Paper Info
Title
Structure, tie persistence and event detection in large phone and SMS networks
Abstract
The effect of the network structure on the dynamics of social and communication networks has been of interest in recent years. It has been observed that network properties such as neighborhood overlap, clustering coefficient, etc. influence the tie strengths and link persistence between individuals. In this paper we study the communication records (both phonecall and SMS) of 2 million anonymized customers of a large mobile phone company with 50 million interactions over a period of 6 months. Our major contributions are the following: (a) we analyze several structural properties in these call/SMS networks and the correlations between them; (b) we formulate a learning problem to determine whether existing links between users will persist in the future. Experimental results show that our method performs better than existing rule based methods; and (c) we propose a change-point detection method in user behaviors using eigenvalue analysis of various behavioral features extracted over time. Our analysis shows that change-points detected by our method coincide with the social events and festivals in our data.
Year
DOI
Venue
2010
10.1145/1830252.1830254
MLG@KDD
Keywords
Field
DocType
sms network,network structure,analysis shows,network property,change-point detection method,million anonymized customer,event detection,communication record,million interaction,tie persistence,communication network,large phone,eigenvalue analysis,change point detection,feature extraction,link analysis,clustering coefficient,rule based
Eigenvalue analysis,Data mining,Rule-based system,Telecommunications network,Link analysis,Computer science,Phone,Artificial intelligence,Mobile phone,Clustering coefficient,Machine learning,Network structure
Conference
Citations 
PageRank 
References 
5
0.51
7
Authors
2
Name
Order
Citations
PageRank
Leman Akoglu1149871.55
Bhavana Bharat Dalvi220117.31