Tang et al., TRANSACTION ON KNOWLEDGE AND DATA ENGINEERING 2010

Citation

Authors: Lei Tang, Huan Liu, and Jianping Zhang

Title: Identifying Evolving Groups in Dynamic Multi-Mode Networks

Journal: TRANSACTION ON KNOWLEDGE AND DATA ENGINEERING 2010

Online version

Paper: [1]

Summary

This paper tries to identify communities in multi-mode networks (networks which contain heterogeneous types of actors and different interactions between these actors). Other goal of this paper is to detect and identify the changes between community memberships and the interactions between actors during time. The authors used a temporally-regularized framework to solve this problem. They examined their framework on both synthetic and real datasets and obtained good results in efficiency and generality of their method.

Brief description of the method

In this paper the authors have used block model approximation to approximate the transaction matrix between two modes at each time, based on group interaction between modes at that time and membership of each mode in each community. Then an error function has been used to identify the error between these two values. The authors have introduced different variations of this model based on various requirements. The defined model a are:

Dataset used

Enron data

DBLP data