Coauthorship network dividing method for selecting seed node

Abstract

The invention provides a coauthorship network dividing method for selecting a seed node according to academic association centrality, relates to the fields of academic author collaborated writing behavior analysis and association dividing, and particularly to a community dividing algorithm which can display a community structure in a complicated coauthorship network. The method comprises the following steps of firstly, according to different attributes of an article which is issued by authors, calculating a collaboration strength by means of a Salton method and constructing the coauthorship network; secondly, considering self impact degrees of the authors and influences of adjacent scholars to the authors, and calculating the academic association centrality of the node; and finally, using the academic association centrality of the node as a seed node selection basis, combining the academic association centrality into a second period of a Louvain community dividing algorithm, namely preventing merging between the seed nodes, and merging the non-seed nodes into the community to which the seed node belongs as possible. The coauthorship network dividing method settles problems of excessive merging between middle-sized communities and large communities and a large number of small communities, and can more accurately find out and divided the academic association in the coauthorship network.

Description

technical field [0001] The invention relates to the fields of academic author authority and community division, and specifically relates to a community division algorithm capable of displaying the community structure in complex co-authorship networks. Background technique [0002] Since 1994, since the emergence of large digital libraries such as IEEE and ACM, some scholars at home and abroad have begun to pay attention to and study the co-authorship network. The main reason for this is that observing the current state and structure of coauthorship networks can provide a lot of valuable data. Coauthorship network is a typical social complex network, which has some structural characteristics, such as small-world characteristics, cohesion tendency in the network, and scale-free characteristics. Therefore, some tools and methods for analyzing complex social networks can also be applied to co-authorship networks to dig out useful information hidden in co-authorship networks. T...

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Problems solved by technology

However, this patent can only analyze the community structure of complex networks in a general way, and does not accurately select nodes containing important community structure information for different types of complex networks for division, and the final division results cannot accurately reflect social behaviors in different types of social networks

[0005] The existing methods for community division in co-authorship networks have the following disadvantages: (1) There is no in-depth community division research combined with the characteristics of co-authorship networks; (2) The algorithm is inefficient and takes a long time to run

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