35,625 research outputs found
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
Exploring cooperative game mechanisms of scientific coauthorship networks
Scientific coauthorship, generated by collaborations and competitions among
researchers, reflects effective organizations of human resources. Researchers,
their expected benefits through collaborations, and their cooperative costs
constitute the elements of a game. Hence we propose a cooperative game model to
explore the evolution mechanisms of scientific coauthorship networks. The model
generates geometric hypergraphs, where the costs are modelled by space
distances, and the benefits are expressed by node reputations, i. e. geometric
zones that depend on node position in space and time. Modelled cooperative
strategies conditioned on positive benefit-minus-cost reflect the spatial
reciprocity principle in collaborations, and generate high clustering and
degree assortativity, two typical features of coauthorship networks. Modelled
reputations generate the generalized Poisson parts and fat tails appeared in
specific distributions of empirical data, e. g. paper team size distribution.
The combined effect of modelled costs and reputations reproduces the
transitions emerged in degree distribution, in the correlation between degree
and local clustering coefficient, etc. The model provides an example of how
individual strategies induce network complexity, as well as an application of
game theory to social affiliation networks
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