Graph Convolutional Neural Networks for Link Prediction in Social Networks

Graph Convolutional Neural Networks for Link Prediction in Social Networks

Nimish Kumar, Himanshu Verma, Yogesh Kumar Sharma
Copyright: © 2023 |Pages: 22
DOI: 10.4018/978-1-6684-6903-3.ch007
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Abstract

Social networks are complex systems that require specialized techniques to analyze and understand their structure and dynamics. One important task in social network analysis is link prediction, which involves predicting the likelihood of a new link forming between two nodes in the network. Graph convolutional neural networks (GCNNs) have recently emerged as a powerful approach for link prediction, leveraging the graph structure and node features to learn effective representations and predict links between nodes. This chapter provides an overview of recent advances in GCNNs for link prediction in social networks, including various GCNN architectures, feature engineering techniques, and evaluation metrics. It discusses the challenges and opportunities in applying GCNNs to social network analysis, such as dealing with sparsity and heterogeneity in the data and leveraging multi-modal and temporal information. Moreover, this also provides reviews of several applications of GCNNs for link prediction in social networks.
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Social network analysis has been an area of research for many years, with link prediction being a key problem in this field. The objective of link prediction is to forecast the likelihood of a connection between two nodes in a network based on their characteristics and the structure of the network. Link prediction is useful for numerous applications in social networks, such as recommender systems, information diffusion, and community detection.

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