Probabilistically rewired message-passing neural networks
Message-passing graph neural networks (MPNNs) emerged as powerful tools for
processing graph-structured input. However, they operate on a fixed input graph structure …
processing graph-structured input. However, they operate on a fixed input graph structure …
Challenging Low Homophily in Social Recommendation
Social relations are leveraged to tackle the sparsity issue of user-item interaction data in
recommendation under the assumption of social homophily. However, social …
recommendation under the assumption of social homophily. However, social …
Latent graph inference with limited supervision
Latent graph inference (LGI) aims to jointly learn the underlying graph structure and node
representations from data features. However, existing LGI methods commonly suffer from the …
representations from data features. However, existing LGI methods commonly suffer from the …
Graph Reasoning with LLMs (GReaL)
Graphs are a powerful tool for representing and analyzing complex relationships in real-
world applications. Large Language Models (LLMs) have demonstrated impressive …
world applications. Large Language Models (LLMs) have demonstrated impressive …
Motif-driven Subgraph Structure Learning for Graph Classification
To mitigate the suboptimal nature of graph structure, Graph Structure Learning (GSL) has
emerged as a promising approach to improve graph structure and boost performance in …
emerged as a promising approach to improve graph structure and boost performance in …
Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning
Large language models (LLMs) have showcased remarkable reasoning capabilities, yet
they remain susceptible to errors, particularly in temporal reasoning tasks involving complex …
they remain susceptible to errors, particularly in temporal reasoning tasks involving complex …