Multidimensional binary search trees used for associative searching
Communications of the ACM
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering binary data streams with K-means
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Homophily in online dating: when do you like someone like yourself?
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Leveraging relational autocorrelation with latent group models
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
Fast and exact out-of-core and distributed k-means clustering
Knowledge and Information Systems
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)
Community evolution in dynamic multi-mode networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Semi-supervised multi-label learning by constrained non-negative matrix factorization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Network quantification despite biased labels
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
A multi-resolution approach to learning with overlapping communities
Proceedings of the First Workshop on Social Media Analytics
Leveraging social media networks for classification
Data Mining and Knowledge Discovery
Group Profiling for Understanding Social Structures
ACM Transactions on Intelligent Systems and Technology (TIST)
Multi-relational matrix factorization using bayesian personalized ranking for social network data
Proceedings of the fifth ACM international conference on Web search and data mining
Community detection via heterogeneous interaction analysis
Data Mining and Knowledge Discovery
Towards group behavioral reason mining
Expert Systems with Applications: An International Journal
Integrating social media data for community detection
MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
Multi-label relational neighbor classification using social context features
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Network denoising in social media
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Link prediction in multi-relational collaboration networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
On the utility of abstraction in labeling actors in social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Detecting profilable and overlapping communities with user-generated multimedia contents in LBSNs
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Box office prediction based on microblog
Expert Systems with Applications: An International Journal
Random walks based modularity: application to semi-supervised learning
Proceedings of the 23rd international conference on World wide web
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The study of collective behavior is to understand how individuals behave in a social network environment. Oceans of data generated by social media like Facebook, Twitter, Flickr and YouTube present opportunities and challenges to studying collective behavior in a large scale. In this work, we aim to learn to predict collective behavior in social media. In particular, given information about some individuals, how can we infer the behavior of unobserved individuals in the same network? A social-dimension based approach is adopted to address the heterogeneity of connections presented in social media. However, the networks in social media are normally of colossal size, involving hundreds of thousands or even millions of actors. The scale of networks entails scalable learning of models for collective behavior prediction. To address the scalability issue, we propose an edge-centric clustering scheme to extract sparse social dimensions. With sparse social dimensions, the social-dimension based approach can efficiently handle networks of millions of actors while demonstrating comparable prediction performance as other non-scalable methods.