activity
20142018
most citedOnline Dating Recommendations: Matching Markets and Learning Preferences

25 citations · 50 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SI2018★ 1 cited

Learning Features of Network Structures Using Graphlets

Kun Tu, Jian Li, Don Towsley +2

Networks are fundamental to the study of complex systems, ranging from social contacts, message transactions, to biological regulations and economical networks. In many realistic a…

cs.SI2018★ 11 cited

Network Classification in Temporal Networks Using Motifs

Kun Tu, Jian Li, Don Towsley +2

Network classification has a variety of applications, such as detecting communities within networks and finding similarities between those representing different aspects of the rea…

cs.SI2016★ 7 cited

Temporal Clustering in Dynamic Networks with Tensor Decomposition

Kun Tu, Bruno Ribeiro, Ananthram Swami +1

Dynamic networks are increasingly being usedd to model real world datasets. A challenging task in their analysis is to detect and characterize clusters. It is useful for analyzing…

cs.SI2014★ 25 cited

Online Dating Recommendations: Matching Markets and Learning Preferences

Kun Tu, Bruno Ribeiro, Hua Jiang +4

Recommendation systems for online dating have recently attracted much attention from the research community. In this paper we proposed a two-side matching framework for online dati…

cs.SI2014★ 6 cited

Who is Dating Whom: Characterizing User Behaviors of a Large Online Dating Site

Peng Xia, Kun Tu, Bruno Ribeiro +5

Online dating sites have become popular platforms for people to look for potential romantic partners. It is important to understand users' dating preferences in order to make bette…