGBN-MAG experiments support TypeBandit as a practical strategy for heterogeneous attribute completion when type-specific information is unevenly distributed and sampling resources are limited.

Ta-Yang Wang, Rajgopal Kannan, Viktor Prasanna

University of Southern California, Los Angeles, USA · DEVCOM Army Research Office, Los Angeles, USA

Graph Representation LearningHeterogeneous GNNs
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Apr 29, 2026

Cheeger--Hodge Contrastive Learning for Structurally Robust Graph Representation Learning

Graph Contrastive Learning (GCL) has emerged as a prominent framework for unsupervised graph representation learning. However, relying on augmentation design alone to define the invariances learned by GCL can be brittle under structural perturbations. To address this issue, we propose Cheeger--Hodge Contrastive Learning (CHCL), a framework that aligns a perturbation-stable Cheeger--Hodge joint signature across augmented views for robust graph representation learning. The proposed signature combines a Cheeger-inspired connectivity signature derived from the algebraic connectivity λ2λ_2 with the low-frequency spectrum of the 1-Hodge Laplacian, thereby capturing both global connectivity and higher-order structural information. By aligning encoder representations with the proposed Cheeger--Hodge joint signature across augmented views, CHCL learns graph embeddings that are robust to local structural perturbations. Extensive experiments on standard benchmarks, transfer settings demonstrate that CHCL consistently improves performance, robustness, and generalization.

Mengyang Zhao, Longlong Li, Cunquan Qu

School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore · Data Science Institute, Shandong University, Jinan 250100, China

Graph Representation LearningGraph Contrastive LearningSpectral Methods
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Apr 28, 2026

Mini-Batch Class Composition Bias in Link Prediction

Prior work on node classification has shown that Graph Neural Networks (GNNs) can learn representations that transfer across graphs, when underlying graph properties are shared. For a fixed graph, one would then expect GNNs trained for link prediction to learn a representation consistent with that learnt for node classification. We show this intuition does not hold in the general case. Instead, we find popular link prediction models can learn a trivial mini-batch dependent heuristic, enabled by batch-normalisation layers, to solve the edge classification task. When correcting for this, we observe increased alignment of the network representation with node-class relevant features, suggesting the network has learnt a graph representation that better aligns with the underlying graph's properties. Our findings suggest that standard link prediction training may be leading us to overestimate link predictors' ability to learn a generalised representation of a graph that is consistent across tasks.

Kieran Maguire, Srinandan Dasmahapatra

University of Southampton School of Electronics and Computer Science

Graph Representation LearningLink PredictionShortcut Learning
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Apr 27, 2026

Latent-Hysteresis Graph ODEs: Modeling Coupled Topology-Feature Evolution via Continuous Phase Transitions

Graph neural ordinary differential equations (Graph ODEs) extend graph learning from discrete message-passing layers to continuous-time representation flows. While it supports adaptive long-range propagation, we show that Graph ODEs with strictly positive irreducible mixing operators face an inherent monostability trap: in the long-time regime, information leakage is unavoidable and the dynamics converge to a single global consensus attractor. We propose the Hysteresis Graph ODE (HGODE), which couples feature evolution with a latent topological potential driven by a learned pairwise force. A double-well edge potential and bipolarized gate allow edge states to polarize into connected or insulated phases while preserving differentiability. We provide asymptotic analysis of the collapse mechanism and the proposed hysteretic topology dynamics, and validate HGODE on theory-driven synthetic diagnostics and real-world graph benchmarks.

Qinhan Hou, Jing Tang

Doctoral Program of Computer Science, University of Helsinki, Helsinki, Finland · Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland

Dynamical SystemsGraph Structure LearningGraph Neural NetworksGraph Representation LearningPhase TransitionsGNN Oversmoothing+2
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Apr 27, 2026

