Abstract
Social network analysis has important research significance in sociology, business analysis, public security, and other fields. The traditional Louvain algorithm is a fast community detection algorithm with reliable results. The scale of complex networks is expanding larger all the time, and the efficiency of the Louvain algorithm will become lower. To improve the detection efficiency of large-scale networks, an improved Fast Louvain algorithm is proposed. The algorithm optimizes the iterative logic from the cyclic iteration to dynamic iteration, which speeds up the convergence speed and splits the local tree structure in the network. The split network is divided iteratively, then the tree structure is added to the partition results, and the results are optimized to reduce the computation. It has higher community aggregation, and the effect of community detection is improved. Through the experimental test of several groups of data, the Fast Louvain algorithm is superior to the traditional Louvain algorithm in partition effect and operation efficiency.
Highlights
Community detection is a significant research issue with respect to complex networks, and the goal of community detection is to discover the communities in networks [1]
Kim et al [10] dealt with clustering techniques applied to Twitter network and Twitter trend analysis
We improve the speed of community detection via optimization strategies. e main contributions of this paper are as follows: (1) An improved algorithm based on Louvain is proposed. e algorithm optimizes the iterative logic from the cyclic iteration to dynamic iteration, which speeds up the convergence speed
Summary
Community detection is a significant research issue with respect to complex networks, and the goal of community detection is to discover the communities in networks [1]. Our goal in this paper is to quickly detect the community structure of a large network using the Louvain algorithm. Compared with the GN algorithm, the time complexity of the Fast Newman algorithm is lower and approximately to o(m(m + n)), so it is faster in computational efficiency When it comes to large-scale community partitioning, the data to be processed are often of a huge scale such as social platform data and regional call data. The Louvain algorithm and improved algorithms based on it have been widely applied in largescale complex network partition [29,30,31,32,33,34,35,36]. Perrin et al [36] proposed a recursive method based on the Louvain algorithm for community detection and the PageRank algorithm for authoritativeness weighting in networks. For community networks with uneven nodes, excessive aggregation is easy to occur
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