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CIKM
2010
Springer
15 years 4 months ago
Mining topic-level influence in heterogeneous networks
Influence is a complex and subtle force that governs the dynamics of social networks as well as the behaviors of involved users. Understanding influence can benefit various applic...
Lu Liu, Jie Tang, Jiawei Han, Meng Jiang, Shiqiang...
WSDM
2012
ACM
283views Data Mining» more  WSDM 2012»
14 years 2 months ago
The life and death of online groups: predicting group growth and longevity
We pose a fundamental question in understanding how to identify and design successful communities: What factors predict whether a community will grow and survive in the long term?...
Sanjay Ram Kairam, Dan J. Wang, Jure Leskovec
CORR
2010
Springer
168views Education» more  CORR 2010»
15 years 5 months ago
Gaussian Process Structural Equation Models with Latent Variables
In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by...
Ricardo Silva
ICDM
2010
IEEE
212views Data Mining» more  ICDM 2010»
15 years 4 months ago
Modeling Information Diffusion in Implicit Networks
Social media forms a central domain for the production and dissemination of real-time information. Even though such flows of information have traditionally been thought of as diffu...
Jaewon Yang, Jure Leskovec
BIOWIRE
2007
Springer
16 years 27 days ago
User-Centric Mobility Models for Opportunistic Networking
Abstract. In this chapter we survey the most recent proposals for modelling user mobility in mobile pervasive networks, and specifically in opportunistic networks. We identify two...
Chiara Boldrini, Marco Conti, Andrea Passarella