Group competitive influence identification of social network groups based on graph representation learning
Group competitive influence identification is a necessary area in social network analysis,whose task is to identify the influence of two groups of nodes in a social network under competing conditions,and has significant implications in practical scenarios such as public opinion analysis.In recent years,much re-search has focused on identifying group influence without competitors.However,competition is ubiquitous in real-world social networks,making it necessary to study group competitive influence identification tasks.Compared to group influence identification in non-competitive scenarios,group competitive influence identifi-cation presents challenges such as the construction of competitive datasets and embedding aggregation of com-petitive groups.Graph representation learning(GRL)has been successful in social network analysis,as it can represent graphical structure as low-dimensional embeddings with structural information and interactions be-tween nodes,providing richer information than traditional methods.This paper pioneered the use of GRL to solve group influence prediction problems in competitive scenarios and proposed a GRL-based framework.To address the problem of constructing competing datasets,a group pair construction method based on influence diversity is proposed.To address the problem of embedding aggregation,a method based on summation-subtraction is proposed to aggregate the embeddings of nodes in a competitive group pair.Extensive experi-ments are conducted on seven real social networks to analyze the effectiveness of the proposed framework.The experimental results show that the proposed framework outperforms the baseline approach.
Group competitive influence identificationSocial network analysisDeep learningGraph neural network
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