An Introduction to Community Detection in Multi-layered Social Network

Social communities extraction and their dynamics are one of the most important problems in today's social network analysis. During last few years, many researchers have proposed their own methods for group discovery in social networks. However, almost none of them have noticed that modern social networks are much more complex than few years ago. Due to vast amount of different data about various user activities available in IT systems, it is possible to distinguish the new class of social networks called multi-layered social network. For that reason, the new approach to community detection in the multi-layered social network, which utilizes multi-layered edge clustering coefficient is proposed in the paper.Comment: M.D. Lytras et al. (Eds.): WSKS 2011, CCIS 278, pp. 185-190, 201

Graphical Analysis of Social Group Dynamics

Identifying communities in social networks becomes an increasingly important research problem. Several methods for identifying such groups have been developed, however, qualitative analysis (taking into account the scale of the problem) still poses serious problems. This paper describes a tool for facilitating such an analysis, allowing to visualize the dynamics and supporting localization of different events (such as creation or merging of groups). In the final part of the paper, the experimental results performed using the benchmark data (Enron emails) provide an insight into usefulness of the proposed tool.Comment: Fourth International Conference on Computational Aspects of Social Networks, CASoN 2012, Sao Carlos, Brazil, November 21-23, 2012, pp. 41-46; IEEE Computer Society, 201

Influence of the Dynamic Social Network Timeframe Type and Size on the Group Evolution Discovery

New technologies allow to store vast amount of data about users interaction. From those data the social network can be created. Additionally, because usually also time and dates of this activities are stored, the dynamic of such network can be analysed by splitting it into many timeframes representing the state of the network during specific period of time. One of the most interesting issue is group evolution over time. To track group evolution the GED method can be used. However, choice of the timeframe type and length might have great influence on the method results. Therefore, in this paper, the influence of timeframe type as well as timeframe length on the GED method results is extensively analysed.Comment: The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE Computer Society, 2012, pp. 678-68

Analysis of group evolution prediction in complex networks

In the world, in which acceptance and the identification with social communities are highly desired, the ability to predict evolution of groups over time appears to be a vital but very complex research problem. Therefore, we propose a new, adaptable, generic and mutli-stage method for Group Evolution Prediction (GEP) in complex networks, that facilitates reasoning about the future states of the recently discovered groups. The precise GEP modularity enabled us to carry out extensive and versatile empirical studies on many real-world complex / social networks to analyze the impact of numerous setups and parameters like time window type and size, group detection method, evolution chain length, prediction models, etc. Additionally, many new predictive features reflecting the group state at a given time have been identified and tested. Some other research problems like enriching learning evolution chains with external data have been analyzed as well