Time-varying Interaction Graph ODE for Dynamic Graph Representation Learning

Graph neural Ordinary Differential Equations (ODE) combine neural ODE with the message passing mechanism of Graph Neural Networks (GNN), providing a continuous-time modeling method for graph representation learning. However, in dynamic graph scenarios, existing graph neural ODEs typically employ a unified message passing mechanism, assuming that inter-node interactions share the same message passing function at any time, which makes it challenging to capture the diversity and time-varying nature of inter-node interaction patterns. To address this, we propose Time-varying Interaction Graph Ordinary Differential Equations (TI-ODE). The core idea of TI-ODE is to decompose the evolution function of a graph ODE into a set of learnable interaction basis functions, where each basis function corresponds to a distinct type of inter-node interaction. These basis functions are dynamically combined through time-dependent learnable weights, enabling inter-node interaction patterns to adaptively evolve over time. Experimental results on six dynamic graph datasets demonstrate that TI-ODE consistently outperforms existing methods and achieves state-of-the-art performance on attribute prediction tasks, and experiments on the Covid dataset further verify the interpretability and generalizability of our TI-ODE. Furthermore, we demonstrate both theoretically and empirically that TI-ODE exhibits superior robustness compared to models utilizing a unified message-passing mechanism.

Xiaoyi Wang, Zhiqiang Wang, Jianqing Liang +4

Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China. · Department of System Engineering and Engineering Management, City University of Hong Kong, Kowloon 999077, Hong Kong, China. · Key Laboratory of Complex Systems and Data Science of Ministry of Education, Complex Systems Research Center, Shanxi University, Taiyuan 030006, Shanxi, China.

Temporal GNNsGraph Representation LearningDynamic Graph LearningNeural ODEs
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Apr 26, 2026

Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphs

Efficiently solving the Job Shop Scheduling Problem in real-world industrial applications requires policies that are both computationally lean and topologically robust. While Reinforcement Learning has shown potential in automating dispatching rules, existing models often struggle with a scalability bottleneck caused by quadratic graph complexity or the architectural overhead of heterogeneous layers. We introduce a unified graph framework that employs feature-based homogenization to project distinct node roles into a shared latent space. This allows a standard homogeneous Graph Isomorphism Network to capture complex resource contention with linear complexity, ensuring low-latency inference for large-scale industrial applications. Our empirical results demonstrate that our framework achieves state-of-the-art performance while exhibiting consistent zero-shot generalization. We identify the job-to-machine ratio as the primary driver of policy effectiveness, rather than absolute problem size. Based on this, we propose a hypothesis of structural saturation, demonstrating that policies trained on critically congested instances (J≈M\mathcal{J} \approx \mathcal{M}) learn scale-invariant resolution strategies. Agents trained at this saturation point internalize invariant conflict-resolution logic, allowing them to treat massive rectangular instances as a sequential concatenation of saturated sub-problems. This approach eliminates the need for expensive scale-specific retraining and prevents overfitting to statistical shortcuts, providing a robust and efficient pathway for deploying RL solutions in dynamic production environments.

Jonathan Hoss, Moritz Link, Noah Klarmann

Faculty of Management and Engineering, Rosenheim Technical University of Applied Sciences, Germany

Graph Neural NetworksReinforcement LearningGraph Representation LearningJob Shop Scheduling
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Apr 26, 2026

Hamiltonian Graph Inference Networks: Joint structure discovery and dynamics prediction for lattice Hamiltonian systems from trajectory data

Lattice Hamiltonian systems underpin models across condensed matter, nonlinear optics, and biophysics, yet learning their dynamics from data is obstructed by two unknowns: the interaction topology and whether node dynamics are homogeneous. Existing graph-based approaches either assume the graph is given or, as in αα-separable graph Hamiltonian network, infer it only for separable Hamiltonians with homogeneous node dynamics. We introduce the Hamiltonian Graph Inference Network (HGIN), which jointly recovers the interaction graph and predicts long-time trajectories from state data alone, for both separable and non-separable Hamiltonians and under heterogeneous node dynamics. HGIN couples a structure-learning module -- a learnable weighted adjacency matrix trained under a Hamilton's-equations loss -- with a trajectory-prediction module that partitions edges into physically distinct subgraphs via kk-means clustering, assigning each subgraph its own encoder and thereby breaking the parameter-sharing bottleneck of conventional GNNs. On three benchmarks -- a Klein--Gordon lattice with long-range interactions and two discrete nonlinear Schrödinger lattices (homogeneous and heterogeneous) -- HGIN reduces long-time energy prediction error and trajectory prediction error by six to thirteen orders of magnitude relative to baselines. A symmetry argument on the Hamiltonian loss further shows that the learned weights encode the parity of the underlying pair potential, yielding an interpretable readout of the system's interaction structure.

