Method and device for detecting large-scale social network communities

Abstract

The invention relates to a method and device for detecting large-scale social network communities. The method includes the steps that input large-scale social networks are modeled into a map G = (V, E); all nodes on the map G are sequenced in a descending order according to the size relation of node degrees through a parallel sorting algorithm, and the sum DSum of effective degrees of all the nodes on the map G is calculated; DSum / P serves as an equally-dividing benchmark reference value, and the map G is equally divided into P sub maps through a load balancing method; the P sub maps are traversed for looking for triangles on the map G through a MapReduce parallel computing model, parallel multilayer coarsening is conducted on the map G based on a triangle topological structure, and then a simplest coarsened reduction map G' is obtained; by the adoption of a community finding algorithm based on genetics, initial community finding is conducted on the simplest coarsened reduction map G', and then a community finding result is generated; the community finding result is coarsened reversely layer by layer and restored to the map G, fine adjustment and optimizing processing is conducted accordingly, and then a community structure of the map G is acquired. According to the method and device, community finding and data analysis of the large-scale social networks can be accurately and efficiently achieved.

Description

technical field [0001] The invention relates to the field of network technology, in particular to a detection method and device for a large-scale social network community. Background technique [0002] The so-called methodology changes the world outlook. The emergence of Internet high-tech has pushed people’s traditional space network social relationship to the Internet (Internet)-based virtual relationship social network, such as newsgroups, BBS (Bulletin Board System, electronic bulletin board) , Blog (Web Log, Chinese means "network log), mail network, etc. are typical instant messaging systems. With the emergence of Web2.0 technology, the manifestations of social networks have begun to diversify, interact, and open. This flexible and active interactive platform has greatly enriched the way people participate in the Internet, and its strong attraction has prompted many social networks such as Facebook, Twitter, LinkedIn, Renren, Sina Weibo, etc. to grow rapidly, showing ...

AI Technical Summary

Problems solved by technology

However, when analyzing large-scale social networks based on the above-mentioned community discovery algorithms, we face a new challenge, that is, the limitation of the physical memory capacity of the processor.

Algorithms are all based on main memory, but massive network graph data cannot be received by a processor unit memory at the same time. If the method of data block storage is adopted, its execution efficiency will occupy large system resources due to frequent I / O interactions. And increase the time complexity of the algorithm; and if sampling approximation or parallelization algorithm is adopted, the incompleteness or segmentation of its information, and the unbalanced load will lead to a sudden decline in the community discovery effect, and even deviate from the original meaning of the community structure.

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