Graph computation method and engine

Abstract

The invention discloses a graph computation method and engine. The computation method includes the steps of (A) retrieving original relation data of a graph, and obtaining retrieval data corresponding to the vertexes and the edges of the graph, (B) selecting one or more vertexes of the graph to serve as start nodes for breadth-first or depth-first multi-step walking, obtaining walking topological graphs of multiple candidate final nodes, and calculating reach probability of the start nodes to the final nodes on the basis of a breadth-first or depth-first graph walking algorithm and according to the retrieval data corresponding to the vertexes and the edges participating in the walking path, (C) sequencing the calculated reach probability. The graph computation method can obtain results free of popular relations and strong relations. The graph computation engine can retrieve various graph data, supports various graph computation algorithms, supports topological graphs of self-defined graph computation, and supports real-time adding, deleting and modifying of the retrieval data, the topological graphs and shared library data.

Description

technical field [0001] The present invention relates to the field of graph computing, in particular to a graph computing method and engine. Background technique [0002] Graph computation plays an important role in relationship building, user group analysis and discovery, and attribute propagation. In the era of big data, when the scale of graphs reaches a certain level, it is difficult for a single machine to solve large-scale graph calculations. Therefore, it is of great significance to develop and debug graph algorithms for large-scale data. Currently more mature solutions include Graphx and GraphLab. One branch of the GraphLab project is GraphChi, which is a framework that can complete big data graph calculations on a single machine. [0003] GraphChi can efficiently perform large-scale graph calculations on personal computers. It has its own original optimization algorithm for obtaining graph data from the hard disk, and supports streaming graph updates and graph str...

AI Technical Summary

Problems solved by technology

[0017] Although GraphChi can realize large-scale graph calculations on personal computers, there are still some deficiencies and defects: GraphChi does not have an indexing module, does not support custom graph calculation topology maps, does not support plug-in hot-swapping, etc.

Therefore, the above-mentioned existing graph computing methods and engines still have inconveniences and defects in use, and need to be further improved.

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