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» Learning Influence Probabilities In Social Networks
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ICMLA
2009
13 years 5 months ago
Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule
Languages that combine predicate logic with probabilities are needed to succinctly represent knowledge in many real-world domains. We consider a formalism based on universally qua...
Sriraam Natarajan, Prasad Tadepalli, Gautam Kunapu...
SIGIR
2006
ACM
14 years 1 months ago
Personalized recommendation driven by information flow
We propose that the information access behavior of a group of people can be modeled as an information flow issue, in which people intentionally or unintentionally influence and in...
Xiaodan Song, Belle L. Tseng, Ching-Yung Lin, Ming...
ICDM
2010
IEEE
186views Data Mining» more  ICDM 2010»
13 years 5 months ago
MoodCast: Emotion Prediction via Dynamic Continuous Factor Graph Model
Human emotion is one important underlying force affecting and affected by the dynamics of social networks. An interesting question is "can we predict a person's mood base...
Yuan Zhang, Jie Tang, Jimeng Sun, Yiran Chen, Jing...
PODC
2010
ACM
13 years 9 months ago
Brief announcement: revisiting the power-law degree distribution for social graph analysis
The study of complex networks led to the belief that the connectivity of network nodes generally follows a Power-law distribution. In this work, we show that modeling large-scale ...
Alessandra Sala, Haitao Zheng, Ben Y. Zhao, Sabrin...
ICMI
2010
Springer
172views Biometrics» more  ICMI 2010»
13 years 5 months ago
Modelling and analyzing multimodal dyadic interactions using social networks
Social network analysis became a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore th...
Sergio Escalera, Petia Radeva, Jordi Vitrià...