1 citations · 1 across the 21 of their papers we have counts for
22 papers
Revisiting Maximum -Biplex Search Through -Bounded-Degree Deletion
Donghang Cui, Ronghua Li, Qiangqiang Dai +1
Biplex, as a relaxation of the biclique model, has emerged as an important cohesive subgraph model for bipartite graph analysis. The maximum -biplex search problem aims to ident…
MCI: A Maximal Clique Index for Efficient Arbitrary-Filtered Approximate Nearest Neighbor Search
Xiaowei Ye, Rong-Hua Li, Guoren Wang +3
Approximate Nearest Neighbor Search with arbitrary filtering predicates (AFANNS) is essential for modern data applications, yet existing methods often incur substantial storage and…
Density Decomposition on Hypergraphs
Xiaoyu Leng, Hongchao Qin, Rong-Hua Li
Decomposing hypergraphs is a key task in hypergraph analysis with broad applications in community detection, pattern discovery, and task scheduling. Existing approaches such as …
RNSG: A Range-Aware Graph Index for Efficient Range-Filtered Approximate Nearest Neighbor Search
Zhiqiu Zou, Ziqi Yin, Rong-Hua Li +3
Range-filtered approximate nearest neighbor (RFANN) search is a fundamental operation in modern data systems. Given a set of objects, each with a vector and a numerical attribute,…
A Computationally Efficient Framework for Overlapping Community Detection in Large Bipartite Graphs
Yue Zeng, Rong-Hua Li, Qiangqiang Dai +1
Community detection, which uncovers closely connected vertex groups in networks, is vital for applications in social networks, recommendation systems, and beyond. Real-world networ…
Scalable and Provable Kemeny Constant Computation on Static and Dynamic Graphs: A 2-Forest Sampling Approach
Cheng Li, Meihao Liao, Rong-Hua Li +1
Kemeny constant, defined as the expected hitting time of random walks from a source node to a randomly chosen target node, is a fundamental metric in graph data management with man…
Optimal -Dense Subgraph Search in Bipartite Graphs
Yalong Zhang, Rong-Hua Li, Qi Zhang +1
Dense subgraph search in bipartite graphs is a fundamental problem in graph analysis, with wide-ranging applications in fraud detection, recommendation systems, and social network…
On the Efficient Discovery of Maximum -Defective Biclique
Donghang Cui, Ronghua Li, Qiangqiang Dai +2
The problem of identifying the maximum edge biclique in bipartite graphs has attracted considerable attention in bipartite graph analysis, with numerous real-world applications suc…
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…
Efficient Maximum Fair Clique Search over Large Networks
Qi Zhang, Rong-Hua Li, Zifan Zheng +3
Mining cohesive subgraphs in attributed graphs is an essential problem in the domain of graph data analysis. The integration of fairness considerations significantly fuels interest…
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification
Henan Sun, Xunkai Li, Zhengyu Wu +3
Recently, graph neural networks (GNNs) have shown prominent performance in semi-supervised node classification by leveraging knowledge from the graph database. However, most existi…
Scalable Algorithms for Laplacian Pseudo-inverse Computation
Meihao Liao, Rong-Hua Li, Qiangqiang Dai +2
The pseudo-inverse of a graph Laplacian matrix, denoted as , finds extensive application in various graph analysis tasks. Notable examples include the calculation of ele…
Privacy-Preserving Graph Embedding based on Local Differential Privacy
Zening Li, Rong-Hua Li, Meihao Liao +2
Graph embedding has become a powerful tool for learning latent representations of nodes in a graph. Despite its superior performance in various graph-based machine learning tasks,…