Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership

Abstract

Abstract-Detecting community structure in social networks is a fundamental problem empowering us to identify groups of actors with similar interests. There have been extensive works focusing on finding communities in static networks, however, in reality, due to dynamic nature of social networks, they are evolving continuously. Ignoring the dynamic aspect of social networks, neither allows us to capture evolutionary behavior of the network nor to predict the future status of individuals. Aside from being dynamic, another significant characteristic of realworld social networks is the presence of leaders, i.e. nodes with high degree centrality having a high attraction to absorb other members and hence to form a local community. In this paper, we devised an efficient method to incrementally detect communities in highly dynamic social networks using the intuitive idea of importance and persistence of community leaders over time. Our proposed method is able to find new communities based on the previous structure of the network without recomputing them from scratch. This unique feature, enables us to efficiently detect and track communities over time rapidly. Experimental results on the synthetic and real-world social networks demonstrate that our method is both effective and efficient in discovering communities in dynamic social networks.

Cooperative Attention-Based Learning between Diverse Data Sources

“…However, how can the interaction information be advanced to create a populous cooperation structure? Accounting for the possible interactions between individual users of a network, emphasizing selective neighbor interactions, can lead to natural reward and cooperation, in line with the game theory model [9,10]. This conditional reward, in accordance with the game theory model, encourages interactions that produce a clustering strategy, known as local interactions with non-random cooperation.…”

“…When individuals collaborate to support each other, they both save and spend money in the process. Cooperation is a vital component of any human community network [1][2][3][4][9][10][11][12][13]. Accumulating evidence suggests that people are impacted by their social connections, spreading emotions, beliefs, and behaviors throughout their networks [15][16][17][18][19][20][21][22][23].…”

Advanced Community Identification Model for Social Networks

“…In this regard, it is not a suitable approach. Similarly, Javadi et al [33] introduced a local community detection method that is based on the leadership concept. In this study, the leaders were identified over a certain time in the first step.…”

The rhythm of the crowd: Properties of evolutionary community detection algorithms for mobile edge selection

“…Evaluation metrics for dynamic graphs must necessarily take into account also the temporal aspect other than the simple distance between nodes. Several variations of the metrics in use for static graphs (such as Modularity, Conductance and Expansion) have been adapted to evaluate the goodness of the clusters in dynamic graphs [47]. Most of such techniques consider the measure that characterize the cluster at the next snapshot with respect to its variation to the current one, providing effective solutions to test the community discovery technique in use.…”