Incremental embedding for temporal networks

Many vital tasks in network analysis involve prediction over edges and nodes. For instance in node classification, we aim at predicting most likely node's label. In a social network, this might be an interest or preference of the user, or in citation network, the research area the paper belongs to [7]. In link prediction, we intend to model the existence of a link between pair of nodes. Predicted links in social networks may denote real-life friends and in citation networks related but unmentioned references. It is recently recognized, that the majority of real-world networks are naturally dynamic. It means they evolve over time and nodes as well as links can appear or disappear. We know so far, that considering temporal information about the network allows their better understanding and modeling [9,16], especially for supervised machine learning tasks.