Ru Geng, Panayotis Kevrekidis, Yixian Gao +2

School of Mathematics and Statistics, Changchun University, Changchun, 130022, China · Department of Mathematics and Statistics, University of Massachusetts, Amherst, 01003, MA, USA · Department of Physics, University of Massachusetts, Amherst, 01003, MA, USA +2

Dynamical SystemsGraph Structure LearningGraph Neural NetworksGraph Representation LearningSystem IdentificationPhysics-Informed ML+2
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Apr 26, 2026

Path-Based Gradient Boosting for Graph-Level Prediction

We propose PathBoost, a gradient tree boosting method for graph-level classification and regression that learns discriminative path-based features directly from the input graph structure. Building on a previous work, which was tailored to a specific chemistry application, PathBoost introduces three key extensions: (i) adaptation to binary classification through gradient boosting with a logistic loss, (ii) incorporation of multiple node and edge attributes into the path feature space via a prefix-based decomposition, and (iii) automatic anchor node selection based on categorical attribute diversity, eliminating the need for the user to specify the starting point of the considered path features. We compared PathBoost to graph neural networks and graph kernel approaches on several benchmark datasets, obtaining better results in half of them, and comparable results in the rest. PathBoost shows better performances on graphs with larger average node counts. Overall, the results demonstrate that path-based boosting methods can be competitive with more complex black-box approaches.

Claudio Meggio, Johan Pensar, Riccardo De Bin

Department of Mathematics, University of Oslo, Oslo, Norway

Gradient-Boosted Decision TreesGraph Representation LearningGraph Classification
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Apr 25, 2026

Layer Embedding Deep Fusion Graph Neural Network

Graph Neural Networks (GNNs) have demonstrated impressive performance in learning representations from graph-structured data. However, their message-passing mechanism inherently relies on the assumption of label consistency among connected nodes, limiting their applicability to low-homophily settings. Moreover, since message passing operates as a hierarchical diffusion process, GNNs face challenges in capturing long-range dependencies. As network depth increases, the structural noise along heterophilic edges tends to be amplified, resulting in over-smoothing. This issue becomes especially prominent in highly heterophilic graphs, where the propagation of inconsistent semantics across the topology continually exacerbates misaggregation. To address this issue, we propose a novel framework named Layer Embedding Deep Fusion Graph Neural Network (LEDF-GNN). Specifically, we design a Layer Embedding Deep Fusion (LEDF) operator that nonlinearly fuses multi-layer embeddings to capture inter-layer dependencies and effectively alleviate deep propagation degradation. Meanwhile, to mitigate structural heterophily, LEDF-GNN employs a Dual-Topology Parallel Strategy (DTPS) that simultaneously leverages the original and reconstructed topologies, allowing for adaptive structure-semantics co-optimization under diverse homophily conditions. Extensive semi-supervised classification experiments on the citation and image benchmarks demonstrate that, under both homophilic and heterophilic settings, LEDF-GNN consistently outperforms state-of-the-art baselines, validating its effectiveness and generalization capability across diverse graph types.

Taihua Xu, Genhao Tian, Jicong Fan +3

Jiangsu University of Science and Technology · The Chinese University of Hong Kong, Shenzhen · Chongqing University of Post and Telecommunications

Graph Structure LearningGraph Neural NetworksGraph Representation LearningMulti-Scale Feature FusionGraph Heterophily
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Apr 23, 2026

Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning

We propose Mochi, a Graph Foundation Model that addresses task unification and training efficiency by adopting a meta-learning based training framework. Prior models pre-train with reconstruction-based objectives such as link prediction, and assume that the resulting representations can be aligned with downstream tasks through a separate unification step such as class prototypes. We demonstrate through synthetic and real-world experiments that this procedure, while simple and intuitive, has limitations that directly affect downstream task performance. To address these limitations, Mochi pre-trains on few-shot episodes that mirror the downstream evaluation protocol, aligning the training objective with inference rather than relying on a post-hoc unification step. We show that Mochi, along with its more powerful variant Mochi++, achieves competitive or superior performance compared to existing Graph Foundation Models across 25 real-world graph datasets spanning node classification, link prediction, and graph classification, while requiring 8∼\sim27 times less training time than the strongest baseline.

