34,979 research outputs found
Community detection and graph partitioning
Many methods have been proposed for community detection in networks. Some of
the most promising are methods based on statistical inference, which rest on
solid mathematical foundations and return excellent results in practice. In
this paper we show that two of the most widely used inference methods can be
mapped directly onto versions of the standard minimum-cut graph partitioning
problem, which allows us to apply any of the many well-understood partitioning
algorithms to the solution of community detection problems. We illustrate the
approach by adapting the Laplacian spectral partitioning method to perform
community inference, testing the resulting algorithm on a range of examples,
including computer-generated and real-world networks. Both the quality of the
results and the running time rival the best previous methods.Comment: 5 pages, 2 figure
An information-theoretic framework for resolving community structure in complex networks
To understand the structure of a large-scale biological, social, or
technological network, it can be helpful to decompose the network into smaller
subunits or modules. In this article, we develop an information-theoretic
foundation for the concept of modularity in networks. We identify the modules
of which the network is composed by finding an optimal compression of its
topology, capitalizing on regularities in its structure. We explain the
advantages of this approach and illustrate them by partitioning a number of
real-world and model networks.Comment: 5 pages, 4 figure
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
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
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