activity
20182021
most citedMining Bursting Communities in Temporal Graphs

3 citations · 5 across the 4 of their papers we have counts for

collaborators

7 papers

cs.LG20212 cited

Deep Unsupervised Active Learning on Learnable Graphs

Handong Ma, Changsheng Li, Xinchu Shi +2

Recently deep learning has been successfully applied to unsupervised active learning. However, current method attempts to learn a nonlinear transformation via an auto-encoder while…

cs.DB2021

Efficient Top-k Ego-Betweenness Search

Qi Zhang, Rong-Hua Li, Minjia Pan +3

Betweenness centrality, measured by the number of times a vertex occurs on all shortest paths of a graph, has been recognized as a key indicator for the importance of a vertex in t…

cs.DB2021

Multi-attributed Community Search in Road-social Networks

Fangda Guo, Ye Yuan, Guoren Wang +2

Given a location-based social network, how to find the communities that are highly relevant to query users and have top overall scores in multiple attributes according to user pref…

cs.LG2020

On Deep Unsupervised Active Learning

Changsheng Li, Handong Ma, Zhao Kang +3

Unsupervised active learning has attracted increasing attention in recent years, where its goal is to select representative samples in an unsupervised setting for human annotating.…

cs.DB2019

Cracking In-Memory Database Index A Case Study for Adaptive Radix Tree Index

Gang Wu, Yidong Song, Guodong Zhao +5

Indexes provide a method to access data in databases quickly. It can improve the response speed of subsequent queries by building a complete index in advance. However, it also lead…

cs.SI20193 cited

Mining Bursting Communities in Temporal Graphs

Hongchao Qin, Rong-Hua Li, Guoren Wang +3

Temporal graphs are ubiquitous. Mining communities that are bursting in a period of time is essential to seek emergency events in temporal graphs. Unfortunately, most previous stud…