João Mattos, Arlei Silva

Computer Science Department · Ken Kennedy Institute

Graph Representation LearningMeta-LearningGraph Foundation Models
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Apr 23, 2026

GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion

Large Language Models (LLMs) have shown immense potential in Knowledge Graph Completion (KGC), yet bridging the modality gap between continuous graph embeddings and discrete LLM tokens remains a critical challenge. While recent quantization-based approaches attempt to align these modalities, they typically treat quantization as flat numerical compression, resulting in semantically entangled codes that fail to mirror the hierarchical nature of human reasoning. In this paper, we propose GS-Quant, a novel framework that generates semantically coherent and structurally stratified discrete codes for KG entities. Unlike prior methods, GS-Quant is grounded in the insight that entity representations should follow a linguistic coarse-to-fine logic. We introduce a Granular Semantic Enhancement module that injects hierarchical knowledge into the codebook, ensuring that earlier codes capture global semantic categories while later codes refine specific attributes. Furthermore, a Generative Structural Reconstruction module imposes causal dependencies on the code sequence, transforming independent discrete units into structured semantic descriptors. By expanding the LLM vocabulary with these learned codes, we enable the model to reason over graph structures isomorphically to natural language generation. Experimental results demonstrate that GS-Quant significantly outperforms existing text-based and embedding-based baselines. Our code is publicly available at https://github.com/mikumifa/GS-Quant.

Qizhuo Xie, Yunhui Liu, Yu Xing +4

State Key Laboratory for Novel Software Technology, Nanjing University, China

Graph Representation LearningKG Completion
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Apr 23, 2026

Drug Synergy Prediction via Residual Graph Isomorphism Networks and Attention Mechanisms

In the treatment of complex diseases, treatment regimens using a single drug often yield limited efficacy and can lead to drug resistance. In contrast, combination drug therapies can significantly improve therapeutic outcomes through synergistic effects. However, experimentally validating all possible drug combinations is prohibitively expensive, underscoring the critical need for efficient computational prediction methods. Although existing approaches based on deep learning and graph neural networks (GNNs) have made considerable progress, challenges remain in reducing structural bias, improving generalization capability, and enhancing model interpretability. To address these limitations, this paper proposes a collaborative prediction graph neural network that integrates molecular structural features and cell-line genomic profiles with drug-drug interactions to enhance the prediction of synergistic effects. We introduce a novel model named the Residual Graph Isomorphism Network integrated with an Attention mechanism (ResGIN-Att). The model first extracts multi scale topological features of drug molecules using a residual graph isomorphism network, where residual connections help mitigate over-smoothing in deep layers. Subsequently, an adaptive Long Short-Term Memory (LSTM) module fuses structural information from local to global scales. Finally, a cross-attention module is designed to explicitly model drug-drug interactions and identify key chemical substructures. Extensive experiments on five public benchmark datasets demonstrate that ResGIN-Att achieves competitive performance, comparing favorably against key baseline methods while exhibiting promising generalization capability and robustness.

Jiyan Song, Wenyang Wang, Chengcheng Yan +2

College of Sciences, Shihezi University, 221 North 4th Road, Shihezi, 832003, Xinjiang, China · School of Mathematics and Computational Science, Xiangtan University, North 2nd Ring Road, Xiangtan, 411105, Hunan, China

Graph Neural NetworksGraph Representation LearningMolecular Representation Learning
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Apr 21, 2026

When Graph Structure Becomes a Liability: A Critical Re-Evaluation of Graph Neural Networks for Bitcoin Fraud Detection under Temporal Distribution Shift

The consensus that GCN, GraphSAGE, GAT, and EvolveGCN outperform feature-only baselines on the Elliptic Bitcoin Dataset is widely cited but has not been rigorously stress-tested under a leakage-free evaluation protocol. We perform a seed-matched inductive-versus-transductive comparison and find that this consensus does not hold. Under a strictly inductive protocol, Random Forest on raw features achieves F1 = 0.821 and outperforms all evaluated GNNs, while GraphSAGE reaches F1 = 0.689 +/- 0.017. A paired controlled experiment reveals a 39.5-point F1 gap attributable to training-time exposure to test-period adjacency. Additionally, edge-shuffle ablations show that randomly wired graphs outperform the real transaction graph, indicating that the dataset's topology can be misleading under temporal distribution shift. Hybrid models combining GNN embeddings with raw features provide only marginal gains and remain substantially below feature-only baselines. We release code, checkpoints, and a strict-inductive protocol to enable reproducible, leakage-free evaluation.

