Who will accept my request? Predicting response of link initiation in two-way relation networks

TL;DR

This paper presents a multilayer network-based method to predict whether a link initiation in social networks will be accepted, using meta-path features and clustering to address data sparsity, outperforming existing approaches.

Contribution

It introduces a novel multilayer network approach with meta-path features and clustering to improve link initiation feedback prediction in social networks.

Findings

The proposed method achieves higher accuracy than state-of-the-art techniques.

Meta-path based features effectively capture complex relationships across layers.

Clustering mitigates data sparsity issues, enhancing prediction performance.

Abstract

Popularity of social networks has rapidly increased over the past few years, and daily lives interrupt without their proper functioning. Social networking platform provide multiple interaction types between individuals, such as creating and joining groups, sending and receiving messages, sharing interests and creating friendship relationships. This paper addresses an important problem in social networks analysis and mining that is how to predict link initiation feedback in two-way networks. Relationships between two individuals in a two-way network include a link invitation from one of the individuals, which will be an established link if it is accepted by the invitee. We consider a sport gaming social networking platform and construct a multilayer social network between a number of users. The network formed by the link initiation process is on one of the layers, while the other two layers include a messaging relationships and interactions between the users. We propose a methodology to solve the link initiation feedback prediction problem in this multilayer fashion. The proposed method is based on features extracted from meta-paths, i.e. paths defined between different individuals from multiples layers in multilayer networks. We proposed a cluster-based approach to handle the sparsity issue in the dataset. Experimental results show that the proposed method can provide accurate prediction that outperforms state-of-the-art methods.