GED: the method for group evolution discovery in social networks

Abstract

The continuous interest in the social network area contributes to the fast development of this field. The new possibilities of obtaining and storing data facilitate deeper analysis of the entire network, extracted social groups and single individuals as well. One of the most interesting research topic is the dynamics of social groups which means analysis of group evolution over time. Having appropriate knowledge and methods for dynamic analysis, one may attempt to predict the future of the group, and then manage it properly in order to achieve or change this predicted future according to specific needs. Such ability would be a powerful tool in the hands of human resource managers, personnel recruitment, marketing, etc. The social group evolution consists of individual events and seven types of such changes have been identified in the paper: continuing, shrinking, growing, splitting, merging, dissolving and forming. To enable the analysis of group evolution a change indicator-inclusion measure was proposed. It has been used in a new method for exploring the evolution of social groups, called Group Evolution Discovery (GED). The experimental results of its use together with the comparison to two well-known algorithms in terms of accuracy, execution time, flexibility and ease of implementation are also described in the paper.

Predicting Community Evolution in Social Networks

“…The idea of using the GED method results [26] to predict group evolution was presented in [16]. The initial idea was to use a simple sequence, which consists of groups size and events between consecutive timeframes as an input for the classifier.…”

“…In [25] authors of SGCI described a tool for visualisation of group evolution based on the SGCI method. A detailed explanation of the GED method can be found in [26], and in [23] both methods of identification events in group evolution are compared.…”

“…• alpha-the GED inclusion measure of group Gi from time frame Tn in group Gj from time frame Tn+1 [26] (a measure used only in approach utilizing the GED method),…”

“…The GED method was run on the datasets with all combinations of GED parameters [26] from the set {50%, 60%, 70%, 80%, 90%, 100%}. The social position measure [32] (measure similar to weighted page rank) was utilized as the node importance measure.…”

Membership Detection Using Cooperative Data Mining Algorithms

“…Twitter [17,23], identifying groups and communities (we refer you to Tang and Huag's [27] survey for an overview of the area), modeling influence [5], understanding and modeling how information flows and spreads through a network [8,[11][12][13][14]21], modeling the evolution of graphs, communities, and groups [2,3,10,15,19], and using social networks as tools, e.g. for event detection [6,20].…”