Graph Representation Learning

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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

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

Feb 20, 2026

Distilling and Adapting: A Topology-Aware Framework for Zero-Shot Interaction Prediction in Multiplex Biological Networks

Multiplex Biological Networks (MBNs), which represent multiple interaction types between entities, are crucial for understanding complex biological systems. Yet, existing methods often inadequately model multiplexity, struggle to integrate structural and sequence information, and face difficulties in zero-shot prediction for unseen entities with no prior neighbourhood information. To address these limitations, we propose a novel framework for zero-shot interaction prediction in MBNs by leveraging context-aware representation learning and knowledge distillation. Our approach leverages domain-specific foundation models to generate enriched embeddings, introduces a topology-aware graph tokenizer to capture multiplexity and higher-order connectivity, and employs contrastive learning to align embeddings across modalities. A teacher-student distillation strategy further enables robust zero-shot generalization. Experimental results demonstrate that our framework outperforms state-of-the-art methods in interaction prediction for MBNs, providing a powerful tool for exploring various biological interactions and advancing personalized therapeutics.

Alana Deng, Sugitha Janarthanan, Yan Sun +2

Department of Computer Science, Western University, London, ON, Canada · Department of Biochemistry, Western University, London, ON, Canada

Link Prediction Graph Representation Learning Multimodal Graph Learning

Feb 20, 2026

Geometry-Induced Diffusion on Graphs: A Learnable Weighted Laplacian for Spectral GNNs

Long-range graph tasks are challenging for Graph Neural Networks (GNNs): global mechanisms such as attention or rewiring schemes can be computationally expensive, while deep local propagation is prone to vanishing gradients, oversmoothing, and oversquashing. The introduced mu-ChebNet architecture is a simple spectral GNN that learns a node-wise weight function mu before applying ChebNet-style filters. The learned weighting mu induces a modified graph Laplacian which effectively changes the propagation geometry without altering the graph topology. This task-dependent geometry promotes preferred routes for information propagation, thereby helping long-range signals avoid highly contractive bottlenecks, and obviating the need for repeated layer stacking. In practice, we replace the fixed graph Laplacian L by a learned operator L_mu, keeping the proposed mu-ChebNet architecture lightweight while making propagation task-adaptive. Furthermore, we provide a spectral analysis demonstrating how mu modulates propagation dynamics, and empirically observe improved performance on both synthetic long-range reasoning tasks and real-world graph benchmarks. The learned weight function is not only interpretable, but also offers a lightweight alternative to attention and rewiring for adaptive graph propagation.

Mia Zosso, Ali Hariri, Victor Kawasaki-Borruat +2

École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland · École Normale Supérieure – PSL, Paris, France

Long-Range Graph Learning Graph Representation Learning Spectral Graph Convolution Graph Diffusion

Feb 20, 2026

Causal Neighbourhood Learning for Invariant Graph Representations

Graph data often contain noisy and spurious correlations that mask the true causal relationships, which are essential for enabling graph models to make predictions based on the underlying causal structure of the data. Dependence on spurious connections makes it challenging for traditional Graph Neural Networks (GNNs) to generalize effectively across different graphs. Furthermore, traditional aggregation methods tend to amplify these spurious patterns, limiting model robustness under distribution shifts. To address these issues, we propose Causal Neighbourhood Learning with Graph Neural Networks (CNL-GNN), a novel framework that performs causal interventions on graph structure. CNL-GNN effectively identifies and preserves causally relevant connections and reduces spurious influences through the generation of counterfactual neighbourhoods and adaptive edge perturbation guided by learnable importance masking and an attention-based mechanism. In addition, by combining structural-level interventions with the disentanglement of causal features from confounding factors, the model learns invariant node representations that are robust and generalize well across different graph structures. Our approach improves causal graph learning beyond traditional feature-based methods, resulting in a robust classification model. Extensive experiments on four publicly available datasets, including multiple domain variants of one dataset, demonstrate that CNL-GNN outperforms state-of-the-art GNN models.

Simi Job, Xiaohui Tao, Taotao Cai +2

School of Science, Engineering and Digital Technologies, University of Southern Queensland, Australia. · School of Data Science, Lingnan University, Hong Kong.

Graph Representation Learning Causal Representation Learning OOD Generalization Graph Structure Learning Causal Inference

Feb 19, 2026

COMBA: Cross Batch Aggregation for Learning Large Graphs with Context Gating State Space Models

State space models (SSMs) have recently emerged for modeling long-range dependency in sequence data, with much simplified computational costs than modern alternatives, such as transformers. Advancing SMMs to graph structured data, especially for large graphs, is a significant challenge because SSMs are sequence models and the shear graph volumes make it very expensive to convert graphs as sequences for effective learning. In this paper, we propose COMBA to tackle large graph learning using state space models, with two key innovations: graph context gating and cross batch aggregation. Graph context refers to different hops of neighborhood for each node, and graph context gating allows COMBA to use such context to learn best control of neighbor aggregation. For each graph context, COMBA samples nodes as batches, and train a graph neural network (GNN), with information being aggregated cross batches, allowing COMBA to scale to large graphs. Our theoretical study asserts that cross-batch aggregation guarantees lower error than training GNN without aggregation. Experiments on benchmark networks demonstrate significant performance gains compared to baseline approaches. Code and benchmark datasets will be released for public access.

