Graph Robustness

Latest papers

11 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Stability Analysis of Signed Graphs under Structural Perturbations
Oct 21, 2026Ning Zhang, Li ZhangGraph Robustness Graph Representation Learning
University of Oxford, Department of Statistics, Oxoford, United Kingdom · University College London, Institute of Financial Technology, Department of Civil, Environmental and Geomatic Engineering, London, United Kingdom
ZeroGAR: Benchmarking the Adversarial Robustness of Zero-Shot Graph Models
Sep 27, 2026Zhongjian Zhang, Xiao Wang, Busheng Zhang +5Graph Representation Learning Adversarial Attacks
Beijing University of Posts and Telecommunications · Beihang University · Tsinghua University +2
When does a spectral prior help graph learning? Connectivity-loss estimation under road-network disruptions
Sep 10, 2026Van-Truong LeGraph Neural Networks Graph Robustness
Faculty of Information Technology, University of Science, Viet Nam National University Ho Chi Minh City, Viet Nam
Convex--Concave Quadratic Spectral Filtering for Graph Neural Networks
Jun 23, 2026Ranhui Yan, Jia Cai, Mengzhu Chen +1Graph Neural Networks Graph Robustness
School of Computing and Artificial Intelligence, Guangzhou Xinhua University, Guangzhou, Guangdong, China · School of Statistics and Data Science, Guangdong University of Finance and Economics,2026 Guangzhou, Guangdong, China
Topology-Aware Gaussian Graph Repair for Robust Graph Neural Networks
Jun 2, 2026Anubha Goel, Juho KanniainenGraph Structure Learning Graph Neural Networks
Computing Science/Financial Computing and Data Analytics Group, Tampere University, Tampere, 33720, Finland
On the Safety of Graph Representation Learning
May 7, 2026Xiaoguang Guo, Zehong Wang, Ziming Li +5Graph Representation Learning Algorithmic Fairness
University of Connecticut · University of Notre Dame
A Unified Benchmark for Evaluating Knowledge Graph Construction Methods and Graph Neural Networks
May 6, 2026Othmane Kabal, Mounira Harzallah, Fabrice Guillet +2KG Construction Graph Neural Networks
Nantes University, LS2N, Nantes, 44300, France · National Institute of Informatics, Chiyoda-ku, Tokyo, 101-8430, Japan · Institute of Science Tokyo, Tokyo, 152-8550, Japan
ASPECT: Node-Level Adaptive Spectral Fusion for Graph Contrastive Learning
Apr 2, 2026Zhuolong Li, Boxue Yang, Haopeng ChenGraph Robustness Graph Contrastive Learning
School of Computer Science, Shanghai Jiao Tong University
A Systematic Evaluation Protocol of Graph-Derived Signals for Tabular Machine Learning
Mar 14, 2026Mario Heidrich, Jeffrey Heidemann, Rüdiger Buchkremer +1Graph Robustness Tabular ML
Universidad Católica San Antonio de Murcia (UCAM), Murcia, Spain · Institute of IT Management and Digitization Research (IFID), FOM University of Applied Sciences in Economics and Management, Düsseldorf, Germany
High-order Knowledge Based Network Controllability Robustness Prediction: A Hypergraph Neural Network Approach
Feb 28, 2026Shibing Mo, Jiarui Zhang, Jiayu Xie +2Graph Robustness
School of Artifcial Intelligence, Xidian University, Xian 710126, China. · School of Computer Science and Technology, Xidian University, Xian 710126, China. · Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China.
Harnessing Trust in Directed Graphs: Redefining Robustness of Graph Learning
Jun 3, 2023Zhichao Hou, Xitong Zhang, Wei Wang +2Graph Neural Networks Adversarial Robustness
North Carolina State University · Michigan State University · Meta +1

Oct 21, 2026J/K move · Enter open · S save

Stability Analysis of Signed Graphs under Structural Perturbations

Ning Zhang, Li Zhang

University of Oxford, Department of Statistics, Oxoford, United Kingdom · University College London, Institute of Financial Technology, Department of Civil, Environmental and Geomatic Engineering, London, United Kingdom

Graph Robustness Graph Representation Learning

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Signed graph learning has emerged as a powerful framework for modeling networks with both positive and negative interactions. While graph-filter-based methods have achieved promising empirical performance on signed networks, their stability under edge perturbations remains less well understood. Existing stability analyses primarily focus on unsigned graphs and do not account for sign-flip perturbations. In this paper, we study the stability of signed graph filters under edge additions, deletions, and sign flips. Our theoretical analysis reveals how signed edge perturbations and node feature correlations jointly determine the learned representation stability. Based on this analysis, we propose a theory-guided perturbation strategy that selects structurally influential signed-edge modifications. Experiments on synthetic and real signed graphs show that the proposed method consistently induces larger embedding drift, polarity change, and downstream link prediction degradation than baselines. These results highlight the importance of sign-aware and distribution-aware stability analysis for signed graph learning.