Incremental structure clustering method and incremental structure clustering system on dynamic graph

A technology of structural clustering and dynamic graph, applied in the field of network, can solve the problem of time-consuming calculation process
CN106909948AInactive Publication Date: 2017-06-30SHENZHEN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
SHENZHEN UNIV
Publication Date
2017-06-30
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention is suitable for the field of network technology, and provides an incremental structure clustering method on a dynamic graph. The method comprises the steps of receiving an undirected and unweighted simple graph, performing initializing processing on the undirected and unweighted simple graph by means of a structure clustering algorithm of the graph, and obtaining an initialized breadth-first forest and a non-tree-side set; when insertion of a new side is detected, performing cluster combining or cluster splitting processing according to the new side and the initialized breadth-first forest and the non-tree-side set, and obtaining the breadth-first forest. Through the method provided by the embodiment, re-calculation of the whole graph is not required, dividing and clustering can be performed just through updating partial side. Compared with an existing SCAN algorithm, the incremental structure clustering method can improve updating operation speed by three orders of magnitude, thereby settling a problem of re-calculation of the whole network and time wasting in a calculation process when static graph updating occurs in prior art.
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Description

technical field

[0001] The invention belongs to the field of network technology, and in particular relates to a method and system for incremental structure clustering on a dynamic graph. Background technique

[0002] With the rapid development of information technology, graph data formed by various real networks can be seen everywhere. Examples include social networks, communication networks, and biological networks. Each network contains a corresponding community structure, and it is found that these implicit community structures are of great significance in real life and have many applications. In biological networks, a community may represent molecules with the same properties. In a social network, a community may represent a relatively close group.

[0003] Clustering of graphs is an important means of discovering these communities. In the past decade, researchers have proposed a large number of models and related algorithms for graph clustering. Among these algorit...

Claims

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