Jiajun Shen, Yufei Jin, Yi He +1

Florida Atlantic University · William & Mary

Graph Representation Learning Graph Neural Networks State Space Models

Feb 19, 2026

Probability-Invariant Random Walk Learning on Gyral Folding-Based Cortical Similarity Networks for Alzheimer's and Lewy Body Dementia Diagnosis

Alzheimer's disease (AD) and Lewy body dementia (LBD) present overlapping clinical features yet require distinct diagnostic strategies. While neuroimaging-based brain network analysis is promising, atlas-based representations may obscure individualized anatomy. Gyral folding-based networks using three-hinge gyri provide a biologically grounded alternative, but inter-individual variability in cortical folding results in inconsistent landmark correspondence and highly irregular network sizes, violating the fixed-topology and node-alignment assumptions of most existing graph learning methods, particularly in clinical datasets where pathological changes further amplify anatomical heterogeneity. We therefore propose a probability-invariant random-walk-based framework that classifies individualized gyral folding networks without explicit node alignment. Cortical similarity networks are built from local morphometric features and represented by distributions of anonymized random walks, with an anatomy-aware encoding that preserves permutation invariance. Experiments on a large clinical cohort of AD and LBD subjects show consistent improvements over existing gyral folding and atlas-based models, demonstrating robustness and potential for dementia diagnosis.

Minheng Chen, Tong Chen, Chao Cao +4

Department of Computer Science and Engineering, University of Texas at Arlington, United States · School of Computing, University of Georgia, United States · Department of Psychiatry, University of Cambridge, United Kingdom +1

Alzheimer's Disease Classification Brain Network Analysis Medical Diagnosis Graph Representation Learning

Feb 18, 2026

Beyond Message Passing: A Symbolic Alternative for Expressive and Interpretable Graph Learning

Graph Neural Networks (GNNs) have become essential in high-stakes domains such as drug discovery, yet their black-box nature remains a significant barrier to trustworthiness. While self-explainable GNNs attempt to bridge this gap, they often rely on standard message-passing backbones that inherit fundamental limitations, including the 1-Weisfeiler-Lehman (1-WL) expressivity barrier and a lack of fine-grained interpretability. To address these challenges, we propose SymGraph, a symbolic framework designed to transcend these constraints. By replacing continuous message passing with discrete structural hashing and topological role-based aggregation, our architecture theoretically surpasses the 1-WL barrier, achieving superior expressiveness without the overhead of differentiable optimization. Extensive empirical evaluations demonstrate that SymGraph achieves state-of-the-art performance, outperforming existing self-explainable GNNs. Notably, SymGraph delivers 10x to 100x speedups in training time using only CPU execution. Furthermore, SymGraph generates rules with superior semantic granularity compared to existing rule-based methods, offering great potential for scientific discovery and explainable AI.

Chuqin Geng, Li Zhang, Haolin Ye +5

School of Computer Science, McGill University, Montreal, Canada · Department of Computer Science, University of Toronto, Toronto, Canada

Graph Representation Learning GNN Explainability GNN Expressivity

Feb 18, 2026

SparTa: Sparse Graphical Task Models from a Handful of Demonstrations

Learning long-horizon manipulation tasks efficiently is a central challenge in robot learning from demonstration. Unlike recent endeavors that focus on directly learning the task in the action domain, we focus on inferring what the robot should achieve in the task, rather than how to do so. To this end, we represent evolving scene states using a series of graphical object relationships. We propose a demonstration segmentation and pooling approach that extracts a series of manipulation graphs and estimates distributions over object states across task phases. In contrast to prior graph-based methods that capture only partial interactions or short temporal windows, our approach captures complete object interactions spanning from the onset of control to the end of the manipulation. To improve robustness when learning from multiple demonstrations, we additionally perform object matching using pre-trained visual features. In extensive experiments, we evaluate our method's demonstration segmentation accuracy and the utility of learning from multiple demonstrations for finding a desired minimal task model. Finally, we deploy the fitted models both in simulation and on a real robot, demonstrating that the resulting task representations support reliable execution across environments.

Adrian Röfer, Nick Heppert, Abhinav Valada

University of Freiburg, Germany · Zuse School ELIZA

Robot Learning Learning from Demonstration Long-Horizon Robotic Manipulation Data-Efficient Robot Learning Graph Representation Learning

Feb 18, 2026

Linked Data Classification using Neurochaos Learning

Neurochaos Learning (NL) has shown promise in recent times over traditional deep learning due to its two key features: ability to learn from small sized training samples, and low compute requirements. In prior work, NL has been implemented and extensively tested on separable and time series data, and demonstrated its superior performance on both classification and regression tasks. In this paper, we investigate the next step in NL, viz., applying NL to linked data, in particular, data that is represented in the form of knowledge graphs. We integrate linked data into NL by implementing node aggregation on knowledge graphs, and then feeding the aggregated node features to the simplest NL architecture: ChaosNet. We demonstrate the results of our implementation on homophilic graph datasets as well as heterophilic graph datasets of verying heterophily. We show better efficacy of our approach on homophilic graphs than on heterophilic graphs. While doing so, we also present our analysis of the results, as well as suggestions for future work.

