1 citations · 2 across the 9 of their papers we have counts for
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Restoring Network Evolution from Static Structure
Jiu Zhang, Zhanwei Du, Hongwei Hu +6
The dynamical evolution of complex networks underpins the structure-function relationships in natural and artificial systems. Yet, restoring a network's formation from a single sta…
Bridging Code Graphs and Large Language Models for Better Code Understanding
Zeqi Chen, Zhaoyang Chu, Yi Gui +3
Large Language Models (LLMs) have demonstrated remarkable performance in code intelligence tasks such as code generation, summarization, and translation. However, their reliance on…
Large Language Model Enhanced Graph Invariant Contrastive Learning for Out-of-Distribution Recommendation
Jiahao Liang, Haoran Yang, Xiangyu Zhao +4
Out-of-distribution (OOD) generalization has emerged as a significant challenge in graph recommender systems. Traditional graph neural network algorithms often fail because they le…
Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning
Zihao Zhao, Xinlong Zhai, Jinyu Yang +1
Foundation models have achieved great success in natural language processing (NLP) and computer vision (CV). Their success largely stems from the ability to integrate multi-domain…
HeTGB: A Comprehensive Benchmark for Heterophilic Text-Attributed Graphs
Shujie Li, Yuxia Wu, Yuan Fang +1
Graph neural networks (GNNs) have demonstrated success in modeling relational data primarily under the assumption of homophily. However, many real-world graphs exhibit heterophily,…
Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic Biases
Michael Y. Hu, Jackson Petty, Chuan Shi +2
Pretraining language models on formal language can improve their acquisition of natural language. Which features of the formal language impart an inductive bias that leads to effec…