Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
A Multi-Agent Approach for Peer-to-Peer Based Information Retrieval System
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Learning Hierarchical Models of Scenes, Objects, and Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Probabilistic models for discovering e-communities
Proceedings of the 15th international conference on World Wide Web
Pachinko allocation: DAG-structured mixture models of topic correlations
ICML '06 Proceedings of the 23rd international conference on Machine learning
Topic evolution and social interactions: how authors effect research
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Identification and evaluation of weak community structures in networks
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Extracting and ranking viral communities using seeds and content similarity
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Discovering Knowledge-Sharing Communities in Question-Answering Forums
ACM Transactions on Knowledge Discovery from Data (TKDD)
Information theoretic criteria for community detection
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Empirical study of topic modeling in Twitter
Proceedings of the First Workshop on Social Media Analytics
Community discovery using nonnegative matrix factorization
Data Mining and Knowledge Discovery
MPI/OpenMP hybrid parallel inference for Latent Dirichlet Allocation
Proceedings of the Third Workshop on Large Scale Data Mining: Theory and Applications
Agent-based modeling of netizen groups in chinese internet events
PAISI'11 Proceedings of the 6th Pacific Asia conference on Intelligence and security informatics
Probabilistic model for discovering topic based communities in social networks
Proceedings of the 20th ACM international conference on Information and knowledge management
Literature search through mixed-membership community discovery
SBP'10 Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and Prediction
Using content and interactions for discovering communities in social networks
Proceedings of the 21st international conference on World Wide Web
V-SMART-join: a scalable mapreduce framework for all-pair similarity joins of multisets and vectors
Proceedings of the VLDB Endowment
Generative Models for Evolutionary Clustering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Latent Community Topic Analysis: Integration of Community Discovery with Topic Modeling
ACM Transactions on Intelligent Systems and Technology (TIST)
Mining groups of common interest: discovering topical communities with network flows
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Complex networks exist in a wide array of diverse domains, ranging from biology, sociology, and computer science. These real-world networks, while disparate in nature, often comprise of a set of loose clusters(a.k.a communities), whose members are better connected to each other than to the rest of the network. Discovering such inherent community structures can lead to deeper understanding about the networks and therefore has raised increasing interests among researchers from various disciplines. This paper describes GWN-LDA (Generic weighted network-Latent Dirichlet Allocation) model, a hierarchical Bayesian model derived from the widely-received LDA model, for discovering probabilistic community profiles in social networks. In this model, communities are modeled as latent variables and defined as distributions over the social actor space. In addition, each social actor belongs to every community with different probability. This paper also proposes two different network encoding approaches and explores the impact of these two approaches to the community discovery performance. This model is evaluated on two research collaborative networks: CiteSeer and NanoSCI. The experimental results demonstrate that this approach is promising for discovering community structures in large-scale networks.