To demonstrate the feasibility and the potential for practical applications of this approach, we present a case study of a 44-year hindcast along the French Basque coast over an unstructured mesh. We introduce two machine learning approaches, a graph neural network and a polynomial ridge ...
In this work, we conduct a novel preliminary study to explore the potential and limitations of polynomial graph filter learning approaches, revealing a severe overfitting issue. To improve the effectiveness of polynomial graph filters, we propose Auto-Polynomial, a novel and general automated ...
Overall, the heat maps of graph measures vs. predictive performance (Figure 4(f)) show that there exist graph structures that can outperform the complete graph (the pixel on bottom right) baselines. The best performing relational graph can outperform the complete graph baseline by 1.4% top-1 e...
markedly altered the spectral power distribution, leading to emergence of PDR (Fig.3). There was a significant difference in the spectral distribution shift induced byαselection compared to the shift induced by structural connectome selection, with a whole-brain Jensen–Shannon divergence difference of...
17 proposed two support vector regression models that use seasonal kernels to measure the similarity between time series examples. This method can accurately predict traffic flow during highly congested periods. The accuracy and stability of these machine learning methods are better than those of ...
Graph Representation is defined as the way of representing a graph using a compressed adjacency list format. In this format, the vertices of the graph are stored in an array and the edges of all vertices are packed into another array. The weights of the edges are stored in a parallel array...
"MHEC: One-shot relational learning of knowledge graphs completion based on multi-hop information enhancement". Neurocomputing 2025. paper (FTMI) Luyi Bai, Shuo Han, Lin Zhu. "Multi-hop interpretable meta learning for few-shot temporal knowledge graph completion". Neural Networks 2025. paper (...
graph twoway lfit — Two-way linear prediction plots Description Options Quick start Remarks and examples Menu Also see Syntax Description twoway lfit calculates the prediction for yvar from a linear regression of yvar on xvar and plots the resulting line. Quick start A linear fit prediction ...
Graph-level tasks include graph classification, graph regression, and graph matching, all of which need the model to learn graph representations. From the perspective of supervision, we can also categorize graph learning tasks into three different training settings: • Supervised setting provides label...
A thorough taxonomy of each stage is presented to answer three critical graph-centric questions: (1) how to enhance graph data availability and quality; (2) how to learn from graph data with limited-availability and low-quality; (3) how to build graph MLOps systems from the graph data-...