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 …

Learning representations for graphs plays a critical role in a wide spectrum of downstream
applications. In this paper, we summarize the limitations of the prior works in three folds …

Though the sheer volume of data that is collected is immense, it is the rare categories that
are often the most important in many high-impact domains, ranging from financial fraud …

Multiple networks emerge in a wealth of high-impact applications. Network alignment, which
aims to find the node correspondence across different networks, plays a fundamental role for …

Logical queries constitute an important subset of questions posed in knowledge graph
question answering systems. Yet, effectively answering logical queries on large knowledge …

Representation learning on temporal graphs has drawn considerable research attention
owing to its fundamental importance in a wide spectrum of real-world applications. Though a …

Network embedding (NE) aims at learning node embeddings via structure-based sampling.
However, there are complex patterns in network structure (heterogeneity, higher-order …

Temporal hypergraphs provide a powerful paradigm for modeling time-dependent, higher-
order interactions in complex systems. Representation learning for hypergraphs is essential …

Heterogeneous information network (HIN) embedding has gained considerable attention in
recent years, which learns low-dimensional representation of nodes while preserving the …

Modeling and exploring high-order connectivity patterns, also called network motifs, are
essential for understanding the fundamental structures that control and mediate the behavior …