Fast symbol community discovery system and algorithm for automatically determining number of communities

An automatic determination and community discovery technology, applied in computing, network data retrieval, data processing applications, etc., can solve problems such as high time complexity, and achieve the effect of low time complexity and reduced time complexity

Inactive Publication Date: 2018-07-10
SHENZHEN INSTITUTE OF INFORMATION TECHNOLOGY
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  • Claims
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Problems solved by technology

This leads to the reason why the time complexity

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  • Fast symbol community discovery system and algorithm for automatically determining number of communities
  • Fast symbol community discovery system and algorithm for automatically determining number of communities
  • Fast symbol community discovery system and algorithm for automatically determining number of communities

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[0034] The system of the present invention is to let A denote the adjacency matrix of network N, a ij Is the element of A, a ij =1 means there is a positive edge between node i and node j, a ij = -1 means there is a negative edge between node i and node j, a ij =0 means that there is no edge between node i and node j.

[0035] The network model (NModel) is defined as follows:

[0036] NModel=(n,K,Z,π,θ,ω)

[0037] Among them, n corresponds to the number of nodes in the network; K is the number of model blocks, which corresponds to the number of communities in the network; Z is an n×K-dimensional matrix with elements Z i Is a K-dimensional indicator vector. If node i belongs to block k, then Z ik =1, otherwise Z ik =0; π={π 1 ,π -1 ,π 0 } Is a 3-dimensional vector, where π 1 Represents the connection probability of positive links in the block, π -1 Represents the connection probability of negative links in the block, π 0 Indicates the probability of no link in the block; θ={θ 1...

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Abstract

The invention relates to a fast symbol community discovery system for automatically determining the number of communities. The system comprises an adjacency matrix A and a network model(NModel) of a network N; the network model (NModel)=(n, K, Z, [pi], [theta], [omega]), wherein n is corresponding to the node number of the network; K is the number of models and corresponding to the number of communities in the network; Z is a n*K dimension matrix, wherein Zi is the indication vector of K dimension; [pi]={ [pi]1, [pi]-1, [pi]0} is a three dimension vector, wherein the [pi] 1 represents the connecting efficiency of positive linkage within the block, [pi]2 represents the connecting efficiency of negative linkage within the block and [pi] 0 represents the efficiency of zero linkage within theblock; [theta]={ [theta]1, [theta]-1, [theta]0} is a three dimension vector, wherein the [theta] 1 represents the connecting efficiency of positive linkage within the block, [theta]2 represents the connecting efficiency of negative linkage within the block and [theta] 0 represents the efficiency of zero linkage within the block; [omega] is a K-dimension vector which indicates the ratio relation ofnodes distributing in different blocks and the formula is satisfied: the summation of [omega]k equals to 1, wherein k is from 1 to K. Parallel computing of parameter estimation and model selection isrealized, which effectively reduces the time complexity of symbolic community mining.

Description

technical field [0001] The invention belongs to the field of model construction and relates to a model selection method, in particular to a fast symbolic community discovery system and algorithm for automatically determining the number of communities. Background technique [0002] A symbolic network refers to a network whose links have positive and negative attributes. Compared with an unsigned network, the positive and negative links of a symbolic network represent positive and negative relationships between individuals respectively. Links represent relationships such as hostility, dislike, and distrust, and symbolic information can more completely express the relationship between individuals, which is helpful for a deeper understanding of the hidden laws of the network. Community is an important structural pattern that is ubiquitous in symbolic networks. Because of the need to consider the symbols of links, symbolic communities are characterized by as many positive links a...

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Application Information

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IPC IPC(8): G06F17/30G06Q50/00
CPCG06F16/951G06Q50/01
Inventor 赵学华谭旭唐飞徐龙琴
Owner SHENZHEN INSTITUTE OF INFORMATION TECHNOLOGY
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