Abstract: Graph neural networks (GNNs) have emerged as a powerful framework for a wide range of node-level graph learning tasks. However, their performance typically depends on random or minimally ...
Abstract: Graph matching aims to establish node correspondences between graphs, which is a classic combinatorial optimization problem. In recent years, (deep) learning-based methods have emerged as a ...
FastNoise2 is built around a node graph architecture. Rather than calling standalone functions to generate noise, you build a tree of interconnected nodes, then evaluate the root node to get the final ...
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