48 citations · 71 across the 4 of their papers we have counts for
6 papers
Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model
Linhao Luo, Yixiang Fang, Xin Cao +2
Community detection, aiming to group the graph nodes into clusters with dense inner-connection, is a fundamental graph mining task. Recently, it has been studied on the heterogeneo…
RRCN: A Reinforced Random Convolutional Network based Reciprocal Recommendation Approach for Online Dating
Linhao Luo, Liqi Yang, Ju Xin +6
Recently, the reciprocal recommendation, especially for online dating applications, has attracted more and more research attention. Different from conventional recommendation probl…
Inductive Link Prediction for Nodes Having Only Attribute Information
Yu Hao, Xin Cao, Yixiang Fang +2
Predicting the link between two nodes is a fundamental problem for graph data analytics. In attributed graphs, both the structure and attribute information can be utilized for link…
Efficient Algorithms for Densest Subgraph Discovery
Yixiang Fang, Kaiqiang Yu, Reynold Cheng +2
Densest subgraph discovery (DSD) is a fundamental problem in graph mining. It has been studied for decades, and is widely used in various areas, including network science, biologic…
A Survey of Community Search Over Big Graphs
Yixiang Fang, Xin Huang, Lu Qin +4
With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component…
Exploring Communities in Large Profiled Graphs
Yankai Chen, Yixiang Fang, Reynold Cheng +3
Given a graph and a vertex , the community search (CS) problem aims to efficiently find a subgraph of whose vertices are closely related to . Communities are pre…