In my last article, I broke down the fundamentals of Knowledge Graphs — nodes, relationships, and properties. That was the foundation. But the real question I often get is: How do you actually design ...
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 ...
We aim to build a pre-trained Graph Neural Network (GNN) model on molecules without human annotations or prior knowledge. Although various attempts have been proposed to overcome limitations in ...
The microbiome represents a complex community of trillions of microorganisms residing in various body parts and plays critical roles in maintaining host health and wellbeing. Understanding the ...
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 ...
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 ...
Shanghai -- We’ve recently come out of two long, interesting days at LDBC’s 18th Technical Users Committee meeting in Guangzhou, in southern China. This post largely concentrates on one point that ...
The inference of cell–cell communication (CCC) is crucial for a better understanding of complex cellular dynamics and regulatory mechanisms in biological systems. However, accurately inferring spatial ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Designing molecular structures with desired chemical properties is an essential task ...
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