  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.
