1 citations · 1 across the 21 of their papers we have counts for
10 papers · 1 filter
Efficient Antagonistic k-plex Enumeration in Signed Graphs
Lantian Xu, Rong-Hua Li, Dong Wen +3
A signed graph is a graph where each edge receives a sign, positive or negative. The signed graph model has been used in many real applications, such as protein complex discovery a…
Counting Cohesive Subgraphs with Hereditary Properties
Rong-Hua Li, Xiaowei Ye, Fusheng Jin +3
Counting small cohesive subgraphs in a graph is a fundamental operation with numerous applications in graph analysis. Previous studies on cohesive subgraph counting are mainly base…
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning
Yinlin Zhu, Xunkai Li, Zhengyu Wu +3
Subgraph federated learning (subgraph-FL) is a new distributed paradigm that facilitates the collaborative training of graph neural networks (GNNs) by multi-client subgraphs. Unfor…
Scalable -clique Densest Subgraph Search
Xiaowei Ye, Miao Qiao, Rong-Hua Li +2
In this paper, we present a collection of novel and scalable algorithms designed to tackle the challenges inherent in the -clique densest subgraph problem (\kcdsp) within networ…
K-stars LDP: A Novel Framework for (p, q)-clique Enumeration under Local Differential Privacy
Henan Sun, Zhengyu Wu, Rong-Hua Li +2
(p,q)-clique enumeration on a bipartite graph is critical for calculating clustering coefficient and detecting densest subgraph. It is necessary to carry out subgraph enumeration w…
LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning
Xunkai Li, Meihao Liao, Zhengyu Wu +4
Most existing graph neural networks (GNNs) are limited to undirected graphs, whose restricted scope of the captured relational information hinders their expressive capabilities and…
Rethinking Node-wise Propagation for Large-scale Graph Learning
Xunkai Li, Jingyuan Ma, Zhengyu Wu +4
Scalable graph neural networks (GNNs) have emerged as a promising technique, which exhibits superior predictive performance and high running efficiency across numerous large-scale…
Towards Effective and General Graph Unlearning via Mutual Evolution
Xunkai Li, Yulin Zhao, Zhengyu Wu +3
With the rapid advancement of AI applications, the growing needs for data privacy and model robustness have highlighted the importance of machine unlearning, especially in thriving…
FedGTA: Topology-aware Averaging for Federated Graph Learning
Xunkai Li, Zhengyu Wu, Wentao Zhang +3
Federated Graph Learning (FGL) is a distributed machine learning paradigm that enables collaborative training on large-scale subgraphs across multiple local systems. Existing FGL s…
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity
Xunkai Li, Zhengyu Wu, Wentao Zhang +3
Recently, Federated Graph Learning (FGL) has attracted significant attention as a distributed framework based on graph neural networks, primarily due to its capability to break dat…