Pooja Honna, Ayush Patravali, Nithin Nagaraj +1

Complex Systems Programme National Institute of Advanced Studies (NIAS) IISc Campus, Bangalore 560 012, India

Node Classification Graph Representation Learning

Feb 17, 2026

Edge-Local and Qubit-Efficient Quantum Graph Learning for the NISQ Era

Graph neural networks (GNNs) are a powerful framework for learning representations from graph-structured data, but their direct implementation on near-term quantum hardware remains challenging due to circuit depth, multi-qubit interactions, and qubit scalability constraints. In this work, we introduce a hybrid quantum graph learning architecture designed explicitly for unsupervised learning in the noisy intermediate-scale quantum (NISQ) regime. Our approach combines a variational quantum feature extraction layer with an edge-local and qubit-efficient quantum message-passing mechanism inspired by the Quantum Alternating Operator Ansatz (QAOA) framework. The message-passing operation is decomposed into pairwise interactions along graph edges using standard single- and two-qubit gates. For a graph with

N

nodes and

n

-qubit feature registers, this reduces the number of qubits required at one time from

Nn

to at most

2n

. We train the model using the Deep Graph Infomax objective to perform unsupervised node representation learning. The external class labels are not used during graph construction or training and are used only to evaluate the learned embeddings. Experiments on the Cora citation network and the Phase 3 release of the 1000 Genomes Project show that the quantum edge interaction contributes to the quality of the learned node representations.

Armin Ahmadkhaniha, Jake Doliskani

Department of Computing and Software, McMaster University

Graph Representation Learning Quantum Neural Networks Hybrid Quantum-Classical ML Graph SSL

Feb 17, 2026

UrbanVerse: Learning Urban Region Representation Across Cities and Tasks

Recent advances in urban region representation learning have enabled a wide range of applications in urban analytics, yet existing methods remain limited in their capabilities to generalize across cities and analytic tasks. We aim to generalize urban representation learning beyond city- and task-specific settings, towards a foundation-style model for urban analytics. To this end, we propose UrbanVerse, a model for cross-city urban representation learning and cross-task urban analytics. For cross-city generalization, UrbanVerse focuses on features local to the target regions and structural features of the nearby regions rather than the entire city. We model regions as nodes on a graph, which enables a random walk-based procedure to form "sequences of regions" that reflect both local and neighborhood structural features for urban region representation learning. For cross-task generalization, we propose a cross-task learning module named HCondDiffCT. This module integrates region-conditioned prior knowledge and task-conditioned semantics into the diffusion process to jointly model multiple downstream urban prediction tasks. HCondDiffCT is generic. It can also be integrated with existing urban representation learning models to enhance their downstream task effectiveness. Experiments on real-world datasets show that UrbanVerse consistently outperforms state-of-the-art methods across six tasks under cross-city settings, achieving up to 35.89% improvements in prediction accuracy.

Fengze Sun, Egemen Tanin, Shanika Karunasekera +3

School of Computing and Information System, University of Melbourne, Melbourne, Australia · School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

Urban Representation Learning Geospatial Representation Learning Spatial Generalization Multi-Task Learning Graph Representation Learning

Feb 17, 2026

Random Wavelet Features for Graph Kernel Machines

Node embeddings map graph vertices into low-dimensional Euclidean spaces while preserving structural information. They are central to tasks such as node classification, link prediction, and signal reconstruction. A key goal is to design node embeddings whose dot products capture meaningful notions of node similarity induced by the graph. Graph kernels offer a principled way to define such similarities, but their direct computation is often prohibitive for large networks. Inspired by random feature methods for kernel approximation in Euclidean spaces, we introduce randomized spectral node embeddings whose dot products estimate a low-rank approximation of any specific graph kernel. We provide theoretical and empirical results showing that our embeddings achieve more accurate kernel approximations than existing methods, particularly for spectrally localized kernels. These results demonstrate the effectiveness of randomized spectral constructions for scalable and principled graph representation learning.

Valentin de Bassompierre, Jean-Charles Delvenne, Laurent Jacques

INMA/ICTEAM, UCLouvain, Belgium

Graph Representation Learning Graph Embedding Spectral Graph Embedding Kernel Methods Random Feature Methods

Feb 16, 2026

Size-Transferability of Graph Transformers with Convolutional Positional Encodings

Transformers have achieved remarkable success across domains, motivating the rise of Graph Transformers (GTs) as attention-based architectures for graph-structured data. A key design choice in GTs is the use of Graph Neural Network (GNN)-based positional encodings to incorporate structural information. In this work, we study GTs through the lens of manifold limit models for graph sequences and establish a theoretical connection between GTs with GNN positional encodings and Manifold Neural Networks (MNNs). Building on transferability results for GNNs under manifold convergence, we show that GTs inherit transferability guarantees from their positional encodings. In particular, GTs trained on small graphs provably generalize to larger graphs under mild assumptions. We complement our theory with extensive experiments on standard graph benchmarks, demonstrating that GTs exhibit scalable behavior on par with GNNs. To further show the efficiency in a real-world scenario, we implement GTs for shortest path distance estimation over terrains to better illustrate the efficiency of the transferable GTs. Our results provide new insights into the understanding of GTs and suggest practical directions for efficient training of GTs in large-scale settings.

