this process is a message passing(aggreation) and pass through a liner layer if these process repeat N time it will mean having N layer of GCN all of these will ...
The official source code for Interpretable Prototype-based Graph Information Bottleneck at NeurIPS 2023. Overview of Interpretable Prototype-based Graph Information Bottleneck. The success of Graph ...
Graphs are ubiquitous tools in science that allow one to explore data patterns, design studies, communicate findings, and make claims. This essay is a companion to the online, evidence-based ...
Abstract: Graph neural networks (GNNs) demonstrate great performance in compound property and activity prediction due to their capability to efficiently learn complex molecular graph structures.
Thanks to their visual simplicity, bar graphs are popular tools for representing data. But do we really understand how to read them? New research from Wellesley College published in the Journal of ...
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Hard in theory, easy in practice: Why graph isomorphism algorithms seem to be so effective
Graphs are everywhere. In discrete mathematics, they are structures that show the connections between points, much like a public transportation network. Mathematicians have long sought to develop ...
Department of Psychological Sciences, Kansas State University, Manhattan, KS, United States The information processing limitations of the human brain make unaided interpretations of large datasets ...
Scientist Yi Nian is sharing his machine-learning expertise with the world in his latest co-authored publication, “Globally Interpretable Graph Learning via Distribution Matching.” Published by 2024 ...
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