tl;dr: We provably improve GNN expressivity by enhancing message passing with substructure encodings. Our method allows incorporating domain specific prior knowledge and can be used as a drop-in ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Accurately modeling chemical reactions in molecular dynamics simulations requires ...

Abstract: The rise of graph analytic systems has created a need for ways to measure and compare the capabilities of these systems. Graph analytics present unique scalability difficulties. The machine ...

Control flow graphs (CFGs) generated through a dynamic or emulated approach contain many benefits over statically generated CFGs such as yielding smaller, more well connected, and less noisy graphs.

This article introduces a model-based design, implementation, deployment, and execution methodology, with tools supporting the systematic composition of algorithms from generic and domain-specific ...

Control flow graphs (CFGs) and function call graphs (FCGs) have become pivotal in providing a detailed understanding of program execution and effectively characterizing the behaviour of malware. These ...

Over the past few years, graph neural networks and graph transformers have been successfully used to analyze graph-structured data, mainly focusing on node classification and link prediction tasks.