Javier Porras-Valenzuela, Zhiyang Wang, Teresa Shang +2

Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA · Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA

Graph Positional Encoding Graph Transformer Graph Representation Learning

Feb 2, 2026

Plain Transformers are Surprisingly Powerful Link Predictors

Link prediction is a core challenge in graph machine learning, demanding models that capture rich and complex topological dependencies. While Graph Neural Networks (GNNs) are the standard solution, state-of-the-art pipelines often rely on explicit structural heuristics or memory-intensive node embeddings -- approaches that struggle to generalize or scale to massive graphs. Emerging Graph Transformers (GTs) offer a potential alternative but often incur significant overhead due to complex structural encodings, hindering their applications to large-scale link prediction. We challenge these sophisticated paradigms with PENCIL, an encoder-only plain Transformer that replaces hand-crafted priors with attention over sampled local subgraphs, retaining the scalability and hardware efficiency of standard Transformers. Through experimental and theoretical analysis, we show that PENCIL extracts richer structural signals than GNNs, implicitly generalizing a broad class of heuristics and subgraph-based expressivity. Empirically, PENCIL outperforms heuristic-informed GNNs and is far more parameter-efficient than ID-embedding--based alternatives, while remaining competitive across diverse benchmarks -- even without node features. Our results challenge the prevailing reliance on complex engineering techniques, demonstrating that simple design choices are potentially sufficient to achieve the same capabilities. Our code is publicly available at https://github.com/quang-truong/pencil.

Quang Truong, Yu Song, Donald Loveland +4

Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA. · Snap Inc., Bellevue, WA, USA.

Transformer Graph Representation Learning Link Prediction Graph Transformer

Dec 20, 2025

Through the PRISm: Importance-Aware Scene Graphs for Semantic Image Retrieval

Web-based visual search increasingly handles queries driven by semantic intent, focusing on what is happening in an image rather than merely what it looks like. While global embeddings from vision-language models capture overall appearance, they often blur fine-grained relational structure. Conversely, scene graphs make these relationships explicit, but generating them automatically for unannotated web images produces dense, noisy graphs that retain every detected entity and relation regardless of relevance. To address this, we introduce PRISm (Pruning-based Image Retrieval via Importance Prediction on Semantic Graphs), an image-to-image retrieval framework that learns to focus on what matters in a scene. PRISm employs an Importance Prediction Module, supervised by caption-derived relevance strictly during training, to score and prune non-essential objects and <subject, relation, object> triplets. An Edge-Aware Contextual GNN then aggregates relation conditioned neighbor messages, fusing the refined graph with global visual features into a single, index-ready embedding per image. On PSG, PRISm outperforms the strongest baseline by points in MAP@1. Furthermore, on Flickr with machine-generated scene graphs and captions, PRISm remains best on top-1 metrics, while adding only 33,ms per query beyond scene graph generation.

Dimitrios Georgoulopoulos, Nikolaos Chaidos, Angeliki Dimitriou +1

National Technical University of Athens Athens, Greece

Image Retrieval Graph-Based Retrieval Graph Representation Learning Graph Neural Networks

Dec 8, 2025

KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging

Jet identification plays a central role in analyzing data from high-energy collider experiments. While deep learning has improved jet classification, it often lacks interpretability. We introduce the Kinematic Interaction Graph Network (KIGNet), a graph neural network that integrates kinematic variables into jet classification by constructing four graph representations per jet, each weighted by a distinct variable: angular separation (

Δ

), relative transverse momentum (

k_T

), momentum fraction (

z

), and invariant mass squared (

m^2

). Three of these (

Δ

,

k_T

,

z

) are motivated by the Lund jet plane, grounded in perturbative QCD factorization; the fourth (

m^2

) adds complementary mass-scale sensitivity for heavy-flavor identification. Using Gradient-weighted Class Activation Mapping (Grad-CAM), we determine which variables dominate classification. Angular separation and relative transverse momentum account for about 76% of the total Grad-CAM attribution (40.72% and 35.67%), with momentum fraction and invariant mass contributing the remaining 24%. This hierarchy is consistent with the soft-collinear structure of QCD radiation in the training data, showing that the network learns physically interpretable representations rather than spurious correlations. On the JetClass dataset, KIGNet achieves a macro-accuracy of 95.07%, macro-AUC of 96.61%, and macro-AUPR of 81.52%, relative improvements of 2.45%, 3.40%, and 19.11% over the state-of-the-art baseline. On the Aspen Open Jets dataset of real CMS collision data, KIGNet produces substantially more structured latent representations than the baseline, reducing the Davies-Bouldin Index by 52.15% (

0.8395 \rightarrow 0.4017

) and increasing the Dunn Index by 42.33% (

0.0189 \rightarrow 0.0269

), confirming that physics-informed kinematic encoding generalizes beyond idealized simulation to experimental detector conditions.

