Minimum Topology Attacks for Graph Neural Networks

no code implementations • 5 Mar 2024 • Mengmei Zhang, Xiao Wang, Chuan Shi, Lingjuan Lyu, Tianchi Yang, Junping Du

To break this dilemma, we propose a new type of topology attack, named minimum-budget topology attack, aiming to adaptively find the minimum perturbation sufficient for a successful attack on each node.

Endowing Pre-trained Graph Models with Provable Fairness

1 code implementation • 19 Feb 2024 • Zhongjian Zhang, Mengmei Zhang, Yue Yu, Cheng Yang, Jiawei Liu, Chuan Shi

Furthermore, with GraphPAR, we quantify whether the fairness of each node is provable, i. e., predictions are always fair within a certain range of sensitive attribute semantics.

Attribute Fairness +1

GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks

1 code implementation • 11 Feb 2024 • Mengmei Zhang, Mingwei Sun, Peng Wang, Shen Fan, Yanhu Mo, Xiaoxiao Xu, Hong Liu, Cheng Yang, Chuan Shi

Large language models (LLMs) like ChatGPT, exhibit powerful zero-shot and instruction-following capabilities, have catalyzed a revolutionary transformation across diverse fields, especially for open-ended tasks.

Graph Question Answering Instruction Following +4

Towards Graph Foundation Models: A Survey and Beyond

no code implementations • 18 Oct 2023 • Jiawei Liu, Cheng Yang, Zhiyuan Lu, Junze Chen, Yibo Li, Mengmei Zhang, Ting Bai, Yuan Fang, Lichao Sun, Philip S. Yu, Chuan Shi

Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and several other domains.

Graph Learning

Provable Training for Graph Contrastive Learning

1 code implementation • NeurIPS 2023 • Yue Yu, Xiao Wang, Mengmei Zhang, Nian Liu, Chuan Shi

To this end, we propose the PrOvable Training (POT) for GCL, which regularizes the training of GCL to encode node embeddings that follows the GCL principle better.

Contrastive Learning