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In recent years, social computing has become a very popular application in the Internet, and therefore large amount of social (communication) data has been collected in different social computing application. This paper will introduce a methodology to collect and analyze multi-source social, and by this for extracting social networks from the data. A system architecture will also be presented in this paper to show how the data can be collected, pre-processed, analyzed. Furthermore, the system will allow the users to use the data as a resource for personal decision support.

Due to the proliferation of online social networking, a large number of personal data are publicly available. As such, personal attacks, reputational, financial, or family losses might occur once this personal and sensitive information falls into the hands of malicious hackers. Research on Privacy-Preserving Network Publishing has attracted much attention in recent years. But most work focus on node de-identification and link protection. In academic social networks, business transaction networks, and transportation networks, etc, node identities and link structures are public knowledge but weights and shortest paths are sensitive. In this work, we study the problem of k-anonymous path privacy. A published network graph with k-anonymous path privacy has at least k indistinguishable shortest paths between the source and destination vertices [21]. In order to achieve such privacy, three different strategies of modification on edge weights of directed graphs are proposed. Numerical comparisons show that weight-proportional-based strategy is more efficient than PageRank-based and degree-based strategies. In addition, it is also more efficient and causes less information loss than running on un-directed graphs.