Link prediction finds missing links (in static networks) or predicts the likelihood of future links
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …

While advances in computing resources have made processing enormous amounts of data
possible, human ability to identify patterns in such data has not scaled accordingly. Efficient …

Graph Neural Networks (GNNs) have recently become increasingly popular due to their
ability to learn complex systems of relations or interactions arising in a broad spectrum of …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

The availability of large scale event data with time stamps has given rise to dynamically
evolving knowledge graphs that contain temporal information for each edge. Reasoning …

Despite the importance and abundance of temporal knowledge graphs, most of the current
research has been focused on reasoning on static graphs. In this paper, we study the …

In this paper, we generally formulate the dynamics prediction problem of various network
systems (eg, the prediction of mobility, traffic and topology) as the temporal link prediction …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Previous work analyzing social networks has mainly focused on binary friendship relations.
However, in online social networks the low cost of link formation can lead to networks with …

Abstract Knowledge graph (KG for short) alignment aims at building a complete KG by
linking the shared entities across complementary KGs. Existing approaches assume that …