Graph partitioning is commonly used for dividing graph data for parallel processing. While they achieve good performance for the traditional graph processing algorithms, the existing graph partitioning methods are unsatisfactory for data-parallel GNN ...
Graph Neural Network based on edges is introduced in this paper and is used to recognize the English uppercase alphabets treating their corresponding graphs as semigraphs. Graph Neural Network(GNN) is a connectionist model comprising of two feedforward ...
Graphs have been widely adopted in various fields, where many graph models are developed. Most of previous research focuses on unipartite or homogeneous graph analysis. In this graphs, the relationships between the same type of entities are ...
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