CC-GA: A clustering coefficient based genetic algorithm for detecting communities in social networks

Proactive Caching at the Edge Leveraging Influential User Detection in Cellular D2D Networks

“…Various kinds of community detection algorithms have been proposed. In this paper, we use the CC-GA algorithm [18]. The main reason for using CC-GA is its promising results when compared to other state-of-the-art algorithms such as the information theoretic algorithm (Infomap) [38] and the label prorogation algorithm (LPA) [39] on various kinds of networks including power-law and Forest fire models.…”

“…CC gives a better population for a genetic algorithm and results in better clustering of the network. Additionally, the algorithm using CC has shown that it converges very quickly [18]. In the following equation, C v i represents the value of clustering coefficient of node v i :…”

“…In the end, the algorithm returns the community structure of the input network. For more details about this algorithm, readers are referred to [18]. The interested readers are referred to [41,42] for more about genetic algorithms.…”

“…Initial parameters for the genetic algorithm are initialized with the values shown in Table 2a. Typically, for network clustering problems, GA parameters like population size, termination criteria and mutation probability are set as 200, 50, and 0.15, respectively [18,47,48]. However, we performed experiments with different parameter settings to select values that perform best in our case.…”

“…Initially, we use a preferential attachment model [17] for representing the mobile cellular network. We then perform clustering using a CC-GA algorithm [18], and find influential users using an eigenvector centrality measure [16]. Finally, based on the users' previous history, we proactively cache the content of the influential users with respect to the available cache size.…”