Graph analytics can lead to better quantitative understanding and control of complex
networks, but traditional methods suffer from the high computational cost and excessive …

Graph neural networks (GNNs) have emerged as a powerful tool for effectively mining and
learning from graph-structured data, with applications spanning numerous domains …

Since many real world networks are evolving over time, such as social networks and user-
item networks, there are increasing research efforts on dynamic network embedding in …

Network embedding, as an approach to learn lowdimensional representations of vertices,
has been proved extremely useful in many applications. Lots of state-of-the-art network …

Temporal graph representation learning has drawn significant attention for the prevalence of
temporal graphs in the real world. However, most existing works resort to taking discrete …

Network embedding has received increasing research attention in recent years. The existing
methods show that the high-order proximity plays a key role in capturing the underlying …

Network embedding aims to preserve vertex similarity in an embedding space. Existing
approaches usually define the similarity by direct links or common neighborhoods between …

In this work, we present a method for node embedding in temporal graphs. We propose an
algorithm that learns the evolution of a temporal graph's nodes and edges over time and …

With the great success of graph embedding model on both academic and industry area, the
robustness of graph embedding against adversarial attack inevitably becomes a central …

Heterogeneous information network (HIN) embedding aims at learning the low-dimensional
representation of nodes while preserving structure and semantics in a HIN. Existing methods …