Comparison of Community Structure Partition Optimization of Complex Networks by Different Community Discovery Algorithms

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.