283,338 research outputs found
Identification of Group Changes in Blogosphere
The paper addresses a problem of change identification in social group
evolution. A new SGCI method for discovering of stable groups was proposed and
compared with existing GED method. The experimental studies on a Polish
blogosphere service revealed that both methods are able to identify similar
evolution events even though both use different concepts. Some differences were
demonstrated as wellComment: The 2012 IEEE/ACM International Conference on Advances in Social
Networks Analysis and Mining, IEEE Computer Society, 2012, pp. 1233-123
Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership
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 real-world 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
Searching for Communities in Bipartite Networks
Bipartite networks are a useful tool for representing and investigating
interaction networks. We consider methods for identifying communities in
bipartite networks. Intuitive notions of network community groups are made
explicit using Newman's modularity measure. A specialized version of the
modularity, adapted to be appropriate for bipartite networks, is presented; a
corresponding algorithm is described for identifying community groups through
maximizing this measure. The algorithm is applied to networks derived from the
EU Framework Programs on Research and Technological Development. Community
groups identified are compared using information-theoretic methods.Comment: 12 pages, 4 figures, to appear in "Proceedings of the 5th Jagna
International Workshop: Stochastic and Quantum Dynamics of Biomolecular
Systems," C. C. Bernido and M. V. Carpio-Bernido, editors. A version with
full-quality figures and larger file size is available at
http://ccm.uma.pt/publications/Barber-Faria-Streit-Strogan-2008.pd
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
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