Predicting Probabilistic Links Using Node Embedding and Graph Neural Network Approach

Abstract

Link prediction describes the procedure for discovering out how likely it is that an edge will be present in a graph network. In the realm of graph network and graph data analytics, link prediction is regarded as a crucial challenge. Estimating possible future linkages among nodes in a network using an overview of the network at a certain moment in time is the focus of link prediction. This technique is utilized in various domains, including recommended systems, network traffic prediction, protein-protein interactions, and numerous other areas. Typically, conventional methods are implemented to quantify the degree of similarity between two specified nodes in a fixed network. The evaluation of networks that undergo dynamic changes over time, however, has recently become the subject of academic research. Heuristic node similarity scores, latent node embeddings, and explicit node attributes are the mainstays of current link prediction methods. Over time, graph neural networks (GNNs) have proven themselves superior to more conventional techniques of link prediction due to their ability to concurrently learn from graph structure and node/edge properties. A graph neural network model for predicting probabilistic links with network embedding via generalized graph convolutional networks, referred to as LPNE2GGCN for graph network datasets, has been proposed. This approach draws inspiration from the promising outcomes achieved by GCNs in evaluating such data. The LPNE2GGCN approach leverages node embedding (Node2Vec) via generalized graph convolutional networks (GraphSAGE). To assess its effectiveness, LPNE2GGCN has been compared against established latent node representation methods, including Node. The performance of LPNE2GGCN is evaluated by contrasting it with well-known techniques for latent node representation. The framework further improves link prediction accuracy by utilizing learning-based optimization techniques like Adadelta and Adam. The experimental results show that the suggested strategy outperforms the state-of-the-art across multiple social network datasets, including those using node embedding techniques and various approaches based on graph neural networks. The proposed model is based on graph neural network techniques that integrates the features obtained from node2vec and utilizes them as input for a hidden neural network consisting of three layers. The initial results suggest that this methodology produces excellent results compared to conventional methods across diverse social network datasets.