Online Discussion Participation Prediction Using Non-negative Matrix Factorization
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
A graph-based clustering scheme for identifying related tags in folksonomies
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Extracting local community structure from local cores
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
Community detection in Social Media
Data Mining and Knowledge Discovery
A Method for Local Community Detection by Finding Core Nodes
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Churn Prediction in a Real Online Social Network Using Local CommunIty Analysis
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
A general collaborative filtering framework based on matrix bordered block diagonal forms
Proceedings of the 24th ACM Conference on Hypertext and Social Media
Improve collaborative filtering through bordered block diagonal form matrices
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
A seed-centric community detection algorithm based on an expanding ring search
AWC '13 Proceedings of the First Australasian Web Conference - Volume 144
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In this paper, we extend the concept of degree from single vertex to sub-graph, and present a formal definition of module/community in a network based on this extension. A new locally optimized algorithm is designed to find the module for a given source vertex in a network. Our analysis shows that the complexity of this algorithm is O(K2d), where K is the number of vertices to be explored in the sub-graph and d is the average degree of the vertices in the sub-graph. Based on this algorithm, we implement a JAVA tool, MoNet, for exploring local community structures in large networks. Using this tool to analyze a co-purchase network from Amazon shows that there are local community structures in this network. Further analyses on these local community structures demonstrate that media items are much easier to form compact local modules than book items do, indicating that recommending digital media items to customers based on co-purchasing information in the online store will be more efficient than recommending books.