Link Prediction for Node Featureless Networks Based on Faster Attention Mechanism

Abstract

Link prediction is an important task in network science.It aims to predict the link existence probabilities of two nodes.There are many relations between substances in real word, which can be described by network science in computers.There are many problems of daily life, which can be transformed to link prediction tasks.Link prediction algorithms for node featureless networks are convenient to migrate in directed networks, weighted networks, time networks, and so on.However, the traditional link prediction algorithms are faced with many problems as follows.The network structures information mining is not deep enough.The feature extraction processes depend on subjective consciousness.The algorithms are short of universality, and the time complexity and space complexity are flawed, which cause that they are difficult to be applied to real industry networks.In order to effectively avoid the above problems, based on the basic structure of graph attention network, graph embedding representation technology is used to collect node characteristics, analogy with the memory addressing strategy in neural turing machine, and combined with the relevant work of important node discovery in complex network, a fast and efficient attention calculation method is designed, and a node featureless network link prediction algorithm FALP integrating fast attention mechanism is proposed.Experiment on three public datasets and a private dataset show that the FALP effectively avoids these problems and has excellent predictive performance.

Keywords

• link prediction|attention mechanism|graph neural networks (gnns)|graph embedding|network science