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Difference between revisions of "Networks Community Detection"

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(Objectives)
(Introduction)
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=Introduction=
 
=Introduction=
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Algorithms for finding communities have many uses in network science, particularly in the social sciences.  Since collective action is such a fundamental aspect of social and political behavior, identifying the groups to which individuals belong is an important task.  Here we discuss a variety of methods for approaching this problem.
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From the outset, we are faced with the problem of articulating a good, quantitative definition of what a community is.  Since the network primarily encodes the structure of the community, this is where we focus our attention.  ''Modularity'', discussed in greater detail next, is the operational definition of what a good community is and, therefore, serves as the basis for nearly all community detection algorithms.
  
 
=Graph Partitioning=
 
=Graph Partitioning=

Revision as of 11:32, 8 July 2011


Objectives

  • Understand the difference between graph partitioning and community detection
  • Survey the different methods available for community detection

Introduction

Algorithms for finding communities have many uses in network science, particularly in the social sciences. Since collective action is such a fundamental aspect of social and political behavior, identifying the groups to which individuals belong is an important task. Here we discuss a variety of methods for approaching this problem.

From the outset, we are faced with the problem of articulating a good, quantitative definition of what a community is. Since the network primarily encodes the structure of the community, this is where we focus our attention. Modularity, discussed in greater detail next, is the operational definition of what a good community is and, therefore, serves as the basis for nearly all community detection algorithms.

Graph Partitioning

Label Propagation

Edge Betweenness

Conclusion

References

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Discussion questions

Problems

Glossary

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