Undirected network edge-connectivity weight prediction method based on node similarity

A technology of undirected network and prediction method, applied in the direction of prediction, resources, instruments, etc., can solve the problems of poor model prediction results, and achieve the effect of simple model and good prediction results.

Inactive Publication Date: 2017-07-18
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0003] In order to overcome the shortcomings of poor model prediction results caused by the lack of edge weights in the existing network, the present invention uses the similarity of network nodes to predict the missing edge weights using a multiple lin

Method used

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  • Undirected network edge-connectivity weight prediction method based on node similarity
  • Undirected network edge-connectivity weight prediction method based on node similarity
  • Undirected network edge-connectivity weight prediction method based on node similarity

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Embodiment Construction

[0011] The present invention will be further described below in conjunction with the accompanying drawings.

[0012] refer to figure 1 , an undirected network edge weight prediction method based on node similarity, including the following steps:

[0013] S1: Utilize the existing nematode neural network (C.elegans) data set, wherein nodes represent nematode neurons, edges represent neuron synapses or gap junctions, and construct an undirected network graph G=(V, E);

[0014] S2: The adjacency matrix A of graph G=(a ij ) n×n , i,j∈{1,2,...,n},

[0015] in:

[0016]

[0017] According to the adjacency matrix A, the following similarity indexes are calculated respectively:

[0018] 1) Common neighbor CN:

[0019]

[0020] Where |Q| represents the number of elements of the set Q, Γ(x) is defined as the set of neighbor nodes of node x, Indicates the CN index value between node x and node y, the same below;

[0021] 2) Salton indicator:

[0022]

[0023] where k x...

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Abstract

The invention relates to an undirected network edge-connectivity weight prediction method based on node similarity. The undirected network edge-connectivity weight prediction method comprises following steps of 1) establishing an undirected network diagram in dependence on an existing data set including nodes and the edge-connectivity weight; 2) respectively calculating following three kinds of similarity indexes including the local similarity index, the global similarity index and the semi-local similarity index in the network diagram in step 1); and 3) predicting the edge-connectivity weight in the test set in dependence on the three kinds of similarity indexes calculated in the step 2) and by means of a multivariate linear regression model, and then examining the model mean performances including the Pearson coefficient and the root-mean-square value by means of a tenfold crossing verification method. According to the invention, by means of the node similarity, the missing edge-connectivity weight is predicted through the multivariate linear regression model, the model is simple, and the prediction result is good.

Description

technical field [0001] The invention relates to the fields of link prediction and data mining, in particular to a method for predicting connection weights based on network node similarity. Background technique [0002] In reality, many systems can be abstracted as complex network models. Individual objects in the system are abstracted as nodes, and the relationship between individuals is abstracted as edges, such as social networks, protein interaction networks, and power networks. Among them, the network edge, as a bridge connecting individual objects, plays an important role in revealing the network structure. In reality, the edges of many networks have weights, and these edge weights have clear physical meanings. Due to various reasons, some network edge weights may be missing, especially when the missing weights contain important network structure information, the prediction of these weights is very critical. Contents of the invention [0003] In order to overcome th...

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Application Information

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IPC IPC(8): G06Q10/04G06F17/30G06Q10/06
CPCG06Q10/04G06F16/2465G06Q10/06393
Inventor 宣琦赵明浩虞烨炜周鸣鸣傅晨波
Owner ZHEJIANG UNIV OF TECH
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