Community discovery method based on label propagation in random walk

Abstract

The invention requests to protect a community discovery algorithm based on label propagation in random walk. The community discovery algorithm comprises the following steps: 1), establishing a transfer matrix: generating an adjacent matrix according to a network topological graph, and calculating the transition probability from a node to other nodes through the combination of the degree of the node with the adjacent matrix so as to finally obtain the transfer matrix; 2), calculating the weighting value of the node: defining the location probability distribution of a walker, and calculating the location probability distribution of the walker after the walker walks t steps and reaches a stable state according to the transfer matrix, wherein at the moment, the value in the location probability distribution is the weighting value of the node; 3), setting the threshold of a central node: completing the screening of the central node according to the descending sequence of node weighting so as to initially generate a sub community; 4), initializing a node label: performing label propagation from the central node, enabling node labels which are mutually and more tightly connected to become coincident gradually, and enabling the nodes holding the same label to form a community. Through the adoption of the community discovery algorithm disclosed by the invention, the problem of the randomness of updating node label sequences in the classic label propagation algorithm is solved, so that the precision of the algorithm is effectively improved.

Description

technical field [0001] The invention relates to the field of communication technology, and relates to a label propagation community discovery algorithm based on a random walk model. Background technique [0002] Communities exist widely in systems with network structures, ranging from biology, computer science, engineering, economics, and politics. For example, in the protein-protein interaction network, the community is the group of proteins with the same specific function in the cell; in the World Wide Web, the community is those web pages with the same or related topics; in the metabolic network, the community is the functional group of those circulations and pathways . [0003] There are some group organizations such as family, work or circle of friends, villages, countries, etc. widely in society. With the rise of the Internet and the application of social media in recent years, geographical restrictions among community members have been gradually eliminated, resulting...

AI Technical Summary

Problems solved by technology

However, the algorithm has high randomness: the labels of nodes in the network have the same weight, so there will be multiple optimal labels in the process of community discovery, and the algorithm just randomly selects one of the labels; Traverse the nodes and update the labels, resulting in the results found by the community not converging, and the same results cannot be obtained after repeated experiments

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