Embedding static graphs in low-dimensional vector spaces plays a key role in network
analytics and inference, supporting applications like node classification, link prediction, and …

Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static)
graph-structured data. However, many real-world systems are dynamic in nature, since the …

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 …

Dynamic networks are used in a wide range of fields, including social network analysis,
recommender systems and epidemiology. Representing complex networks as structures …

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 …

Understanding the interconnected relationships of large-scale information networks like
social, scholar and Internet of Things networks is vital for tasks like recommendation and …

Modeling the dynamics into graph neural networks (GNNs) contributes to the understanding
of evolution in dynamic graphs, which helps optimize temporal-spatial representations for …

The entity alignment task aims to align entities corresponding to the same object in different
KGs. The recent work focuses on applying knowledge embedding or graph neural networks …

Temporal link prediction (TLP) aims to predict future links and is attracting increasing
attention. The diverse interaction patterns and nonlinear nature of temporal networks make it …

Network representation learning (NRL) is an effective graph analytics technique and
promotes users to deeply understand the hidden characteristics of graph data. It has been …