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The main subject studied in this dissertation is a multi-layered socialnetwork (MSN) and its analysis. One of the crucial problems in multi-layeredsocial network analysis is community extraction. To cope with this problem theCLECC measure (Cross Layered Edge Clustering Coefficient) was proposed in thethesis. It is an edge measure which expresses how much the neighbors of twogiven users are similar each other. Based on this measure the CLECC algorithmfor community extraction in the multi-layered social networks was designed. Thealgorithm was tested on the real single-layered social networks (SSN) andmulti-layered social networks (MSN), as well as on benchmark networks from GNBenchmark (SSN), LFR Benchmark (SSN) and mLFR Benchmark (MSN) a specialextension of LFR Benchmark, designed as a part of this thesis, which is able toproduce multi-layered benchmark networks. The second research problemconsidered in the thesis was group evolution discovery. Studies on this problemhave led to the development of the inclusion measure and the Group EvolutionDiscovery (GED) method, which is designed to identify events between two groupsin successive time frames in the social network. The method was tested on areal social network and compared with two well-known algorithms regardingaccuracy, execution time, flexibility and ease of implementation. Finally, anew approach to prediction of group evolution in the social network wasdeveloped. The new approach involves usage of the outputs of the GED method. Itis shown, that using even a simple sequence, which consists of severalpreceding groups sizes and events, as an input for the classifier, the learnedmodel can produce very good results also for simple classifiers.

A Method for Group Extraction and Analysis in Multilayer Social Networks