Improved community mining method based on LFM and EAGLE
2016
Community structures are crucial topological characteristic of complex
networks. Consequently, network community structure mining has great
significance to the real world. Complex networks have both hierarchy and
overlaps, but it is still a problem to excavate the hierarchy and overlaps
of networks efficiently and accurately at the same time by algorithm. This
paper proposes an efficient and functional complex network community
partition algorithm by combining fitness function optimization and community
similarity, which can uncover both overlapping and hierarchical community
structure of complex networks. Its basic idea is to use fitness function
optimization at the bottom of hierarchy division to identify efficiently and
accurately the underlying community structure which is with overlaps.
Hierarchical structure is based on the community similarity to merge the
underlying sub-communities with the principle of maximum similarity
circulation. The experimental results utilizing Karate Club Network and US
college football network show that the proposed algorithm is a manageable
and accurate method for not only discovering the gradation community
structure, but also overlap between excavated club.
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