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Semantic Similarity of Node Profiles in Social Networks

Rawashdeh, Ahmad
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439279922

Abstract Details

2015, PhD, University of Cincinnati, Engineering and Applied Science: Computer Science and Engineering.
It can be said, without exaggeration, that social networks have taken a large segment of population by a storm. Regardless of the actual geographical location, of socio-economic status, as long as access to an internet connected computer is available, a person has access to the whole world, and to a multitude of social networks. By being able to share, comment, and post on various social networks sites, a user of social networks becomes a "citizen of the world", ensuring presence across boundaries (be they geographic, or socio-economic boundaries). At the same time social networks have brought forward many issues interesting from computing point of view. One of these issue is that of evaluating similarity between nodes/profiles in a social network. Such evaluation is not only interesting, but important, as the similarity underlies the formation of communities (in real life or on the web), of acquisition of friends (in real life and on the web). In this thesis, several methods for finding similarity, including semantic similarity, are investigated, and a new approach, Wordnet-Cosine similarity is proposed. The Wordnet-Cosine similarity (and associated distance measure) combines both a lexical database, Wordnet, with Cosine similarity (from information retrieval) to find possible similar profiles in a network. In order to assess the performance of Wordnet-Cosine similarity measure, two experiments have been conducted. The first experiment illustrates the use for Wordnet-Cosine similarity in community formation. Communities are considered to be clusters of profiles. The results of using Wordnet-Cosine are compared with those using four other similarity measures (also described in this thesis). In the second set of experiments, Wordnet-Cosine was applied to the problem of link prediction. Its performance of predicting links in a random social graph was compared with a random link predictor and was found to achieve better accuracy.
Anca Ralescu, Ph.D. (Committee Chair)
Irene Diaz, Ph.D. (Committee Member)
Rehab M. Duwairi, Ph.D. (Committee Member)
Kenneth Berman, Ph.D. (Committee Member)
Chia Han, Ph.D. (Committee Member)
Dan Ralescu, Ph.D. (Committee Member)
119 p.
Computer Science
Social Networks; Wordnet; Semantic; Machine Learning; Link Prediction

Recommended Citations

Citations

  • Rawashdeh, A. (2015). Semantic Similarity of Node Profiles in Social Networks [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439279922

    APA Style (7th edition)

  • Rawashdeh, Ahmad. Semantic Similarity of Node Profiles in Social Networks. 2015. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439279922.

    MLA Style (8th edition)

  • Rawashdeh, Ahmad. "Semantic Similarity of Node Profiles in Social Networks." Doctoral dissertation, University of Cincinnati, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439279922

    Chicago Manual of Style (17th edition)

ucin1439279922
1,131
© 2015, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.