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- SGAT: Simplicial Graph Attention Network32
Simplicial Graph Attention Network is presented, a simplicial complex approach to represent high-order interactions involving multiple nodes or edges by placing features from non-target nodes on the simplices and using attention mechanisms and upper adjacencies to generate representations.
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- Meta: Graph-Encoded Printed Circuit Board Datasets for Component Classification With Graph Neural Networks–
A graph-based framework for printed circuit board (PCB) image analysis, targeting core hardware assurance tasks such as integrated circuit segmentation and component identification, and establishing GraphPCB as a new testbed for node classification in structured visual domains.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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