48 citations · 123 across the 11 of their papers we have counts for
11 papers
An Augmented Index-based Efficient Community Search for Large Directed Graphs
Yankai Chen, Jie Zhang, Yixiang Fang +2
Given a graph G and a query vertex q, the topic of community search (CS), aiming to retrieve a dense subgraph of G containing q, has gained much attention. Most existing works focu…
A Survey of Densest Subgraph Discovery on Large Graphs
Wensheng Luo, Chenhao Ma, Yixiang Fang +1
With the prevalence of graphs for modeling complex relationships among objects, the topic of graph mining has attracted a great deal of attention from both academic and industrial…
Bipartite Graph Convolutional Hashing for Effective and Efficient Top-N Search in Hamming Space
Yankai Chen, Yixiang Fang, Yifei Zhang +1
Searching on bipartite graphs is basal and versatile to many real-world Web applications, e.g., online recommendation, database retrieval, and query-document searching. Given a que…
WISK: A Workload-aware Learned Index for Spatial Keyword Queries
Yufan Sheng, Xin Cao, Yixiang Fang +4
Spatial objects often come with textual information, such as Points of Interest (POIs) with their descriptions, which are referred to as geo-textual data. To retrieve such data, sp…
GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs
Linhao Luo, Yixiang Fang, Moli Lu +3
Heterogeneous graphs, which contain nodes and edges of multiple types, are prevalent in various domains, including bibliographic networks, social media, and knowledge graphs. As a…
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…