Graph Regression
Graph regression uses graph neural networks (GNNs) to predict continuous values associated with nodes or graphs, addressing challenges in diverse fields like materials science and power grid optimization. Current research emphasizes improving GNN architectures, such as incorporating attention mechanisms, Hodge Laplacians, and Kolmogorov-Arnold networks, to enhance predictive accuracy and efficiency, particularly for large and complex graphs. These advancements are driving progress in applications requiring the analysis of relational data with continuous outputs, leading to more accurate predictions and efficient solutions in various scientific and engineering domains.
Papers
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