C4.5: programs for machine learning
C4.5: programs for machine learning
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
IEEE Intelligent Systems
ACM SIGKDD Explorations Newsletter
Usage patterns of collaborative tagging systems
Journal of Information Science
The political blogosphere and the 2004 U.S. election: divided they blog
Proceedings of the 3rd international workshop on Link discovery
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Ontologies are us: A unified model of social networks and semantics
Web Semantics: Science, Services and Agents on the World Wide Web
Analysis of topological characteristics of huge online social networking services
Proceedings of the 16th international conference on World Wide Web
Ontologies are us: a unified model of social networks and semantics
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Mining competitor relationships from online news: A network-based approach
Electronic Commerce Research and Applications
Internet Auction Fraud Detection Using Social Network Analysis and Classification Tree Approaches
International Journal of Electronic Commerce
Mining longitudinal network for predicting company value
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Recently, many Web services such as social networking services, blogs, and collaborative tagging have become widely popular. Many attempts are being made to investigate user interactions by analyzing social networks among users. However, analyzing a social network with attributional data is often not an easy task because numerous ways exist to define features through aggregation of different tables. In this study, we propose an algorithm to identify important network-based features systematically from a given social network to analyze user behavior efficiently and to expand the services. We apply our method for link-based classification and link prediction tasks with two different datasets, i.e., an @cosme (an online viral marketing site) dataset and a Hatena Bookmark (collaborative tagging service) dataset, to demonstrate the usefulness of our algorithm. Our algorithm is general and can provide useful network-based features for social network analyses.