Method and system for counting triangles in large-scale graph with low space overhead

Abstract

The invention discloses a method and system for counting triangles in a large-scale graph with low space overhead, and the method comprises the steps: obtaining a data set, constructing a directed graph according to the data set, and obtaining graph data; traversing all vertexes in the graph data and putting the vertexes into a preset Bloom sieve according to a preset rule; obtaining two vertexescorresponding to an edge according to the edge in the graph data; and judging that the two vertexes exist in the Bloom sieve, and calculating an intersection of neighbor vertex sets of the two vertexes to obtain the number of triangles corresponding to the side. The system comprises a graph data module, an input module, a vertex module and an intersection module. By using the method and the system, the number of triangles can be quickly calculated from large-scale data under the condition of ensuring low space overhead. The method and the system for counting the triangles in the large-scale graph with low space overhead can be widely applied to the field of large-scale data processing.

Description

technical field [0001] The invention relates to the field of large-scale data processing, in particular to a method and system for counting triangles in a large-scale graph with low space overhead. Background technique [0002] As we use the Internet more and more times in our lives, the increase rate and total amount of various types of data are also increasing day by day. It is even difficult to describe the increase and growth rate of data with words such as "exponential growth" and "massive". However, the speed at which people analyze and process data has failed to keep up with the scale and speed of data growth. The information and value hidden in large-scale data are only the tip of the iceberg that we have discovered and utilized. Therefore, in recent years, the research direction and trend of the Internet is towards how to analyze and process large-scale data. As one of the common and important data structures, graph data can better express the connection between in...

AI Technical Summary

Problems solved by technology

However, for various large-scale sparse graphs in the real world, such as social networks, traffic road networks, etc., there are often too many space overheads (for example, single-machine methods cannot handle large-scale graphs) or excessive time overhead (such as random accessing memory or communicating too frequently in a distributed approach) etc.

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