Distributed community evolution method for dynamic time sequence communication network

Abstract

The invention relates to a distributed community evolution method for a dynamic time sequence communication network, which comprises the following steps of: calculating and updating the similarity between nodes, and adding a time label to the similarity; enabling each node to share a local similarity view, and updating the local similarity view of each node according to the time label; enabling each node to share a community ID visual field, and updating a community fusion threshold value according to the number of nodes in the same community; checking update request of the neighbor node by each node, updateing the own community ID according to the time label and the priority of the update request, and copying the update request of the neighbor node; selecting whether to perform community fusion or community splitting according to the time label and the priority of the update request by each node; According to the method, a community evolution method is simplified into two steps of community fusion and community splitting, and a distributed algorithm is adopted, so that each node can judge whether the node needs to be updated or not only by utilizing local information, and therefore, the calculation amount is reduced, and the method can adapt to a communication network with a continuously changing topological structure.

Description

technical field [0001] The invention relates to the field of complex network community detection, in particular to a distributed community evolution method for dynamic time series communication networks. Background technique [0002] In a traditional communication network, in order to realize communication between nodes, it is first necessary to establish a complete communication path between two nodes. The path usually includes multiple intermediate nodes for storing and forwarding information. However, in networks such as mobile vehicular networks, interstellar networks, and space communication networks, due to high-speed movement of nodes, alternate activity, and attacks, the topology of the communication network changes rapidly, resulting in the inability to establish a stable end-to-end network between nodes. connect. In view of the characteristics of this type of network, there are a large number of routing algorithms in the prior art to realize information transmiss...

AI Technical Summary

Problems solved by technology

Moreover, the connectivity in the same community is based on time changes and cannot always be used as a static graph to design routing

Generally speaking, the community detection algorithm in the prior art has a large amount of computation and cannot be applied to a rapidly changing network

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