Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Leveraging relational autocorrelation with latent group models
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
Finding community structure in mega-scale social networks: [extended abstract]
Proceedings of the 16th international conference on World Wide Web
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
Using ghost edges for classification in sparsely labeled networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
An Algorithm to Find Overlapping Community Structure in Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Large scale multi-label classification via metalabeler
Proceedings of the 18th international conference on World wide web
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Characterizing user behavior in online social networks
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Scalable learning of collective behavior based on sparse social dimensions
Proceedings of the 18th ACM conference on Information and knowledge management
Toward Predicting Collective Behavior via Social Dimension Extraction
IEEE Intelligent Systems
Exploring the Community Set Space
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Community finding within the community set space
Proceedings of the 7th Workshop on Social Network Mining and Analysis
Random walks based modularity: application to semi-supervised learning
Proceedings of the 23rd international conference on World wide web
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The recent few years have witnessed a rapid surge of participatory web and social media, enabling a new laboratory for studying human relations and collective behavior on an unprecedented scale. In this work, we attempt to harness the predictive power of social connections to determine the preferences or behaviors of individuals such as whether a user supports a certain political view, whether one likes one product, whether he/she would like to vote for a presidential candidate, etc. Since an actor is likely to participate in multiple different communities with each regulating the actor's behavior in varying degrees, and a natural hierarchy might exist between these communities, we propose to zoom into a network at multiple different resolutions and determine which communities are informative of a targeted behavior. We develop an efficient algorithm to extract a hierarchy of overlapping communities. Empirical results on several large-scale social media networks demonstrate the superiority of our proposed approach over existing ones without considering the multi-resolution or overlapping property, indicating its highly promising potential in real-world applications.