Saket Maganti

Graph Neural NetworksDistribution ShiftGraph Representation LearningFinancial Fraud DetectionTemporal Distribution Shift
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Apr 21, 2026

Revisiting Catastrophic Forgetting in Continual Knowledge Graph Embedding

Knowledge Graph Embeddings (KGEs) support a wide range of downstream tasks over Knowledge Graphs (KGs). In practice, KGs evolve as new entities and facts are added, motivating Continual Knowledge Graph Embedding (CKGE) methods that update embeddings over time. Current CKGE approaches address catastrophic forgetting (i.e., the performance degradation on previously learned tasks) primarily by limiting changes to existing embeddings. However, we show that this view is incomplete. When new entities are introduced, their embeddings can interfere with previously learned ones, causing the model to predict them in place of previously correct answers. This phenomenon, which we call entity interference, has been largely overlooked and is not accounted for in current CKGE evaluation protocols. As a result, the assessment of catastrophic forgetting becomes misleading, and CKGE methods performance is systematically overestimated. To address this issue, we introduce a corrected CKGE evaluation protocol that accounts for entity interference. Through experiments on multiple benchmarks, we show that ignoring this effect can lead to performance overestimation of up to 25%, particularly in scenarios with significant entity growth. We further analyze how different CKGE methods and KGE models are affected by the different sources of forgetting, and introduce a catastrophic forgetting metric tailored to CKGE.

Gerard Pons, Carlos Escolano, Besim Bilalli +1

Data-intensive Technologies and Knowledge Systems (DTAK), Department of Service and Information System Engineering, Universitat Politècnica de Catalunya, Barcelona, 08034, Catalonia, Spain · Intelligent Data Science and Artificial Intelligence Research Center (IDEAI), Department of Computer Science, Universitat Politècnica de Catalunya, Barcelona, 08034, Catalonia, Spain

Graph Representation LearningKG EmbeddingGraph EmbeddingKG CompletionCatastrophic Forgetting
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Apr 21, 2026

TACENR: Task-Agnostic Contrastive Explanations for Node Representations

Graph representation learning has achieved notable success in encoding graph-structured data into latent vector spaces, enabling a wide range of downstream tasks. However, these node representations remain opaque and difficult to interpret. Existing explainability methods primarily focus on supervised settings or on explaining individual representation dimensions, leaving a critical gap in explaining the overall structure of node representations. In this paper, we propose TACENR (Task-Agnostic Contrastive Explanations for Node Representations), a local explanation method that identifies not only attribute features but also proximity and structural ones that contribute the most in the representation space. TACENR builds on contrastive learning, through which we learn a similarity function in the representation space, revealing which are the features that play an important role in the representation of a node. While our focus is on task-agnostic explanations, TACENR can be applied to supervised scenarios as well. Experimental results demonstrate that proximity and structural features play a significant role in shaping node representations and that our supervised variant performs comparably to existing task-specific approaches in identifying the most impactful features.

Vasiliki Papanikou, Evaggelia Pitoura

University of Ioannina, Greece · Archimedes/Athena Research Center, Greece

Graph Representation LearningGNN Explainability
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Apr 20, 2026

LoReC: Rethinking Large Language Models for Graph Data Analysis

The advent of Large Language Models (LLMs) has fundamentally reshaped the way we interact with graphs, giving rise to a new paradigm called GraphLLM. As revealed in recent studies, graph learning can benefit from LLMs. However, we observe limited benefits when we directly utilize LLMs to make predictions for graph-related tasks within GraphLLM paradigm, which even yields suboptimal results compared to conventional GNN-based approaches. Through in-depth analysis, we find this failure can be attributed to LLMs' limited capability for processing graph data and their tendency to overlook graph information. To address this issue, we propose LoReC (Look, Remember, and Contrast), a novel plug-and-play method for GraphLLM paradigm, which enhances LLM's understanding of graph data through three stages: (1) Look: redistributing attention to graph; (2) Remember: re-injecting graph information into the Feed-Forward Network (FFN); (3) Contrast: rectifying the vanilla logits produced in the decoding process. Extensive experiments demonstrate that LoReC brings notable improvements over current GraphLLM methods and outperforms GNN-based approaches across diverse datasets. The implementation is available at https://github.com/Git-King-Zhan/LoReC.

