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Abstract

Social tagging networks have become highly popular for publishing and searching
contents. Users in such networks can review, rate and comment on contents, or
annotate them with keywords (social tags) to give short but exact text
representations of even non-textual contents. In addition, there is an inherent
support for interactions and relationships among users. Thus, users naturally
form groups of friends or of common interests.

We address three research areas in our work utilising these intrinsic features
of social tagging networks.

(1) We investigate new approaches for exploiting the social knowledge of and
the relationships between users for searching and recommending relevant
contents, and integrate them in a comprehensive framework, coined SENSE, for
search in social tagging networks.

(2) To dynamically update precomputed lists of transitive friends in descending
order of their distance in user graphs of social tagging networks, we provide
an algorithm for incrementally solving the all pairs shortest distance problem
in large, disk-resident graphs and formally prove its correctness.

(3) Since users are content providers in social tagging networks, users may
keep their own data at independent, local peers that collaborate in a
distributed P2P network. We provide an algorithm for such systems to counter
cheating of peers in authority computations over social networks.

The viability of each solution is demonstrated by extensive experiments
regarding effectiveness and efficiency.