Md Raqibul Islam, Adrita Khan, Mir Sazzat Hossain +6

Center for Computational & Data Sciences, Independent University, Bangladesh, Dhaka-1229, Bangladesh · Department of Theoretical Physics, University of Dhaka, Dhaka-1000, Bangladesh · Department of Physical Sciences, Independent University, Bangladesh, Dhaka-1229, Bangladesh

Graph Representation Learning Jet Tagging High-Energy Physics Physics-Informed ML GNN Explainability

Nov 14, 2025

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing

Graph neural networks (GNNs) have become an indispensable tool for analyzing relational data. Classical GNNs are broadly classified into three variants: convolutional, attentional, and message-passing. While the standard message-passing variant is expressive, its typical pair-wise messages only consider the features of the center node and each neighboring node individually. This design fails to incorporate contextual information contained within the broader local neighborhood, potentially hindering its ability to learn meaningful relationships within the entire set of neighboring nodes. To address this, the paper first refines the concept of neighborhood-contextualization within GNNs, leveraging ideas from set-based aggregation methods and a key property of the attentional variant. This then serves as the basis for generalizing the message-passing variant to the proposed neighborhood-contextualized message-passing (NCMP) framework. To demonstrate its utility, a simple, mathematically grounded method to parametrize and operationalize NCMP is presented, leading to the development of the proposed Soft-Isomorphic Neighborhood-Contextualized Graph Convolution Network (SINC-GCN). Across a diverse set of synthetic and benchmark datasets, SINC-GCN strikes a highly favorable balance between expressivity and efficiency. Notably, while more complex models incur significant computational overhead, SINC-GCN delivers substantial performance gains with considerable effect sizes over baseline GNN models while maintaining a highly efficient asymptotic runtime complexity, further underscoring the distinctive utility of neighborhood-contextualization. Overall, by integrating multiset neighborhood context, the proposed NCMP framework serves as a practical and scalable path toward enhancing the graph representational power of classical GNNs.

Brian Godwin Lim, Galvin Brice Lim, Renzo Roel Tan +2

Nara Institute of Science and Technology, Kyoto University, and Ateneo de Manila University · UNI-President Information Philippines Corporation · Nara Institute of Science and Technology and Ateneo de Manila University +2

Graph Neural Networks Graph Representation Learning GNN Expressivity Message Passing Neural Networks

Nov 4, 2025

Graph Homomorphism Distortion: A structural (pseudo)-metric for attributed graphs

A large driver of the complexity of graph learning is the interplay between structure and features. When analyzing the expressivity of graph neural networks, however, existing approaches ignore features in favor of structure, making it nigh-impossible to assess to what extent two graphs with close features should be considered similar. We address this by developing a new (pseudo-)metric based on graph homomorphisms Inspired by concepts from metric geometry, our graph homomorphism distortion measures the minimal worst-case distortion that node features of one graph are subjected to when mapping one graph to another. We demonstrate the utility of our (pseudo-)metric by showing that i.) it can be efficiently approximated while retaining distinction capabilities, ii.) it provides a granular approach to graph distinction, and, iii.) it serves as a structural encoding, extending the concept of homomorphism-based descriptors to attributed graphs, thus improving the predictive capabilities of graph neural networks.

Martin Carrasco, Olga Zaghen, Kavir Sumaraj +2

AIDOS Group, University of Fribourg, Fribourg, Switzerland · AMLAB, University of Amsterdam, Amsterdam, Netherland

Graph Representation Learning GNN Expressivity

Oct 12, 2025

Structured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scans

With the growing volume of CT examinations, there is an increasing demand for automated tools such as organ segmentation, abnormality detection, and report generation to support radiologists in managing their clinical workload. Multi-label classification of 3D Chest CT scans remains a critical yet challenging problem due to the complex spatial relationships inherent in volumetric data and the wide variability of abnormalities. Existing methods based on 3D convolutional neural networks struggle to capture long-range dependencies, while Vision Transformers often require extensive pre-training on large-scale, domain-specific datasets to perform competitively. In this work, we propose a 2.5D alternative by introducing a new graph-based framework that represents 3D CT volumes as structured graphs, where axial slice triplets serve as nodes processed through spectral graph convolution, enabling the model to reason over inter-slice dependencies while maintaining complexity compatible with clinical deployment. Our method, trained and evaluated on 3 datasets from independent institutions, achieves strong cross-dataset generalization, and shows competitive performance compared to state-of-the-art visual encoders. We further conduct comprehensive ablation studies to evaluate the impact of various aggregation strategies, edge-weighting schemes, and graph connectivity patterns. Additionally, we demonstrate the broader applicability of our approach through transfer experiments on automated radiology report generation and abdominal CT data.

Theo Di Piazza, Carole Lazarus, Olivier Nempont +1

INSA Lyon, University of Lyon, CNRS, INSERM, CREATIS UMR 5220, U1294, Villeurbanne, France · Hospices Civil de Lyon, Lyon, France · Philips Clinical Informatics, Innovation Paris, France

Graph Neural Networks Graph Representation Learning Medical Image Classification Spectral Graph Convolution Computed Tomography

Oct 9, 2025

Computationally-efficient Graph Modeling with Refined Graph Random Features

We propose refined GRFs (GRFs++), a new class of Graph Random Features (GRFs) for efficient and accurate computations involving kernels defined on the nodes of a graph. GRFs++ resolve some of the long-standing limitations of regular GRFs, including difficulty modeling relationships between more distant nodes. They reduce dependence on sampling long graph random walks via a novel walk-stitching technique, concatenating several shorter walks without breaking unbiasedness. By applying these techniques, GRFs++ inherit the approximation quality provided by longer walks but with greater efficiency, trading sequential, inefficient sampling of a long walk for parallel computation of short walks and matrix-matrix multiplication. Furthermore, GRFs++ extend the simplistic GRFs walk termination mechanism (Bernoulli schemes with fixed halting probabilities) to a broader class of strategies, applying general distributions on the walks' lengths. This improves the approximation accuracy of graph kernels, without incurring extra computational cost. We provide empirical evaluations to showcase all our claims and complement our results with theoretical analysis.