Hongyu Zhan, Qixin Wang, Yusen Tan +6

The Hong Kong University of Science and Technology (Guangzhou) · Tongji University · Westlake University +2

Graph Representation LearningLLMs for Graph Learning
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Apr 19, 2026

Contraction and Hourglass Persistence for Learning on Graphs, Simplices, and Cells

Persistent homology (PH) encodes global information, such as cycles, and is thus increasingly integrated into graph neural networks (GNNs). PH methods in GNNs typically traverse an increasing sequence of subgraphs. In this work, we first expose limitations of this inclusion procedure. To remedy these shortcomings, we analyze contractions as a principled topological operation, in particular, for graph representation learning. We study the persistence of contraction sequences, which we call Contraction Homology (CH). We establish that forward PH and CH differ in expressivity. We then introduce Hourglass Persistence, a class of topological descriptors that interleave a sequence of inclusions and contractions to boost expressivity, learnability, and stability. We also study related families parametrized by two paradigms. We also discuss how our framework extends to simplicial and cellular networks. We further design efficient algorithms that are pluggable into end-to-end differentiable GNN pipelines, enabling consistent empirical improvements over many PH methods across standard real-world graph datasets. Code is available at this https URL.

Mattie Ji, Indradyumna Roy, Vikas Garg

University of Pennsylvania · Aalto University · Indian Institute of Technology Bombay +1

Graph Neural NetworksGraph Representation LearningPersistent HomologyTopological Representation LearningTopological Data Analysis
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Apr 19, 2026

HSG: Hyperbolic Scene Graph

Scene graph representations enable structured visual understanding by modeling objects and their relationships, and have been widely used for multiview and 3D scene reasoning. Existing methods such as MSG learn scene graph embeddings in Euclidean space using contrastive learning and attention based association. However, Euclidean geometry does not explicitly capture hierarchical entailment relationships between places and objects, limiting the structural consistency of learned representations. To address this, we propose Hyperbolic Scene Graph (HSG), which learns scene graph embeddings in hyperbolic space where hierarchical relationships are naturally encoded through geometric distance. Our results show that HSG improves hierarchical structure quality while maintaining strong retrieval performance. The largest gains are observed in graph level metrics: HSG achieves a PP IoU of 33.17 and the highest Graph IoU of 33.51, outperforming the best AoMSG variant (25.37) by 8.14, highlighting the effectiveness of hyperbolic representation learning for scene graph modeling. Code: https://github.com/AIGeeksGroup/HSG.

Liyang Wang, Zeyu Zhang, Hao Tang

School of Computer Science, Peking University

Graph Representation LearningHierarchical Representation LearningGraph-Based Retrieval3D Scene RepresentationHyperbolic Representation Learning
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Apr 17, 2026

Graph self-supervised learning based on frequency corruption

Graph self-supervised learning can reduce the need for labeled graph data and has been widely used in recommendation, social networks, and other web applications. However, existing methods often underuse high-frequency signals and may overfit to specific local patterns, which limits representation quality and generalization. We propose Frequency-Corrupt Based Graph Self-Supervised Learning (FC-GSSL), a method that builds corrupted graphs biased toward high-frequency information by corrupting nodes and edges according to their low-frequency contributions. These corrupted graphs are used as inputs to an autoencoder, while low-frequency and general features are reconstructed as supervision targets, forcing the model to fuse information from multiple frequency bands. We further design multiple sampling strategies and generate diverse corrupted graphs from the intersections and unions of the sampling results. By aligning node representations from these views, the model can discover useful frequency combinations, reduce reliance on specific high-frequency components, and improve robustness. Experiments on 14 datasets across node classification, graph prediction, and transfer learning show that FC-GSSL consistently improves performance and generalization.

