Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the bursty evolution of blogspace
WWW '03 Proceedings of the 12th international conference on World Wide Web
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Extracting evolution of web communities from a series of web archives
Proceedings of the fourteenth ACM conference on Hypertext and hypermedia
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Dynamic social network analysis using latent space models
ACM SIGKDD Explorations Newsletter
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Spectral Clustering, With Application To Speech Separation
The Journal of Machine Learning Research
Evolutionary spectral clustering by incorporating temporal smoothness
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
An event-based framework for characterizing the evolutionary behavior of interaction graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Blog Community Discovery and Evolution Based on Mutual Awareness Expansion
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks
Proceedings of the 17th international conference on World Wide Web
Constructing evolutionary taxonomy of collaborative tagging systems
Proceedings of the 18th ACM conference on Information and knowledge management
Quantifying sentiment and influence in blogspaces
Proceedings of the First Workshop on Social Media Analytics
Community Discovery via Metagraph Factorization
ACM Transactions on Knowledge Discovery from Data (TKDD)
Overlapping communities in dynamic networks: their detection and mobile applications
MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
A time-varying propagation model of hot topic on BBS sites and Blog networks
Information Sciences: an International Journal
Role-dynamics: fast mining of large dynamic networks
Proceedings of the 21st international conference companion on World Wide Web
Community cores in evolving networks
Proceedings of the 21st international conference companion on World Wide Web
Community detection via heterogeneous interaction analysis
Data Mining and Knowledge Discovery
Dynamic clustering with soft computing
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A sparsity-inducing formulation for evolutionary co-clustering
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-Layer network for influence propagation over microblog
PAISI'12 Proceedings of the 2012 Pacific Asia conference on Intelligence and Security Informatics
Maximum margin clustering on evolutionary data
Proceedings of the 21st ACM international conference on Information and knowledge management
User community discovery from multi-relational networks
Decision Support Systems
Modeling dynamic behavior in large evolving graphs
Proceedings of the sixth ACM international conference on Web search and data mining
Social network restructuring after a node removal
International Journal of Web Engineering and Technology
OCTracker: A Density-Based Framework for Tracking the Evolution of Overlapping Communities in OSNs
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Community-based features for identifying spammers in online social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Incremental local community identification in dynamic social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Adaptive evolutionary clustering
Data Mining and Knowledge Discovery
Discovering generalized association rules from Twitter
Intelligent Data Analysis
Social reader: towards browsing the social web
Multimedia Tools and Applications
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We discover communities from social network data and analyze the community evolution. These communities are inherent characteristics of human interaction in online social networks, as well as paper citation networks. Also, communities may evolve over time, due to changes to individuals' roles and social status in the network as well as changes to individuals' research interests. We present an innovative algorithm that deviates from the traditional two-step approach to analyze community evolutions. In the traditional approach, communities are first detected for each time slice, and then compared to determine correspondences. We argue that this approach is inappropriate in applications with noisy data. In this paper, we propose FacetNet for analyzing communities and their evolutions through a robust unified process. This novel framework will discover communities and capture their evolution with temporal smoothness given by historic community structures. Our approach relies on formulating the problem in terms of maximum a posteriori (MAP) estimation, where the community structure is estimated both by the observed networked data and by the prior distribution given by historic community structures. Then we develop an iterative algorithm, with proven low time complexity, which is guaranteed to converge to an optimal solution. We perform extensive experimental studies, on both synthetic datasets and real datasets, to demonstrate that our method discovers meaningful communities and provides additional insights not directly obtainable from traditional methods.