Abstract:
In the past few years, various methods have been developed that attempt to embed graph nodes (e.g. users that interact through a social platform) onto low-dimensional vector spaces, exploiting the relationships (commonly displayed as edges) among them. The extracted vector representations of the graph nodes are then used to effectively solve machine learning tasks such as node classification or link prediction. These methods, however, focus on the static properties of the underlying networks, neglecting the temporal unfolding of those relationships. This affects the quality of representations, since the edges don’t encode the response times (i.e. speed) of the users’ (i.e. nodes) interactions. To overcome this limitation, we propose an unsupervised method that relies on temporal random walks unfolding at the same timescale as the evolution of the underlying dataset. We demonstrate its superiority against state-of-the-art techniques on the tasks of hidden link prediction and future link forecast. Moreover, by interpolating between the fully static and fully temporal setting, we show that the incorporation of topological information of past interactions can further increase our method efficiency.
