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You are here		swethatanamala.github.io Introduction to Graph theory · Swetha's Blog
\|	\|	11011110.github.io Bandwidth vs breadth-first search	5.2 parsecs away Travel
\|	\|	Another new preprint of mine recently appeared: "Bandwidth vs BFS width in matrix reordering, graph reconstruction, and graph drawing", arXiv:2505.10789, wit...	5.2 parsecs away Travel
\|	\|	research.google Robust Graph Neural Networks	3.7 parsecs away Travel
\|	\|	Posted by Bryan Perozzi, Research Scientist and Qi Zhu, Research Intern, Google Research Graph Neural Networks (GNNs) are powerful tools for levera...	3.7 parsecs away Travel
\|	\|	www.simula.no Enabling Graph Neural Networks at Exascale \| Simula	2.5 parsecs away Travel
\|	\|	Graph neural networks (GNNs), which extend the successful ideas of deep learning to irregularly structured data, are a recent addition to the field of artificial intelligence. While traditional deep learning has focused on regular inputs such as images composed of pixels in two-dimensional space, graph neural networks can analyze and learn from unstructured connections between objects. This gives GNNs the ability to tackle completely new classes of problems, such as analyzing social networks and power grids or uncovering molecule structures in computational chemistry.	2.5 parsecs away Travel
\|	\|	jakeseliger.com 2012 October 09 « The Story's Story	30.8 parsecs away Travel
\|		1 post published by Jake Seliger on October 9, 2012	30.8 parsecs away Travel