Abstract
In reality, complex problems can be transformed into complex networks. Through the community partition of complex networks, the relationship between nodes can be found more clearly. This paper briefly introduced three algorithms for community structure partition of complex networks, which were based on the similarity of common neighbor nodes, ant colony algorithm and density peak clustering, and compared the performance of the three algorithms by using six artificial networks whose chaotic factors gradually increased and two real networks in MATLAB software. The results suggested that the increase of chaotic factors in the artificial network reduced the normalized mutual information (NMI) of the partition results calculated by the three algorithms, but the NMI of the algorithm based on density peak clustering in the same artificial network was the highest, the next was the algorithm based on ant colony algorithm, and the lowest was the algorithm based on the similarity of common neighbor nodes; for the real network, the modularity of the algorithm based on density peak clustering was the highest, the algorithm based on ant colony algorithm was the second, and the algorithm based on the similarity of common neighbor nodes was the lowest. In conclusion, the more fuzzy the community structure is in the complex network, the lower the performance of the partition algorithm is, and the algorithm based on density peak clustering has the best performance.
Highlights
Real life is composed of many complex problems
This paper briefly introduces three algorithms for the community structure division of complex networks, which were based on the similarity of common neighbor nodes, ant colony algorithm and density peak clustering algorithm, and compared the performance of the three algorithms by using six artificial
The artificial network LFR1~LFR6 improved the blend factor mu gradually, the network community structure became fuzzy gradually, and the normalized mutual information (NMI) of the three algorithms for community structure partition gradually reduced; in the process of mu increase, under the same artificial network, the community partition algorithm based on density peak clustering had the largest NMI, followed by the community partition algorithm based on the ant colony algorithm and the community partition algorithm based on the similarity of common neighbor nodes
Summary
Real life is composed of many complex problems. Studying the laws that govern them can help solve them, which can in turn promote social development [1]. Users with different identities will gradually focus on the same or similar interests, forming a community structure [3]. Genes with similar functions can be summarized, so as to mine the effective information of genes. Zuo et al [5] detected the similar energy behavior nodes in the sensor node network using complex network community division algorithm and selected the cluster center and hop nodes using the immune response principle, so as to realize the energy-saving topology of the sensor network. This paper briefly introduces three algorithms for the community structure division of complex networks, which were based on the similarity of common neighbor nodes, ant colony algorithm and density peak clustering algorithm, and compared the performance of the three algorithms by using six artificial. Networks whose chaotic factors gradually increased and two real networks in MATLAB software
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