A Secure Online Algorithm for Link Analysis on Weighted Graph

Link analysis algorithms have been used successfully on hyperlinked data to identify authoritative documents and retrieve other information. However, existing link analysis algorithms such as HITS suffer two major limitations: (1) they only work in environments with explicit hyperlinked structure such as www or social network and (2) they fail to capture the rich information that is encoded by patterns of user access. In this paper we propose the use of weighted graph that is generated and updated via analysis of patterns of user access to address both issues. We then present a generalized HITS algorithm that is suitable for such an approach. The algorithm uses the idea of “lazy update” to amortize cost across a number of updates while still providing accurate ranking to users in real-time. We proved the convergence of the new online algorithm and evaluated its benefit using simulation. Finally we devised a scheme that makes the algorithm distributed and privacy preserving using cryptographic techniques thus making it really acceptable in settings such as collaborative work and online community.

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