Training graph neural networks (GNNs) on large graphs is complex and extremely time
consuming. This is attributed to overheads caused by sparse matrix multiplication, which are …

Sampling subgraphs for training Graph Neural Networks (GNNs) is receiving much attention
from the GNN community. While a variety of methods have been proposed, each method …

State-of-the-art neural algorithmic reasoners make use of message passing in graph neural
networks (GNNs). But typical GNNs blur the distinction between the definition and invocation …

How can we best encode structured data into sequential form for use in large language
models (LLMs)? In this work, we introduce a parameter-efficient method to explicitly …

The mining and exploitation of graph structural information have been the focal points in the
study of complex networks. Traditional structural measures in Network Science focus on the …

Graph neural networks (GNNs) demonstrate outstanding performance in a broad range of
applications. While the majority of GNN applications assume that a graph structure is given …

Data continuously emitted from industrial ecosystems such as social or e-commerce
platforms are commonly represented as heterogeneous graphs (HG) composed of multiple …

ZX-diagrams are a powerful graphical language for the description of quantum processes
with applications in fundamental quantum mechanics, quantum circuit optimization, tensor …

Establishing open and general benchmarks has been a critical driving force behind the
success of modern machine learning techniques. As machine learning is being applied to …

Text reading order is a crucial aspect in the output of an OCR engine, with a large impact on
downstream tasks. Its difficulty lies in the large variation of domain specific layout structures …