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      Localized Algorithm of Community Detection on Large-Scale Decentralized Social Networks

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          Abstract

          Despite the overwhelming success of the existing Social Networking Services (SNS), their centralized ownership and control have led to serious concerns in user privacy, censorship vulnerability and operational robustness of these services. To overcome these limitations, Distributed Social Networks (DSN) have recently been proposed and implemented. Under these new DSN architectures, no single party possesses the full knowledge of the entire social network. While this approach solves the above problems, the lack of global knowledge for the DSN nodes makes it much more challenging to support some common but critical SNS services like friends discovery and community detection. In this paper, we tackle the problem of community detection for a given user under the constraint of limited local topology information as imposed by common DSN architectures. By considering the Personalized Page Rank (PPR) approach as an ink spilling process, we justify its applicability for decentralized community detection using limited local topology information.Our proposed PPR-based solution has a wide range of applications such as friends recommendation, targeted advertisement, automated social relationship labeling and sybil defense. Using data collected from a large-scale SNS in practice, we demonstrate our adapted version of PPR can significantly outperform the basic PR as well as two other commonly used heuristics. The inclusion of a few manually labeled friends in the Escape Vector (EV) can boost the performance considerably (64.97% relative improvement in terms of Area Under the ROC Curve (AUC)).

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          The anatomy of a large-scale hypertextual Web search engine

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            Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search

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              Bubble rap

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                Author and article information

                Journal
                1212.6323

                Social & Information networks, General physics, Machine learning

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