Depth belief network-based link prediction method

A deep belief network and link prediction technology, applied in the field of artificial neural network, can solve the problems of low network universality and low prediction accuracy of link prediction algorithm, and achieve the effect of high prediction accuracy and universality.

Inactive Publication Date: 2017-08-01
NANJING UNIV OF POSTS & TELECOMM
View PDF0 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the above-mentioned traditional methods have the problems of low prediction accuracy of the

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Depth belief network-based link prediction method
  • Depth belief network-based link prediction method
  • Depth belief network-based link prediction method

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0029] The present invention will be further described in detail with reference to the accompanying drawings. The present invention proposes an algorithm for link prediction using a deep confidence network classification model for undirected networks. The specific implementation methods include:

[0030] Training data set acquisition module, according to the characteristics of the deep belief network training process, this module needs to collect training edge sets, verification edge sets and test edge sets.

[0031] The network node feature representation module uses the deepwalk algorithm to obtain the feature vector representation of each network node on the network data processed by the collected data set;

[0032] Generate edge feature representation module, each edge can be represented by a node pair, we use the method of directly splicing the respective feature vectors of two node pairs to generate the feature vector representation of the corresponding edge;

[0033] Construct ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a depth belief network-based link prediction method. The method comprises the steps that firstly a training data collection module performs random sampling in a given network structure to obtain a training edge set, a verification edge set and a test edge set; a network node characteristic representation module generates a characteristic representation of each network node by using a deepwalk algorithm in a network processed through the training data collection module; an edge characteristic representation generation module calculates a characteristic representation of each edge in the training edge set, the verification edge set and the test edge set, and performs normalization processing on eigenvectors of generated edges to meet the requirements of a depth belief network on input data; and finally a depth belief network training module constructs a depth belief network structure and loads the training edge set, the verification edge set and the test edge set to perform training. According to the method, the prediction accuracy higher than that of a conventional link prediction algorithm can be achieved; and the method has universality for networks with various structure characteristics.

Description

technical field [0001] The invention belongs to the technical field of artificial neural networks, and in particular relates to a link prediction method based on a deep belief network. Background technique [0002] Walk-based network representation learning algorithms, such as deepwalk, use the theoretical method of word2vec to compare the nodes in the network with the word units in natural language processing, and compare the connection paths one by one in the network to natural A sentence in language processing; use the method of solving the co-occurrence relationship between each word (that is, all conditional probability parameters) in the probabilistic language model to explore the connection structure between network nodes; use the method of generating word vectors to generate A vector representation of nodes in a network. The vector of network nodes obtained through this analogy algorithm reflects the structural characteristics of the connection between the correspon...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F17/30G06N3/08G06Q50/00
CPCG06F16/958G06N3/08G06Q50/01
Inventor 李涛王次臣李华康
Owner NANJING UNIV OF POSTS & TELECOMM
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap