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Abstract:
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In this paper, we address the problem of graph feature extraction and selection for link analysis in weblogs and similar social networks. First, we present an approach based on collaborative recommendation using the link structure of a social network and content-based recommendation using mutual declared interests. Next, we describe the application of this approach to a small representative subset of a large real-world social network: the user/community network of the blog service LiveJournal. We then discuss the ground features available in LiveJournal's public user information pages and describe some graph algorithms for analysis of the social network along with a feature set for classifying users as friends or non-friends. These are used to identify candidates, provide ground truth for recommendations, and construct features for learning the concept of an existing link. Finally, we evaluate the performance of classification learning algorithms and committee machines relative to genetic feature selection wrappers and filters.
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