3 citations · 3 across the 2 of their papers we have counts for
4 papers
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
Yunfei Liu, Jintang Li, Yuehe Chen +9
Graph clustering, a fundamental and challenging task in graph mining, aims to classify nodes in a graph into several disjoint clusters. In recent years, graph contrastive learning…
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
Jintang Li, Jiawang Dan, Ruofan Wu +9
Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data. However, many real-world applications,…
A Survey of the Evolution of Language Model-Based Dialogue Systems: Data, Task and Models
Hongru Wang, Lingzhi Wang, Yiming Du +4
Dialogue systems (DS), including the task-oriented dialogue system (TOD) and the open-domain dialogue system (ODD), have always been a fundamental task in natural language processi…
HeteroNet: Heterophily-aware Representation Learning on Heterogenerous Graphs
Jintang Li, Zheng Wei, Jiawang Dan +9
Real-world graphs are typically complex, exhibiting heterogeneity in the global structure, as well as strong heterophily within local neighborhoods. While a growing body of literat…