A Link Prediction Method Based on Bayesian Estimation and Neighbor Set of Seed Nodes
A technology of Bayesian estimation and seed nodes, applied in digital transmission systems, data exchange networks, electrical components, etc., can solve the problem of low prediction accuracy and achieve high accuracy
Active Publication Date: 2020-05-05
ZHEJIANG UNIV OF TECH
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[0005] In order to overcome the problem that the existing link prediction method based on the neighbor set of seed nodes only considers the intermediate nodes of the paths whose path lengths are equal to 2 and 3, and only considers the degrees of these nodes, resulting in low prediction accuracy, the present invention proposes A high-accuracy link prediction method based on Bayesian estimation and seed node neighbor set
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[0029] The present invention will be further described below in conjunction with the accompanying drawings.
[0030] refer to figure 1 , a link prediction method based on Bayesian estimation and seed node neighbor set, including the following steps:
[0031] Step 1: Establish a network model G(V,E), V represents the set of nodes in the network, E represents the set of edges in the network, the total number of nodes in the network is recorded as N, and U represents the set of node pairs in the network, |U |=N(N-1) / 2 represents the total number of node pairs in the network;
[0032] Step 2: Randomly select two nodes x and y in the network as seed nodes, namely figure 1 The black dots in the middle indicate the possibility of calculating the existence of a direct edge between them:
[0033]
[0034] Among them, |E| represents the total number of edges actually existing in the network, and A 1 Indicates that there is a direct connection between the two nodes x and y;
[00...
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The invention provides a link estimation method based on Bayesian estimation and a seed node neighbor set. The method comprises the following steps: establishing a network model, randomly selecting two nodes that are not directly connected to serve as seed nodes, separately calculating an edge existence probability and an edge non-existence probability of the nodes, separately calculating a connection edge generation probability and a connection edge non-generation probability of the two nodes according to the credibility of intermediate nodes on a path with a length 2 or 3 between the two nodes, calculating a likelihood value of each intermediate node on the paths with the lengths 2 and 3 between the two nodes according to the Bayesian estimation and the seed node neighbor set, wherein the similarity score is the sum of the likelihood values of all intermediate nodes; traversing the network, obtaining the similarity score between the two seed nodes by using the above method, sorting all seed nodes in a descending order, and obtaining the nodes corresponding to B score values as predicted connection edges. According to the link estimation method, different importance of different intermediate nodes in a local path between the two nodes is distinguished according to the Bayesian estimation in combination with the seed node neighbor set, and thus the prediction effect of the algorithm is good.
Description
technical field [0001] The invention relates to the fields of network science and link prediction, in particular to a link prediction method based on Bayesian estimation and seed node neighbor set. Background technique [0002] Complex systems in real life can be studied using complex networks. Nodes in the network represent individuals in the complex system, and edges represent the interrelationships between nodes in the system. Link prediction is one of the important research fields of complex networks, because link prediction can predict the links that may be generated between nodes during the evolution of the network, so the evolution trend of the network can be predicted in advance, and it can be judged The "ghost edge" that does not exist in the network can better help researchers study the internal laws of the network. [0003] The link prediction problem has received extensive attention from researchers. In comparison, the link prediction algorithm based on network...
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IPC IPC(8): H04L12/24
CPCH04L41/145H04L41/147
Inventor 杨旭华项旗立张海丰肖杰
Owner ZHEJIANG UNIV OF TECH