Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …

Graph kernels have become an established and widely-used technique for solving
classification tasks on graphs. This survey gives a comprehensive overview of techniques …

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 …

Recently, there has been an increasing interest in (supervised) learning with graph data,
especially using graph neural networks. However, the development of meaningful …

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 …

Learning node embeddings that capture a node's position within the broader graph structure
is crucial for many prediction tasks on graphs. However, existing Graph Neural Network …

Link prediction is a key problem for network-structured data. Link prediction heuristics use
some score functions, such as common neighbors and Katz index, to measure the likelihood …

Kernel classifiers and regressors designed for structured data, such as sequences, trees
and graphs, have significantly advanced a number of interdisciplinary areas such as …

In recent years, graph neural networks (GNNs) have become the de facto tool for performing
machine learning tasks on graphs. Most GNNs belong to the family of message passing …

The task of representing entire graphs has seen a surge of prominent results, mainly due to
learning convolutional neural networks (CNNs) on graph-structured data. While CNNs …