Graph Learning

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Period ending 2026-09-14

2 new papers

A weekly snapshot of new work published in Graph Learning.

Period ending 2026-09-07

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A weekly snapshot of new work published in Graph Learning.

208 papers

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GSLAD: Prototype-Regularized Graph Structure Learning for Multivariate Time Series Anomaly Detection
Sep 14, 2026Zepeng Zhang, Fuad Khuri, Keivan Faghih Niresi +1Graph Learning Time Series
Graph Matching Relaxations and Amortization for Supervised Graph Prediction
Sep 14, 2026Federico Méndez, Paul Krzakala, Gabriel Melo +3Graph Learning
Topology-induced Operators Reveal Complementary Graph Representations without Training
Sep 8, 2026Meng Qin, Jinqiang Cui, Hongwei Zheng +2Graph Learning Representation Learning
$α$ -Graph: Attention-Infused Normalizing Flow Approach to Tractable Graph Modelingα-Graph: Attention-Infused Normalizing Flow Approach to Tractable Graph Modeling
Sep 7, 2026Thanh-Dat Truong, Sarah Alharbi, Susan Gauch +3Graph Learning Graph Neural Networks
When Vision Meets Graphs: A Survey on Graph Reasoning and Learning
Sep 3, 2026Xinjian Zhao, Wei Pang, Zhixuan Yu +8Graph Learning
Geometry-Aware Graph Construction via Adaptive Spectral Bandwidth Control
Sep 3, 2026Ecem Bozkurt, Antonio OrtegaGraph Learning Statistical Manifold
From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning
Sep 2, 2026Rémi Bourgerie, Šarūnas Girdzijauskas, Viktoria FodorFederated Graph Learning Graph Learning
MUGEN: Generating Unlearnable Graph Examples for Multiple Learning Tasks
Sep 1, 2026Ziyan Liu, Chengshuai Zhao, Huan LiuGraph Learning Machine Unlearning
ToxLens: A Reproducible Graph-Learning Framework for Leakage-Aware, Uncertainty-Calibrated Molecular Toxicity Prediction
Aug 31, 2026Magnus H. Strømme, Alex G. C. de Sá, David B. AscherToxicity Drug-Drug Interactions
Difference-of-Convex Regularization for Graph Learning by Differentiable Programming
Aug 13, 2026Liping Tao, Chee Wei TanGraph Laplacians Graph Learning
AutoGrable: What Is a Good Graph for a Table?
Aug 11, 2026Tamara Cucumides, Floris GeertsGraph Learning Table
Spectral Embeddings of Degree- $α$ Laplacians in Random Dot Product GraphsSpectral Embeddings of Degree-α Laplacians in Random Dot Product Graphs
Aug 11, 2026John Park, Ning HaoExponential Random Graph Models Graph Laplacians
Can Graph Learning Learn Circuits?
Aug 9, 2026Chester Tan, Moritz Lampert, Courtney Maynard +3Graph Neural Networks Graph Learning
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search
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Edge Sparsification via Temporal Forman-Ricci Curvature for Dynamic Graph Learning
Aug 7, 2026Poupak Azad, Cuneyt Gurcan Akcora, Kiarash ShamsiTemporal Graph Neural Networks Graph Learning
ReGraph: Learning to Generate Recipe Graphs from Food Images
Aug 7, 2026Guoshan Liu, Bin Zhu, Pengkun Jiao +3Recipe Graph Learning
Integrating Implicit and Explicit Relational Biases through Graph-Based Multiple Instance Learning: A Case Study in Skin Lesion Diagnosis
Aug 6, 2026Rafał Buler, Jakub Buler, Maciej Bobowicz +1Skin Lesion Classification Multiple Instance Learning
Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts
Aug 6, 2026Quanxin Wang, Xuanting Xie, Bingheng Li +4Graph Learning Temporal Link Prediction
Tropical Algebraic Geometry for Neuronal Representations: An Arakelov-Green Measure Based Descriptor for Graph Learning
Aug 5, 2026Yuyang Zhang, Weihan Xu, Xuehai Zhou +2Graph Learning Graph Neural Networks
NodeJEPA: Structure-Conditioned Latent Prediction for Node-Level Graph Self-Supervised Learning
Aug 5, 2026Tinghe Zhang, Jian Xu, Jiaheng Chen +3Joint-Embedding Predictive Architectures Graph Learning
EvtGraph: Event-Adaptive Compression for Sparse Temporal Graph Learning in Multimodal Time Series
Aug 5, 2026Ziqian Wang, Tingxiong Xiao, Yuxiao Cheng +1Temporal Graph Neural Networks Time Series
Learning and Clustering on Temporal Graphs: Principles, Primitives, and Pooling
Aug 4, 2026Nelson Aloysio Reis de Almeida Passos, Emanuele Carlini, Salvatore TraniTemporal Graph Neural Networks Graph Learning
ProPRL: Property-Aware Prerequisite Relation Learning in Educational Knowledge Graphs
Aug 4, 2026Xinghe Cheng, Jiapu Wang, Chaobo He +2Preference Learning Knowledge Graphs
Empowering Credit Risk Detection in Weixin Pay with Billion-Scale Deep Graph Learning
Aug 3, 2026Xin Liu, Xiyuan Chen, Chenglong Wu +3Graph Learning Credit Risk Prediction
Multi-Source Dynamic Graph Learning for Compound-Flood Forecasting in Managed Coastal Systems
Aug 3, 2026Liangjun You, Min Wu, Orlando Woods +1Accurate Flood Prediction Retrieval-Augmented Forecasting
Interpretable Unsupervised Community Detection with LLM-Symbolized Structured Processes
