  With the rapid information explosion on online social network sites (SNSs),
it becomes difficult for users to seek new friends or broaden their social
networks in an efficient way. Link prediction, which can effectively conquer
this problem, has thus attracted wide attention. Previous methods on link
prediction fail to comprehensively capture the factors leading to new link
formation: 1) few models have considered the varied impacts of users'
short-term and long-term interests on link prediction. Besides, they fail to
jointly model the influence from social influence and "weak links"; 2)
considering that different factors should be derived from information sources
of different modalities, there is a lack of effective multi-modal framework for
link prediction. In this view, we propose a novel multi-modal framework for
link prediction (referred as LP-UIT) which fuses a comprehensive set of
features (i.e., user information and topological features) extracted from
multi-modal information (i.e., textual information, graph information, and
numerical information). Specifically, we adopt graph convolutional network to
process the network information to capture topological features, employ natural
language processing techniques (i.e., TF-IDF and word2Vec) to model users'
short-term and long-term interests, and identify social influence and "weak
links" from numerical features. We further use an attention mechanism to model
the relationship between textual and topological features. Finally, a
multi-layer perceptron (MLP) is designed to combine the representations from
three modalities for link prediction. Extensive experiments on two real-world
datasets demonstrate the superiority of LP-UIT over the state-of-the-art
methods.
