Graph classification is a widely studied problem and has broad applications. In many real-
world problems, the number of labeled graphs available for training classification models is …

Link prediction is one of the key problems for graph-structured data. With the advancement
of graph neural networks, graph autoencoders (GAEs) and variational graph autoencoders …

Spiking neural networks (SNNs) have captivated the attention worldwide owing to their
compelling advantages in low power consumption, high biological plausibility, and strong …

The convolution operator at the core of many modern neural architectures can effectively be
seen as performing a dot product between an input matrix and a filter. While this is readily …

Link prediction is an important learning task for graph-structured data, and has become
increasingly popular due to its wide application areas. Graph Neural Network (GNN)-based …

Advanced graph neural networks have shown great potentials in graph classification tasks
recently. Different from node classification where node embeddings aggregated from local …

Graph neural networks are increasingly becoming the framework of choice for graph-based
machine learning. In this paper, we propose a new graph neural network architecture that …

Recent research has explored how Language Models (LMs) can be used for feature
representation and prediction in tabular machine learning tasks. This involves employing …

With the increase of graph-structure data, link prediction has become an active research
topic. Recent progress in link prediction involves learning graph embeddings through graph …

The task of determining whether or not a link will exist between two entities, given the current
position of the network, is called link prediction. The study of predicting and analyzing links …