Krzysztof Choromanski, Avinava Dubey, Arijit Sehanobish +1

Google DeepMind, New York, NY, USA · Columbia University, New York, NY, USA · Google Research, Mountain View, CA, USA +2

Graph Representation Learning Randomized Sketching Random Feature Methods

Sep 26, 2025

Rotary Position Encodings for Graphs

We study the extent to which rotary position encodings (RoPE), a recent transformer position encoding algorithm broadly adopted in large language models (LLMs) and vision transformers (ViTs), can be applied to graph-structured data. We find that rotating tokens depending on the spectrum of the graph Laplacian efficiently injects structural information into the attention mechanism, boosting performance in synthetic and real-world graph learning tasks. This approach, coined Wave-Induced Rotary Encodings (WIRE), enjoys intriguing theoretical properties: it recovers regular RoPE on grids, and depends asymptotically on the graph effective resistance. Unlike bias-based relative position encodings, WIRE is compatible with linear attention.

Isaac Reid, Arijit Sehanobish, Cederik Höfs +7

University of Cambridge · Google DeepMind · Independent Researcher +3

Graph Positional Encoding Rotary Positional Embeddings Graph Representation Learning Attention Mechanisms Graph Transformer

Aug 31, 2025

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering

Legal documents pose substantial challenges for unsupervised knowledge discovery due to specialized domain terminology, multi-scale document lengths, and scarce expert annotations. This paper introduces Topic-Graph Spherical Fusion (TGSF), a dual-manifold representation learning framework for unsupervised legal document clustering that unifies dense topic-semantic discovery and relational graph embeddings on a joint unit hypersphere. Instantiating dense topic discovery via Top2Vec and biased second-order random walks via Node2Vec, TGSF constructs a connected multi-relational document-topic graph combining multi-topic centroid bridges with temperature-sharpened semantic k-Nearest Neighbor (k-NN) document edges, guaranteeing a single connected component. The resulting L2-normalized semantic and graph representations are fused via modality-weighted concatenation onto a joint unit hypersphere prior to KMeans partitioning, decomposing squared chordal distance into a convex combination of semantic and structural cosine similarities. Evaluations across three legal benchmarks (ACORD, CUAD, and LEDGAR) show that TGSF outperforms semantic-only, structural-only, Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), and TF-IDF baselines. Across 216 factorial grid configurations and 240 Bayesian TPE and Multi-Objective NSGA-II Pareto optimization trials per dataset (720 trials total), TGSF improves Normalized Mutual Information (NMI) from 0.606 to 0.653 on LEDGAR (ARI = 0.425), 0.228 to 0.284 on ACORD (ARI = 0.260), and 0.535 to 0.570 on CUAD (ARI = 0.282). Functional ANOVA and Pareto frontier diagnostics further characterize hyperparameter sensitivity for reproducible legal text mining.

Deepak Bastola, Woohyeok Choi

Department of Mathematical Sciences, Salisbury University · Department of Mathematics and Statistics, Carleton College

Text Clustering Legal Document Analysis Unsupervised Representation Learning Graph Representation Learning

Aug 20, 2025

Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning

Temporal graph learning is crucial for dynamic networks where nodes and edges evolve over time and new nodes continuously join the system. Inductive representation learning in such settings faces two major challenges: effectively representing unseen nodes and mitigating noisy or redundant graph information. We propose GTGIB, a versatile framework that integrates Graph Structure Learning (GSL) with Temporal Graph Information Bottleneck (TGIB). We design a novel two-step GSL-based structural enhancer to enrich and optimize node neighborhoods and demonstrate its effectiveness and efficiency through theoretical proofs and experiments. The TGIB refines the optimized graph by extending the information bottleneck principle to temporal graphs, regularizing both edges and features based on our derived tractable TGIB objective function via variational approximation, enabling stable and efficient optimization. GTGIB-based models are evaluated to predict links on four real-world datasets; they outperform existing methods in all datasets under the inductive setting, with significant and consistent improvement in the transductive setting.

Jiafeng Xiong, Rizos Sakellariou

Department of Computer Science, University of Manchester, UK

Graph Structure Learning Temporal GNNs Graph Representation Learning Temporal Link Prediction Dynamic Graph Learning

Aug 8, 2025

Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmark

While graph-based Android malware classifiers report strong benchmark accuracy of over 94%, their performance sharply decreases up to 45% when exposed to previously unseen variants of known malware families. In this work, we systematically investigate this critical yet overlooked challenge for real-world deployment by introducing a benchmarking suite designed to simulate two prevalent scenarios: MalNet-Tiny-Common for covariate shift, and MalNet-Tiny-Distinct for domain shift. We further identify an inherent limitation of existing benchmarks where input representation is limited to structure-only function call graphs, discarding the semantic signals needed for robust cross-distribution reasoning. To verify this, we propose a semantic enrichment framework that extends raw graph topology with function-level attributes, combining lightweight metadata with LLM-based code embeddings. Empirical evaluations confirm the effectiveness of our data-centric methodology, with which classification performs better under distribution shift compared to model-based approaches, and consistently further enhances robustness when used in conjunction. We release our precomputed datasets alongside an extensible pipeline implementation, laying the groundwork for more resilient malware detection systems in evolving threat environments.

