Community Analysis in Dynamic Social Networks by Tanja Falkowski

By Tanja Falkowski

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Extra resources for Community Analysis in Dynamic Social Networks

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Concerning the giant component, the authors observed an decreasing average distance between users over time. , reciprocity, transitivity, popularity, subgroup formation. Therefore, statistical models usually do not make justice to the complexity of the network structure and can only reflect a part of a complex reality. 2 Community Evolution Models The membership of actors to communities shifts over time, resulting in growing and declining communities. Cortes et al. [38] propose a data structure that captures a dynamic graph over time.

We discuss the algorithm in Chapter 3 in more detail and discuss how we apply this algorithm to detect communities as clusters that are observable over time. 3 Density-based Methods In density-based clustering, a cluster is a dense region of points that is surrounded by a region of low density. This definition of a cluster is applicable when the clusters have an irregular shape and when noise points are present. Ester et al. proposed DBSCAN, a density-based algorithm that produces a partitional clustering [50].

Studied the evolution of structures within two social networks (Flickr and Yahoo! 360◦ ) [109]. They observed the overall network properties and the evolution of “giant components” which represent a large group of people who are connected to one another. The study showed that the density of the network follows the same pattern in both networks: rapid growth, decline and then slow but steady growth. The authors assume that this behavior is due to the activities of “early adopters” who create significant linkages with other individuals which results in a rapid growth.

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