Aug 2, 2026Aoting Zeng, Kai Wang, Jianwei Wang +3Unsupervised Graph Learning
Nonlinear Laplacians Improve Signed-Directed Graph Learning
Aug 1, 2026Ali Parviz, Yuichi YoshidaGraph Laplacians Graph Neural Networks
HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning
Aug 1, 2026Ruichen Xu, Jingxiang Qu, Wenhan Gao +5Graph Learning Video Joint Embedding Predictive Architecture
Multi-Granularity Position Embedding of Graphs via Granular-Ball for Link Prediction
Jul 31, 2026Sen Zhao, Cheng Liu, Shuyin Xia +4Temporal Link Prediction Graph Learning
TopoFormer: Topology Meets Attention for Graph Learning
Jul 30, 2026Md Joshem Uddin, Astrit Tola, Cuneyt Gurcan Akcora +1Graph Learning Topology
Dynamic Spectral Filtering for Temporal Graph Learning: Learning Evolving Propagation Operators
Jul 30, 2026Yan KongTemporal Graph Neural Networks Graph Learning
Guarding Organizations Against Malware Risk: A Novel Graph-Based Malware Detection Method
Jul 29, 2026Yinan Gao, Jiarong Xu, Xiaohang Zhao +1Malware Classification Graph Learning
Contrastive Representation Learning of Longitudinal Disease Trajectories on Temporal Graphs
Jul 28, 2026Bastian PfeiferLongitudinal Patient Records Patient Trajectories
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Jul 25, 2026Kawshik Banerjee, Khaled Mohammed SaifuddinGraph Learning Model and Context Compression
Graph Learning on Ensembles of Cyclic Peptides: An Investigation of Molecular Ensemble Modeling
Jul 23, 2026Aaron Feller, Kris Deibler, Maxim SecorConformational Ensembles Graph Neural Networks
CLARK: Closed-loop Learning for Adaptive Reasoning over Knowledge Graphs
Jul 22, 2026Yousef Khan, Luca Gherardini, Marco Maratea +2Knowledge Graphs Graph Learning
OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation
Jul 21, 2026Yuze Dai, Zhihan Zhang, Yan Zhao +6Text-Attributed Graph Graph Learning
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Discrete Ricci Curvature on Protein Contact Graphs for Lightweight Fold Classification
Jul 17, 2026Jianru ShenProtein Molecular Descriptors
Structure-Feature Aligned Graph Learning via Alternating Constrained Optimization
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Distance-Preserving Embeddings in Inhomogeneous Random Graphs
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Jul 9, 2026Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri +2Crystal Structure Prediction Molecular Property Prediction
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Jul 9, 2026Duen Horng Chau, Donghao Ren, Fred Hohman +1Dimensionality Reduction K-Nearest-Neighbor
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Jul 9, 2026Wenxiu Ding, Muzhi Liu, Zheng Yan +3Differential Privacy Graph Learning
Structure Learning on Clustered Data
Jul 9, 2026Ryan Thompson, Matt P. Wand, Veerabhadran BaladandayuthapaniCausal Discovery Clustering
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Eigenbasis-Independent Learnable Spectral Positional Encodings for Directed Graphs via Hermitian Block Krylov Subspaces
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EntroPath: Maximum Entropy Path Ensemble Embedding for Manifold Learning
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Diffusion enabled Optimal Transport distances for graph matching
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Jul 6, 2026Johannes Kiechle, Richard Osuala, Daniel M. Lang +5Patient Trajectories Magnetic Resonance Imaging
Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy
Jul 6, 2026Jingyun Zhang, Hao Peng, Jianxin Li +2Graph Learning Contrastive Learning
Target-Aware Interaction-Guided Reinforcement Learning for Black-Box Node Injection Attacks on Graph Neural Networks
Jul 5, 2026Yi Lan, Ye YuanGraph Neural Networks Novel Latent-Class Attack
Graph Classification via Network Usable Information: From Representation Evaluation to Structure Selection
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A Near-Linear-Time Solver for Graph $p$ -Laplacian Semi-Supervised Learning via Continuation in $p$ A Near-Linear-Time Solver for Graph p-Laplacian Semi-Supervised Learning via Continuation in p
Jul 3, 2026Oren E. LivneGraph Laplacians Semi-Supervised Learning
MABLE: Masked Autoencoding with Bi-Lipschitz Decoding for Embeddings and Graph Metric Learning
Jul 3, 2026Yaniv Shulman, Shaghayegh Akbarpour, Jack B. MuirGraph Learning Hyperspherical Embedding Geometry
Poisson-Gamma Modeling of Inter-Relational Dependencies in Dynamic Knowledge Graphs
Jul 3, 2026Nan Fang, Yijun Wang, Hao Liao +1Graph Learning Knowledge Graphs
SAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport
Jul 1, 2026Yuting Zhang, Yanbei Liu, Zhitao Xiao +3Graph Learning Efficient Continual Learning
Beyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection
Jun 26, 2026Liming Liu, Chao Hu, Mingfei Lu +3Generalist Graph Anomaly Detection Fraud Detection

Sep 14, 2026 · cs.LGJ/K move · Enter open · S save

GSLAD: Prototype-Regularized Graph Structure Learning for Multivariate Time Series Anomaly Detection

Zepeng Zhang, Fuad Khuri, Keivan Faghih Niresi, Olga Fink

Unsupervised multivariate time series anomaly detection methods typically identify anomalies through forecasting, reconstruction, or representation discrepancies. However, industrial faults may first alter inter-variable structural patterns while individual trajectories remain close to normal, resulting in weak anomaly signals. In this paper, we propose GSLAD, a prototype-regularized graph structure learning framework that uses structural deviations for anomaly scoring. GSLAD adopts a two-phase training strategy. First, a condition-aware graph learner and a graph-based forecaster are optimized with predictive supervision. The inferred normal graphs are then clustered into multiple structural prototypes representing different normal operating regimes, with edge-wise variability characterizing structural uncertainty. Deviations from these prototypes regularize the graph learner in the second phase, encouraging stable and regime-specific structural patterns. During inference, uncertainty-normalized structural deviation is combined with predictive deviation for anomaly scoring. Experiments on four industrial benchmarks demonstrate strong overall performance of GSLAD and confirm the effectiveness of structural deviation for anomaly detection and diagnosis.

Graph Learning Time Series

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