The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Classifying web review opinions for consumer product analysis
Proceedings of the 11th International Conference on Electronic Commerce
Proceedings of the 11th International Conference on Electronic Commerce
Social networks integration and privacy preservation using subgraph generalization
Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics
Social Network Analysis and Mining for Business Applications
ACM Transactions on Intelligent Systems and Technology (TIST)
A spectral analysis approach for social media community detection
SocInfo'11 Proceedings of the Third international conference on Social informatics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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The Internet is an ideal platform for business-to-consumer (B2C) and business-to-business (B2B) electronic commerce where businesses and consumers conduct commerce activities such as searching for consumer products, promoting business, managing supply chain and making electronic transactions. With the advance of Web 2.0 technologies and the popularity of social media sites, social commerce offers new opportunities of social interaction between electronic commerce consumers as well as social interaction between consumers and e-retailers. The user contributed content provides a tremendous amount of information that may assist in electronic commerce services. Social network analysis and mining has been a powerful tool for electronic commerce vendors and marketing companies to understand the user behavior which is useful for identifying potential customers of their products. However, the capability of social network analysis and mining diminishes when the social network data is incomplete, especially when there are only limited ties available. The social networks extracted from explicit relationships in social media are usually sparse. Many social media users who have similar interest may not have direct interactions with one another or purchase the same products. Therefore, the explicit relationships between electronic commerce users are not sufficient to construct social networks for effective social network analysis and mining. In this work, we propose the temporal analysis techniques to identify implicit relationships for enriching the social network structure. We have conducted an experiment on Digg.com, which is a social media site for users to discover and share content from anywhere of the Web. The experiment shows that the temporal analysis techniques outperform the baseline techniques that only rely on explicit relationships.