271,636 research outputs found
Consensus clustering in complex networks
The community structure of complex networks reveals both their organization
and hidden relationships among their constituents. Most community detection
methods currently available are not deterministic, and their results typically
depend on the specific random seeds, initial conditions and tie-break rules
adopted for their execution. Consensus clustering is used in data analysis to
generate stable results out of a set of partitions delivered by stochastic
methods. Here we show that consensus clustering can be combined with any
existing method in a self-consistent way, enhancing considerably both the
stability and the accuracy of the resulting partitions. This framework is also
particularly suitable to monitor the evolution of community structure in
temporal networks. An application of consensus clustering to a large citation
network of physics papers demonstrates its capability to keep track of the
birth, death and diversification of topics.Comment: 11 pages, 12 figures. Published in Scientific Report
Community structure detection in the evolution of the United States airport network
This is the post-print version of the Article. Copyright © 2013 World Scientific PublishingThis paper investigates community structure in the US Airport Network as it evolved from 1990 to 2010 by looking at six bi-monthly intervals in 1990, 2000 and 2010, using data obtained from the Bureau of Transportation Statistics of the US Department of Transport. The data contained monthly records of origin-destination pairs of domestic airports and the number of passengers carried. The topological properties and the volume of people traveling are both studied in detail, revealing high heterogeneity in space and time. A recently developed community structure detection method, accounting for the spatial nature of these networks, is applied and reveals a picture of the communities within. The patterns of communities plotted for each bi-monthly interval reveal some interesting seasonal variations of passenger flows and airport clusters that do not occupy a single US region. The long-term evolution of the network between those years is explored and found to have consistently improved its stability. The more recent structure of the network (2010) is compared with migration patterns among the four US macro-regions (West, Midwest, Northeast and South) in order to identify possible relationships and the results highlight a clear overlap between US domestic air travel and migration
Evolution of Communities with Focus on Stability
Community detection is an important tool for analyzing the social graph of
mobile phone users. The problem of finding communities in static graphs has
been widely studied. However, since mobile social networks evolve over time,
static graph algorithms are not sufficient. To be useful in practice (e.g. when
used by a telecom analyst), the stability of the partitions becomes critical.
We tackle this particular use case in this paper: tracking evolution of
communities in dynamic scenarios with focus on stability. We propose two
modifications to a widely used static community detection algorithm: we
introduce fixed nodes and preferential attachment to pre-existing communities.
We then describe experiments to study the stability and quality of the
resulting partitions on real-world social networks, represented by monthly call
graphs for millions of subscribers.Comment: AST at 42nd JAIIO, September 16-20, 2013, Cordoba, Argentina. arXiv
admin note: substantial text overlap with arXiv:1311.550
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