Graph neural networks (GNNs) offer promising learning methods for graph-related tasks. However, GNNs are at risk of adversarial attacks. Two primary limitations of the current evasion attack methods are highlighted: (1) The current GradArgmax ignores the "long-term" benefit of the perturbation. It is faced with zero-gradient and invalid benefit estimates in certain situations. (2) In the reinforcement learning-based attack methods, the learned attack strategies might not be transferable when the attack budget changes. To this end, we first formulate the perturbation space and propose an evaluation framework and the projective ranking method. We aim to learn a powerful attack strategy then adapt it as little as possible to generate adversarial samples under dynamic budget settings. In our method, based on mutual information, we rank and assess the attack benefits of each perturbation for an effective attack strategy. By projecting the strategy, our method dramatically minimizes the cost of learning a new attack strategy when the attack budget changes. In the comparative assessment with GradArgmax and RL-S2V, the results show our method owns high attack performance and effective transferability. The visualization of our method also reveals various attack patterns in the generation of adversarial samples.
Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function
Illinois Institute of Technology（伊利诺伊理工学院）；The Pennsylvania State University（宾夕法尼亚州立大学）；Huazhong University of Science and Technology（华中科技大学）；University of Maryland（马里兰大学）；Nanchang University（南昌大学）；Duke University（杜克大学）
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
University of Oxford（牛津大学）；Amazon Web Services AI Shanghai Lablet（亚马逊云科技AI上海实验室）；The Alan Turing Institute（阿兰·图灵研究所）；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）
Degree-Mass Message Passing for Betweenness Ranking in Directed and Undirected Networks
King's College London（国王学院伦敦）；University of Bonn（波恩大学）；Lamarr Institute（拉马尔研究所）；University of Tokyo（东京大学）；National Institute of Advanced Industrial Science（国家先进工业科学研究院）；Université Côte d’Azur（坎特伯雷大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化技术研究所）；I3S（信息科学与系统研究所）；Institute of Science Tokyo（东京科学研究所）
MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering
National University of Singapore（新加坡国立大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing University of Posts and Telecommunications（北京邮电大学）
A Critical Audit of Spatiotemporal Forecasting Benchmark Datasets and Models
Imperial College London（帝国理工学院）；Ruhr University Bochum（鲁尔大学波鸿分校）；Queen Mary University of London（伦敦玛丽女王大学）；Ben-Gurion University of the Negev（内盖夫本-古里安大学）
Graph Neural Networks for Scalable and Transferable Node Centrality Approximation
Center for Nonlinear and Complex Systems, Università degli Studi dell’Insubria（非线性与复杂系统中心，因苏布利亚大学）；Data Science Institute, Hasselt University（哈塞尔特大学数据科学研究所）
Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense
ISP RAS Research Center for Trusted Artificial Intelligence（俄罗斯科学院可信人工智能研究信息与处理中心）；Ivannikov Institute for System Programming of the Russian Academy of Sciences（俄罗斯科学院伊万诺夫系统编程研究所）；Moscow Institute of Physics and Technology (National Research University)（莫斯科物理技术学院（国家研究大学））；Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）；AIRI
GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations
Stony Brook University（石溪大学）；Sapienza University of Rome（罗马第一大学）；The Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Rutgers University（罗格斯大学）；South China Normal University（华南师范大学）
Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation
Department of Computer Science, University of Manitoba（曼尼托巴大学计算机科学系）；University of Toledo（托莱多大学）；NEC Laboratories Europe（NEC欧洲实验室）；Department of Computer Science, Virginia Tech, USA（弗吉尼亚理工学院弗吉尼亚理工大学计算机科学系）；AI Initiative - University of Central Florida（中央佛罗里达大学人工智能计划）
Hierarchy-Aware Neural Subgraph Matching with Enhanced Similarity Measure
College of Computer Science and Technology, National University of Defense Technology（计算机科学与技术学院，国防科技大学）；College of Information and Communication, National University of Defense Technology（信息与通信学院，国防科技大学）
Graph Neural Network-based Algorithm Selection for the Traveling Salesman Problem: A Systematic Study of Cost and Rank Losses under Distinct Budget Regimes
R2PS: Worst-Case Robust Real-Time Pursuit Strategies under Partial Observability
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）
Highly Accurate Disease Diagnosis and Highly Reproducible Biomarker Identification with PathFormer
Washington University in St. Louis（圣路易斯华盛顿大学）；The Hong Kong Polytechnic University（香港理工大学）；Institute for Informatics (I2)（信息学研究所（I2））；Peking University（北京大学）；Washington University School of Medicine（华盛顿大学医学院）
