Depth belief network-based link prediction method

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...

AI Technical Summary

Problems solved by technology

However, the above-mentioned traditional methods have the problems of low prediction accuracy of the link prediction algorithm and low universality to various structural characteristics of the network.

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