Graph Structure Learning

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9 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

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Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning
Aug 20, 2025Jiafeng Xiong, Rizos SakellariouGraph Structure Learning Temporal GNNs
Department of Computer Science, University of Manchester, UK
Learning Latent Graph Geometry via Fixed-Point Schrödinger-Type Activation: A Theoretical Study
Jul 27, 2025Dmitry Pasechnyuk-Vilensky, Martin TakáčGraph Structure Learning Graph Neural Networks
MBZUAI, UAE
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming
Jun 2, 2025Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka +1Graph Structure Learning Graph Representation Learning
Time-Varying Graph Learning with Constraints on Graph Temporal Variation
Jan 10, 2020Haruki Yokota, Koki Yamada, Yuichi Tanaka +1Graph Signal Processing Graph Structure Learning
Graduate School of Engineering, The University of Osaka, Osaka 565-0871, Japan · Graduate School of Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan · Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089 USA
Multi-Source Wasserstein Distributionally Robust Graph Learning
Date pendingChuansen Peng, Yifan Xia, Jinshan Zhong +1Graph Structure Learning Graph Representation Learning
School of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, China · School of Statistics and Data Science, Southwestern University of Finance and Economics, Chengdu, Sichuan, 611130, China

Aug 20, 2025, cs.LGJ/K move · Enter open · S save

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

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

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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.

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Latest papers 95

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

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

Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming

Time-Varying Graph Learning with Constraints on Graph Temporal Variation

Multi-Source Wasserstein Distributionally Robust Graph Learning