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"Improving performance and efficiency of Graph Neural Networks by injec" by Wei Dong, Junsheng Wu et al.

Aggregation functions are regarded as the multiplication between an aggregation matrix and node embeddings, based on which a full rank matrix can enhance representation capacity of Graph Neural Networks (GNNs). In this work, we fill this research gap based on the full rank aggregation matrix and its functional form, i.e., the injective aggregation function, and state that injectivity is necessary to guarantee the rich representation capacity to GNNs. To this end, we conduct theoretical injectivity analysis for the typical feature aggregation methods and provide inspiring solutions on turning the non-injective aggregation functions into injective versions. Based on our injective aggregation functions, we create various GNN structures by combining the aggregation functions with the other ingredient of GNNs, node feature encoding, in different ways. Following these structures, we highlight that by using our injective aggregation function entirely as a pre-processing step before applying i ....

Graph Neural Networks Gnns , Graph Neural Networks , Aggregation Function , Ggregation Matrix , Graph Neural Networks , Raffic State Prediction ,

Industry Compute Cluster Enables Innovative Research and Development for Health and Life Sciences

University-purchased High Performance Computing (HPC) systems are typically funded to support principal investigators and their teams. But in 2014, the Center for Computational Research (CCR) at the University at Buffalo (UB) created a dedicated cluster to give businesses of Western New York access to large-scale computing resources they would either have to build on their own using public cloud services. ....

New York , United States , Ken Strandberg , Mark Ehrensberger , Edward Furlani , Comsol Multiphysics , Priyanshu Vishnoi , Acta Biomater , Brian Peterson , Adrian Levesque , Ben Sainsbury , Uniformed Services University , National Institute Of Allergy , Computational Research , Graph Neural Networks Gnns , Pharmacological Development Through Digital Biology , Breakthrough Devices Program , Life Sciences , National Institutes For Health , University At Buffalo , University Purchased High Performance Computing , Western New York , Artificial Intelligence , Enhanced Pharmacodynamics , Scientific Officer Scott , National Institutes ,