Graph centrality calculating method and device

Abstract

The invention discloses a graph centrality calculating method and device. The method includes sampling an original graph indicative of a network structure and acquiring nodes of part in sequential connection in the original graph and at least two sampling slave graphs composed of connection edges between the nodes; calculating the graph centrality separately for the sampling slave graphs and forming graph centrality calculating results of the corresponding sampling slave graphs; mapping the calculation results of the sampling slave graphs into the original graph and acquiring a mapping resultof sampling nodes in the original graph and a mapping result of non-sampling nodes in the original graph; clustering the mapping results corresponding to the sampling slave graphs and acquiring the centrality calculation result of the original graph. By applying the method provided by the invention, precision and efficiency of centrality calculation of the original graph specific to a network canbe improved.

Description

technical field [0001] The invention relates to graph theory technology, in particular to a graph centrality calculation method and device. Background technique [0002] With the rapid development of the Internet, it is increasingly important to analyze the network, especially the online social network, to provide more valuable services. In particular, the method of graph computing on the network has become a widely used network analysis method. [0003] As an important goal of graph calculation for a network, the calculation of Graph Centrality refers to finding the most important nodes in a graph corresponding to a network, and the calculated nodes are different according to different networks. For example: [0004] Graph centrality can refer to some of the most influential users in an online social network (OSN, Online Social Network), or some of the most important sites in an urban transportation network, or the most influential users in an online store (Online Store). ...

AI Technical Summary

Problems solved by technology

[0005] The scale of nodes in the network is constantly expanding. Taking online social networks as an example, the number of users is constantly increasing, and the scale of the corresponding corresponding graph is also increasing. For example, the graph may include millions of nodes and hundreds of millions of nodes. The edges (associated lines between nodes), which will undoubtedly lead to the calculation of the centrality of the graph will become increasingly difficult:

[0006] On the one hand, computing graph centrality will lead to very large computing resource overhead, and even high cost due to excessive computing resources, making it difficult to implement;

[0007] On the other hand, due to the lengthy process of calculating the centrality of the graph, due to the very large time overhead, it affects the timeliness of the calculation results of the centrality of the dependent graph

[0008] Related technologies have no effective solution to save the computing resource overhead of calculating graph centrality and improve the timeliness of graph centrality calculation

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