Haojie Li, Mengjiao Zhang, Guanfeng Liu +3

School of Data Science, Qingdao University of Science and Technology Qingdao, China · School of Information Science and Technology, Qingdao University of Science and Technology Qingdao, China · School of Computing, Macquarie University Sydney, Australia

Graph Neural NetworksGraph Representation LearningGraph SSLFrequency-Domain Feature LearningGraph Contrastive Learning
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Apr 14, 2026

Graph-Based Fraud Detection with Dual-Path Graph Filtering

Fraud detection on graph data can be viewed as a demanding task that requires distinguishing between different types of nodes. Because graph neural networks (GNNs) are naturally suited for processing information encoded in graph form through their message-passing operations, methods based on GNN models have increasingly attracted attention in the fraud detection domain. However, fraud graphs inherently exhibit relation camouflage, high heterophily, and class imbalance, causing most GNNs to underperform in fraud detection tasks. To address these challenges, this paper proposes a Graph-Based Fraud Detection Model with Dual-Path Graph Filtering (DPF-GFD). DPF-GFD first applies a beta wavelet-based operator to the original graph to capture key structural patterns. It then constructs a similarity graph from distance-based node representations and applies an improved low-pass filter. The embeddings from the original and similarity graphs are fused through supervised representation learning to obtain node features, which are finally used by an ensemble tree model to assess the fraud risk of unlabeled nodes. Unlike existing single-graph smoothing approaches, DPF-GFD introduces a frequency-complementary dual-path filtering paradigm tailored for fraud detection, explicitly decoupling structural anomaly modeling and feature similarity modeling. This design enables more discriminative and stable node representations in highly heterophilous and imbalanced fraud graphs. Comprehensive experiments on four real-world financial fraud detection datasets demonstrate the effectiveness of our proposed method.

Wei He, Wensheng Gan, Philip S. Yu

School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China · Department of Computer Science, University of Illinois Chicago, Chicago 60607, USA

Financial Fraud DetectionGraph Representation Learning
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Apr 12, 2026

DBGL: Decay-aware Bipartite Graph Learning for Irregular Medical Time Series Classification

Irregular Medical Time Series play a critical role in the clinical domain to better understand the patient's condition. However, inherent irregularity arising from heterogeneous sampling rates, asynchronous observations, and variable gaps poses key challenges for reliable modeling. Existing methods often distort temporal sampling irregularity and missingness patterns while failing to capture variable decay irregularity, resulting in suboptimal representations. To address these limitations, we introduce DBGL, Decay-Aware Bipartite Graph Learning for Irregular Medical Time Series. DBGL first introduces a patient-variable bipartite graph that simultaneously captures irregular sampling patterns without artificial alignment and adaptively models variable relationships for temporal sampling irregularity modeling, enhancing representation learning. To model variable decay irregularity, DBGL designs a novel node-specific temporal decay encoding mechanism that captures each variable's decay rates based on sampling interval, yielding a more accurate and faithful representation of irregular temporal dynamics. We evaluate the performance of DBGL on four publicly available datasets, and the results show that DBGL outperforms all baselines.

Jian Chen, Yuzhu Hu, Xiaoyan Yuan +6

Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China · Shenzhen MSU-BIT University, China · Department of Data Science, City University of Hong Kong, China +1

Irregular Time-Series ModelingMultivariate Time Series ClassificationTime Series Representation LearningGraph Representation Learning
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Apr 10, 2026

Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Models

Large-scale digital platforms generate billions of timestamped user-item interactions (events) that are crucial for predicting user attributes in, e.g., fraud prevention and recommendations. While self-supervised learning (SSL) effectively models the temporal order of events, it typically overlooks the global structure of the user-item interaction graph. To bridge this gap, we propose three model-agnostic strategies for integrating this structural information into contrastive SSL: enriching event embeddings, aligning client representations with graph embeddings, and adding a structural pretext task. Experiments on four financial and e-commerce datasets demonstrate that our approach consistently improves the accuracy (up to a 2.3% AUC) and reveals that graph density is a key factor in selecting the optimal integration strategy.

Harry Proshian, Nikita Severin, Sergey Nikolenko +5

Steklov Institute of Mathematics Saint Petersburg, Russia · Independent Researcher Belgrade, Serbia · ISP RAS Steklov Institute of Mathematics Saint Petersburg, Russia +4

Self-Supervised LearningContrastive LearningSequence ModelingGraph Representation Learning
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