Community search algorithm based on k-kernel

Abstract

The invention discloses a community search algorithm based on a k-kernel. The community search algorithm includes the steps of growing a maximum spanning tree (MST) for an image, preprocessing the MST, finding a subtree for connecting all search nodes from the MST, finding a subtree with the search nodes, and returning the maximum K-kernel. By means of the community search algorithm, the k-kernel with a designated node can be found in the time complexity O(T), the value of k is the maximum, and T represents the size of a community where search needs to be conducted.

Description

technical field [0001] The invention relates to a graph index technology of maximum spanning tree, in particular to a community search algorithm based on k-kernel. Background technique [0002] In recent years, community mining in graphs and social networks has attracted widespread attention, which is also one of the more fundamental problems in graph mining. Most research works only focus on finding out the community structure in the original image. However, in many application scenarios it is of interest to find the communities formed by a given set of nodes. [0003] The community search problem based on a given node is defined as: Given an undirected connected graph G and a point set Q in the graph, find a k-core of G so that it contains all nodes in the given node set Q and Its k value is the largest. [0004] For this problem, a simple greedy algorithm can find qualified communities in polynomial time (see reference [1] for details); global search algorithm (global ...

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Problems solved by technology

[0008] Although global search and local search have good time complexity, for a given query node, these two algorithms need to execute a complete algorithm for each query, and the time complexity is still high

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