Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different ...

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Skoltech and MIPT researchers have sped up the search for high-performance metal alloys for the aerospace industry, ...

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Advances like these lead me to believe that useful quantum computing is inevitable and increasingly imminent. And that’s good ...

Graph theory is an integral component of algorithm design that underlies sparse matrices, relational databases, and networks. Improving the performance of graph algorithms has direct implications to ...

Aurora has a modular design that consists of four similar units, each installed in a standard server rack that is slightly ...

The experimental results show that the performance of the LGNN algorithm in some tasks is slightly ... especially when dealing with large-scale and sparse graph data. This may be because the model ...

GCN, a groundbreaking disentangled graph convolutional network that dynamically adjusts feature channels for enhanced node ...

Layout algorithms can reduce edge crossings and shorten edges using ... In a case study visualizing 13 control flow graphs, most with over 1000 nodes, we show that our method can be employed to create ...