Community division method of multi-relation social network

Abstract

The invention belongs to the technical field of computer applications, and discloses a community division method of a multi-relation social network. The community division method of the multi-relationsocial network comprises the following steps: firstly, converting original network data into similarity tensor, and then establishing an analysis model; then using a tensor decomposition method to obtain a tensor decomposition result; and finally obtaining a community division result by using a clustering analysis method. According to the community division method, the multi-relation network dataare used for performing the community division, the division accuracy is better than the community division result obtained by just using single-relation data, and compared with the traditional method, the tensor decomposition method has the advantages of being more efficient and accurate; and compared with the existing multi-relation network community division method, data information can be fully used, and a better division result is obtained by using a mature mathematical theory method.

Description

technical field [0001] The invention belongs to the technical field of computer applications, and in particular relates to a community division method of a multi-relationship social network. Background technique [0002] In a social network, there are often various connections between people. This kind of network containing multiple social relationships is called a multi-relationship social network, which is ubiquitous in real life. In a multi-relational social network, nodes in the network often cluster, and each cluster in this cluster is called a community. In the article "Uncovering Groups via Heterogeneous Interaction Analysis" published by Tang et al., the problem of community division in multi-relational networks was studied, but the weight of each relationship was not considered in the community division. The key to community division in multi-relational social networks is how to consider the impact of each dimension relationship on community division. The present ...

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Problems solved by technology

In addition, the existing methods of using tensor decomposition to divide multi-relational social network communities have high complexity and poor performance

[0003] To sum up, the problem existing in the existing technology is that there is a lack of theoretical guidance when determining the weight of each relationship. The difference between a multi-relationship social network and a single-relationship social network is that each relationship contributes differently to the formation of a community. , analyzing the weight of each relationship is a key issue in the division of multi-relationship social network communities

The community division method of tensor decomposition using the traditional HOOI (High Order Orthogonal Iteration) method has high complexity and is not suitable for large-scale social networks

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