Community Detection in Multidimensional Social Networks
Date
2014
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Haverford College. Department of Computer Science
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Thesis
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Award
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eng
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Haverford users only
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Abstract
Information about interactions between human actors, and the attributes about the actors in the networks, has become increasingly abundant in computer systems over the last decade. Multidimensional social networks are an increasingly common representation of interactions in markets, political networks, social networking sites, etc. The problem of detecting communities based off of this information is one that is of emerging interest in a variety of fields. Traditional clustering techniques, however, are not suited for dealing with the hybrid network of attribute information and structural relationships. Algorithms dealing with the extraction of communities that are based on multidimensional relationships are the focus of this paper. The topic of multidimensional community detection has many applications. One such application is personalizing the web, since many web services are using less sophisticated models on high value, high dimension data. There are also implications for improving research in other fields, especially Sociology and Social Movement Theory. Generally, from social media to advertising, these methods can lead to a more connected world, with more information passing, and could allow people to connect in dimensions of similarity that aren’t their most obvious feature (e.g. internet communities not being limited by geolocation).