Dynamic Social Network Analysis Using Latent Space Model and an Integrated Clustering Algorithm

Optimal change point detection and localization in sparse dynamic networks

“…For instance, Barabási and Albert (1999) allowed for time-varying nodes and edges, Crane (2015) assumed infinite population at every time point and allowed for random observations at different time points, to name but a few. In terms of the network models imposed for every time point, Snijders (2002) explored dynamic exponential random graph models, Tang et al (2013) studied a dynamic version of random dot product models, Ho et al (2011) extended the mixed membership models to a dynamic one, Xu and Zheng (2009), Sewell and Chen (2015) among others considered dynamic latent space models, and dynamic stochastic block models have also been extensively studied.…”

Discovery of Really Popular Friends from Social Networks

“…This explains why, over the past few years, many research works on social computing [23], [28] have been proposed. These works include the analysis [2], [18], [29], mining [13], [15], [22], [30] and visualization [3] of, as well as information propagation [8], [17] in, complex social networks (e.g., communication, friendship, professional, and organizational networks). In general, social networks [6], [7], [12] are structures made of social entities (e.g., individuals, corporations, collective social units, or organizations) that are linked by some specific types of interdependency (e.g., kinship, friendship, common interest, beliefs, or financial exchange).…”

Towards Contextualizing Community Detection in Dynamic Social Networks

“…Social network can be generally defined as a group of individuals who are connected by a set of relationships [7]. For example, we can consider a research laboratory, illustrated by Fig.…”

“…Furthermore, a key characteristic of social networks is their continual change [7]. Real-world social networks such as the research laboratory are not always static.…”