The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman
algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a …

Current few-shot action recognition methods reach impressive performance by learning
discriminative features for each video via episodic training and designing various temporal …

The requirement for appropriate ways to measure the similarity between data objects is a
common but vital task in various domains, such as data mining, machine learning and so on …

Graphs have been widely used to represent complex data in many applications, such as e-
commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data …

Molecular relational learning, whose goal is to learn the interaction behavior between
molecular pairs, got a surge of interest in molecular sciences due to its wide range of …

Similarity search in graph databases is one of the most fundamental operations in graph
analytics. Among various distance functions, graph and subgraph edit distances (GED and …

While the celebrated graph neural networks (GNNs) yield effective representations for
individual nodes of a graph, there has been relatively less success in extending to the task …

Self-supervised learning of graph neural networks (GNNs) aims to learn an accurate
representation of the graphs in an unsupervised manner, to obtain transferable …