Multi-Relational Characterization of Dynamic Social Network Communities [chapter]

Yu-Ru Lin, Hari Sundaram, Aisling Kelliher
2010 Handbook of Social Network Technologies and Applications  
2 Being influenced by this theory, we believe that semantics is an emergent artifact of human activity that evolves over time. Human activity is mostly social, and the social networks of human are conceivable loci for the construction of meaning. Hence, it is crucial to identify real human networks as communities of people interacting with each other through meaningful social activities, and producing stable associations between concepts and artifacts in a coherent manner. Motivating
more » ... s. The discovery of human communities is not only philosophically interesting, but also has practical implications. As new concepts emerge and evolve around real human networks, community discovery can result in new knowledge and provoke advancements in information search and decisionmaking. Example applications include:  Context-sensitive information search and recommendation: The discovered community around an information seeker can provide context (including objects, activities, time) that help identify most relevant information. For example, when a user is looking at a particular photo, the community structure may be used to identify peers or objects likely co-occurring with the photo.  Content organization, tracking and monitoring: The rapid growth of content on social media sites creates several challenges. First, the content in a photo stream (either for a user or a community) is typically organized using a temporal order, making the exploration and browsing of content cumbersome. Second, sites including Flickr provide frequency based aggregate statistics including popular tags, top contributors. These aggregates do not reveal the rich temporal dynamics of community sharing and interaction. Community structure may be used to reflect the social sharing practice and facilitate the organization, tracking and monitoring of user-generated social media content.  Behavioral prediction: Studies have shown that individual behaviors usually result from mechanisms depending on their social networks, e.g. social ebeddedness [17] and influence [13] . Community structure that accounts for inherent dependencies between individuals embedded in a social network can help understand and predict the behavioral dynamics of individuals.
doi:10.1007/978-1-4419-7142-5_18 fatcat:4hj5h3z6y5a2jc2rxiocfqcsb4