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CIKM
2009
Springer
16 years 1 months ago
Group CRM: a new telecom CRM framework from social network perspective
The structure of customer communication network provides us a natural way to understand customers’ relationships. Traditional customer relationship management (CRM) methods focu...
Bin Wu, Qi Ye, Shengqi Yang, Bai Wang
SDM
2008
SIAM
138views Data Mining» more  SDM 2008»
15 years 8 months ago
Learning Markov Network Structure using Few Independence Tests
In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for...
Parichey Gandhi, Facundo Bromberg, Dimitris Margar...
ICDE
2004
IEEE
151views Database» more  ICDE 2004»
16 years 8 months ago
Improved File Synchronization Techniques for Maintaining Large Replicated Collections over Slow Networks
We study the problem of maintaining large replicated collections of files or documents in a distributed environment with limited bandwidth. This problem arises in a number of impo...
Torsten Suel, Patrick Noel, Dimitre Trendafilov
KDD
2009
ACM
180views Data Mining» more  KDD 2009»
16 years 7 months ago
Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
Sofus A. Macskassy
KDD
2009
ACM
190views Data Mining» more  KDD 2009»
16 years 7 months ago
Efficient influence maximization in social networks
Influence maximization is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. In this paper, we study the ...
Wei Chen, Yajun Wang, Siyu Yang