Community division method in complex network

Abstract

The invention provides a community classification method in complex network, a plurality of different nodes having partial influences are taken as the cores, the influences of the nodes are caused to evenly diffuse from the core outwards layer by layer, finally the node having the greatest influence becomes the core, the influences of the nodes in layer-by-layer expansion continuously attenuate, the interconnection of the nodes form a local region which expands until the method stops, the influences of the nodes are slight and can reach the edge of the network of the local region. For a large-scale unordered complex network, the positions of the nodes having different importance degrees can be rapidly located, a great deal of more fine-granularity information is dug out, simultaneously the original structural character of the network is kept unchanged, and the original large-scale complex network is simplified and downsized, so that not only the efficiency of search can be improved, but also the structure of the large-scale network from macroscopic view can be more clearly analyzed.

Description

technical field [0001] The invention belongs to the field of data mining, relates to a community division method, and specifically designs a community division method in a complex network. Background technique [0002] Since the 1990s, the rapid development of information technology represented by the Internet has brought human society into the Internet age. Complex networks are ubiquitous, in the real world, from the Internet World Wide Web, from urban road networks to aviation roadmaps, from VLSI to large-scale power grids, from cellular neural networks to protein interaction networks; complex networks can also be used to Describe the social relationship between people, the cooperative relationship between scientists, the citation relationship between papers, the predatory relationship between species in the biological world; even the semantic relationship between words in the text, etc., can be viewed It can be said that people already live in a world full of various com...

AI Technical Summary

Problems solved by technology

[0020] 1. Since the betweenness of the entire network must be analyzed for each calculation, the time complexity of the algorithm is high (O(mn)), and n and m are the number of nodes and edges of the network respectively

[0021] 2. Decompose the network into nodes through a dendrogram, forcing any node to belong to a community, without considering whether it is really meaningful

[0031] 1. For a large-scale network, it is necessary to introduce various node similarity metrics according to different scenarios, such as the number of paths between nodes in structured equivalence and hierarchical clustering, etc., to calculate the distance between any two nodes in the network. The tightness of the connection, the algorithm efficiency is relatively low, and the complexity is relatively large;

[0032] 2. It is not guaranteed to obtain the optimal network division result. Since it is necessary to manually specify the expected number of communities or provide a critical threshold for algorithm termination, there is no guarantee that the obtained division is an optimal network division result, and the actual In the application, since the user cannot predict the size of the community, it is usually necessary to divide the network with a variety of partition schemes of different sizes, and then select the best partition scheme by introducing some optimization principles, and the complexity of the algorithm is relatively high

[0033] 3. In all community division methods, the division result of each node can only belong to a single community, which is a kind of hard division, which is different from the network structure and node position in the real world to some extent , for example, there are node members in the real network who can belong to different communities, and it can be found from different perspectives that the members take on different roles and tasks in the network topology connected by different communities, while the traditional algorithm can only perform precise division. For the real network, a lot of important information is lost

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