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Network-Based Social Search

Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10100)

Abstract

With the wide adoption of social media in recent years, researchers on social information access are gaining more interests on applying various of social interactions (e.g., friendship, bookmarking, tagging) for satisfying people’s information needs. In this chapter, we focus on methods and technologies to boost information retrieval performance based on the idea of representing social information as networks. We study three different types of networks: people-centric networks, document-centric networks and heterogeneous networks combining both. Information from these networks has been utilized to compute vertex similarity (at the individual level), identify network clusters (at the community level) and calculate entire network measurements (at the network level), which are further applied to help search problems not only for seeking documents but also when searching for people. This chapter provides an extensive reviews of existing methods and technologies for performing such two search topics using networks. Through this chapter, our goal is to provide readers with introductory review of the existing work, and provide concrete presentations of relevant technologies for designing and developing network-based social search systems. Finally, we also point out potential remaining challenges on this topic.

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computing and InformationUniversity of PittsburghPittsburghUSA

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