Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks

Abstract

In some online social network services (SNSs), the members are allowed to label their relationships with others, and such relationships can be represented as the links with signed values (positive or negative). The networks containing such relations are named signed social networks (SSNs), and some real-world complex systems can be also modeled with SSNs. Given the information of the observed structure of an SSN, the link prediction aims to estimate the values of the unobserved links. Noticing that most of the previous approaches for link prediction are based on the members' similarity and the supervised learning method, however, research work on the investigation of the hidden principles that drive the behaviors of social members are rarely conducted. In this paper, the deep belief network (DBN)-based approaches for link prediction are proposed. Including an unsupervised link prediction model, a feature representation method and a DBN-based link prediction method are introduced. The experiments are done on the datasets from three SNSs (social networking services) in different domains, and the results show that our methods can predict the values of the links with high performance and have a good generalization ability across these datasets.

Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review

“…The experimental data revealed that the proposed method outperformed approaches that rely on topological features alone. Several link prediction approaches that are based on a deep belief network were presented in [12]. The results obtained from the experiments on datasets collected from different sources revealed that the methods effectively predicted link values and exhibited a remarkable generalization capability among the studied datasets.…”

A Fusion Link Prediction Method Based on Limit Theorem

“…This is the step of model training or pdf estimation. Among all combination methods, there is an inevitable time complexity, that is to obtain the similarity matrix or final link prediction scores according to Equation (14). This step requires time O(d•n s •N 2 ).…”

“…At the same time, similarity index fusion methods are springing up [7,8]. Recent years, with the development of deep learning, some deep features extraction methods have been proposed [9,10], the fusion of structure and attribute information has been attached importance again [11][12][13][14]. These methods have strong consistency.…”

An Efficient Link Prediction Technique in Social Networks based on Node Neighborhoods

“…Bayesian network has been also considered as a consistent model to understand the relations between future links to be predicted in networks [19], [20]. Recently, negative link prediction in social networks has attracted the attention of many researchers and considerable research work is being carried out to find efficient techniques for the same [15], [16], [18]. Such techniques aim to perform link prediction across multiple signed networks.…”