WebGraph Sparsification via Meta Learning, Yu Lab, Harvard Medical School. Mar, 2024. Modern Approaches to Classical Selection Problems, Data Science and Engineering … WebMar 17, 2024 · Representation learning on heterogeneous graphs aims to obtain meaningful node representations to facilitate various downstream tasks. Existing heterogeneous graph learning methods are primarily developed by following the propagation mechanism of node representations. There are few efforts on studying the …

Semisance on Twitter

WebJun 10, 2024 · Graph sparsification concerns data reduction where an edge-reduced graph of a similar structure is preferred. Existing methods are mostly sampling-based, which introduce high computation complexity in general and lack of flexibility for a different reduction objective. We present SparRL, the first general and effective reinforcement … WebNov 17, 2024 · Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning pp. 432-441. ... Graph … little earth united tribes

Talks Guihong Wan, PhD

WebAbstract: We present a novel edge sparsification approach for semi-supervised learning on undirected and attributed graphs. The main challenge is to retain few edges while … WebUnder the NeuralSparse framework, supervised graph sparsification could seamlessly connect with existing graph neural networks for more robust performance. Experimental results on both benchmark and private datasets show that NeuralSparse can yield up to 7.2% improvement in testing accuracy when working with existing graph neural networks … Web@inproceedings{Wan2024GraphSV, title={Graph Sparsification via Meta-Learning}, author={Guihong Wan and Harsha Kokel}, year={2024} } Guihong Wan, Harsha Kokel; Published 2024; Computer Science; We present a novel graph sparsification approach for semisupervised learning on undirected attributed graphs. The main challenge is to … little earthworms