Ngoc N. Tran, Anwar Said, Waseem Abbas +2

Vanderbilt University, USA · Highmark Health, USA · University of Texas at Dallas, USA

Distribution Shift Robustness Graph Representation Learning OOD Generalization Android Malware Detection Malware Classification Graph Classification+1

Jul 27, 2025

Learning Latent Graph Geometry via Fixed-Point Schrödinger-Type Activation: A Theoretical Study

We study neural architectures in which each hidden layer is defined by the stationary state of a dissipative Schrödinger-type dynamics on a learned latent graph. On stable branches, the local stationary problem defines a differentiable implicit graph layer. To learn the graph itself, we optimize over the stratified moduli space of weighted graphs and equip each stratum with a non-degenerate Kähler-Hessian metric that keeps natural-gradient descent and face crossing well posed. We then show that a multilayer stationary network is equivalent to an exact global stationary problem on a supra-graph, and that it admits a penalized global relaxation whose stationary states converge to the exact one as the penalty parameter tends to infinity. Reverse-mode differentiation is recovered as the adjoint of the exact global system, and the penalized adjoint converges to it in the same limit. Finally, under finite-dimensional strong-monotonicity and admissible-lift assumptions, the corresponding represented hypothesis classes coincide among resolvent feed-forward networks, graph-stationary networks, supra-graph stationary systems, and sheaf-based architectures with unitary connection. The resulting structural identifications yield complexity bounds controlled by sparse graph or supra-graph geometry rather than dense ambient connectivity.

Dmitry Pasechnyuk-Vilensky, Martin Takáč

MBZUAI, UAE

Graph Structure Learning Graph Neural Networks Graph Representation Learning Sheaf Neural Networks Neural Network Approximation Theory

Jul 3, 2025

A Machine Learning Benchmarking Framework for Lipid Nanoparticle Transfection Efficiency Prediction

The discovery of new ionizable lipids for efficient lipid nanoparticle (LNP)-mediated RNA delivery remains a major bottleneck in RNA therapeutics development. Recent advances demonstrate the potential of machine learning (ML) models to predict transfection efficiency directly from lipid structure, enabling high-throughput virtual screening and accelerating lead identification. However, as new models for LNP transfection prediction continue to emerge, the lack of rigorous and standardized benchmarking poses a significant risk and may undermine confidence in their reliability for discovery. Here, we present a robust ML benchmarking framework for evaluating transfection prediction models based on ionizable lipid structures. This framework systematically benchmarks diverse molecular representations paired with a broad range of ML architectures spanning traditional models, feedforward neural networks, and state-of-the-art graph-based methods. In addition, the presented framework supports assessment of model generalization and evaluates prediction reliability beyond standard regression metrics. Using a curated dataset of 1,100 unique ionizable lipid structures derived from the HeLa transfection dataset originally reported by Xu et al., we show that within this framework, models leveraging explicit molecular substructure encoding consistently achieve the highest predictive accuracy and should serve as essential baselines for the development of new, more sophisticated models. In contrast, some current graph-based models, including AGILE, Chemprop, and KPGT, tend to show comparatively lower accuracy. The presented framework provides a standardized, transparent, and comprehensive benchmarking resource that enables meaningful comparison of emerging architectures and establishes strong baselines for future development of predictive models in lipid-based RNA delivery.

Asal Mehradfar, Mohammad Shahab Sepehri, Jose Miguel Hernandez-Lobato +4

Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA · Department of Engineering, University of Cambridge, Cambridge, UK · Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI +1

Graph Representation Learning Molecular Property Prediction Molecular Representation Learning

Jun 12, 2025

Macro Graph of Experts for Billion-Scale Multi-Task Recommendation

Graph-based multi-task learning at billion-scale presents a significant challenge, as different tasks correspond to distinct billion-scale graphs. Traditional multi-task learning methods often neglect these graph structures, relying solely on individual user and item embeddings. However, disregarding graph structures overlooks substantial potential for improving performance. In this paper, we introduce the Macro Graph of Experts (MGOE) framework, the first approach capable of leveraging macro graph embeddings to capture task-specific macro features while modeling the correlations between task-specific experts. Specifically, we propose the concept of a Macro Graph Bottom, which, for the first time, enables multi-task learning models to incorporate graph information effectively. We design the Macro Prediction Tower to dynamically integrate macro knowledge across tasks. MGOE has been deployed at scale, powering multi-task learning for a leading billion-scale recommender system, Alibaba. Extensive offline experiments conducted on three public benchmark datasets demonstrate its superiority over state-of-the-art multi-task learning methods, establishing MGOE as a breakthrough in multi-task graph-based recommendation. Furthermore, online A/B tests confirm the superiority of MGOE in billion-scale recommender systems.

Hongyu Yao, Zijin Hong, Hao Chen +6

The Hong Kong Polytechnic University Hong Kong SAR, China · National University of Defense Technology Changsha, China · Beihang University Beijing, China

Graph Representation Learning Multi-Task Learning Recommender Systems Mixture-of-Experts Models

Jun 2, 2025

Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming

Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph is a signed graph with no cycles containing an odd number of negative edges. Laplacian of a balanced signed graph has eigenvectors that map via a simple linear transform to ones in a corresponding positive graph Laplacian, thus enabling reuse of spectral filtering tools designed for positive graphs. We propose an efficient computation method to learn a balanced signed graph Laplacian directly from data. Specifically, extending a previous linear programming (LP) based sparse inverse covariance estimation method called CLIME, we formulate a new LP problem for each Laplacian column

i

, where the linear constraints restrict weight signs of edges stemming from node

i

, so that nodes of same / different polarities are connected by positive / negative edges. We derive a feasible CLIME parameter

ρ_i

for each sign-constrained column problem. We solve the LP problem efficiently by tailoring a sparse LP method based on ADMM. We theoretically prove that the row / column updates produce a non-increasing objective sequence, and show that the iterations are terminated in a finite number of steps. Extensive experimental results on synthetic and real-world datasets show that our balanced graph learning method outperforms competing methods and enables reuse of spectral filters, wavelets, and graph neural nets (GNN) constructed for positive graphs.

Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka +1

Graph Structure Learning Graph Representation Learning Alternating Direction Method of Multipliers Linear Programming

May 17, 2025

Relation-Aware Graph Foundation Model

In recent years, large language models (LLMs) have demonstrated remarkable capability to generalize across diverse natural language processing tasks, inspiring the development of graph foundation models (GFMs) for large-scale pre-training. However, unlike language models with explicit token units, graphs lack a well-defined unit for generalization, making it challenging to design effective pre-training strategies. In this work, we propose REEF, a novel GFM framework that leverages relation tokens as the fundamental units. We construct a vocabulary of relation tokens to encode relational information within graphs. To accommodate diverse relations, we introduce two hypernetworks that adaptively generate the parameters of aggregators and classifiers in graph neural networks based on relation tokens. In addition, we design another hypernetwork to construct dataset-specific projectors and incorporate a dataset-level feature bias into the initial node representations, enhancing flexibility across different datasets with the same relation. Extensive experiments demonstrate that REEF consistently outperforms existing methods in both pre-training and transfer learning, highlighting its potential as a general-purpose graph foundation model.

Jianxiang Yu, Jiapeng Zhu, Yibo Zhao +4

School of Data Science and Engineering, East China Normal University · Ant Group

Graph Neural Networks Graph Representation Learning Graph Foundation Models

May 3, 2025

Causally Fair Node Classification on Non-IID Graph Data

Fair machine learning seeks to identify and mitigate biases in predictions against unfavorable populations characterized by demographic attributes, such as race and gender. Recent research has extended fairness to graph data, such as social networks, but many studies neglect the causal relationships among data instances. This paper addresses a prevalent challenge in many fair machine learning research, which typically assumes independent and identically distributed (IID) data, from the causal perspective. Specifically, this work targets the circumstance where nodes with different neighborhood structures follow different causal mechanisms, violating the invariance assumptions required for classical structural causal models and do-calculus. We base our research on the Network Structural Causal Model (NSCM) framework and develop a Message Passing Variational Autoencoder for Causal Inference (MPVA) to compute interventional distributions for causally fair node classification. We establish theoretical soundness under two conditions: Decomposability and Graph Independence. These conditions formalize when causal mechanism heterogeneity can be overcome by constructing a structural representation that restores invariance and facilitates the computation of interventional distributions using do-calculus in non-IID settings. Empirical evaluations on semi-synthetic and real-world datasets demonstrate that MPVA outperforms conventional methods by effectively approximating interventional distributions and mitigating bias. Our findings demonstrate the potential of causality-based fairness in complex ML applications and motivate future work on relaxing the classic assumptions in algorithmic fairness.

Yucong Dai, Lu Zhang, Yaowei Hu +2

Clemson University · University of Arkansas · Walmart Inc.

Structural Causal Models Graph Representation Learning Algorithmic Fairness

Nov 29, 2024

MEGA: Message Passing Neural Networks for Multigraphs with EdGe Attributes

Edge-attributed multigraphs, in which multiple edges with distinct attributes connect the same pair of nodes, arise naturally in many real-world systems. In these graphs, effective learning requires preserving information from repeated interactions while distinguishing contributions from different neighbors. Existing neural network solutions for edge-attributed multigraphs remain limited: some lose information from repeated interactions, while others break permutation equivariance. To address this, we introduce neighbor-aware aggregation, an operator that first combines multi-edge features for each neighbor and then aggregates across neighbors. This operator captures per-neighbor statistics that standard single-stage aggregation cannot represent. Building on this operator, we present MEGA-GNN, a model-agnostic message-passing framework for edge-attributed multigraphs. We show that MEGA-GNN is permutation equivariant and has the same asymptotic complexity as standard GNNs with edge updates. We evaluate our approach on datasets from social networks and financial transaction networks. Neighbor-aware aggregation consistently improves GNN performance and matches or surpasses state-of-the-art methods.

H. Çağrı Bilgi, Kubilay Atasu

1Delft University of Technology

Graph Neural Networks Graph Representation Learning Message Passing Neural Networks