WebOverall, GRB serves as a scalable, unified, modular, and reproducible benchmark on evaluating the adversarial robustness of GML models. It is designed to facilitate the … WebEvaluating Graph Vulnerability and Robustness using TIGER: ⚙ Toolbox: 📝 arXiv‘2024: TIGER: 2024: 147: Graph Robustness Benchmark: Rethinking and Benchmarking Adversarial Robustness of Graph Neural Networks: ⚙ Toolbox: 📝 NeurIPS'2024: Graph Robustness Benchmark (GRB) 2024
arXiv:2202.08057v1 [cs.LG] 16 Feb 2024
WebGraph robustness or network robustness is the ability that a graph or a network preserves its connectivity or other properties after the loss of vertices and edges, which has been a central problem in the research of complex networks. In this paper, we introduce the Modified Zagreb index and Modified Zagreb index centrality as novel measures to study … WebMoreover, OGB-LSC datasets were deployed at ACM KDD Cup 2024 and attracted more than 500 team registrations globally, during which significant performance improvements were made by a variety of innovative techniques. We summarize the common techniques used by the winning solutions and highlight the current best practices in large-scale … imperial tools hvac
Ben Edelman - Principal Consultant - Dun & Bradstreet LinkedIn
WebG-XAI Bench provides comprehensive programmatic functionality in the form of data processing functions, GNN model implementations, collections of synthetic and real … WebApr 20, 2024 · Recently, graph convolutional networks (GCNs) have shown to be vulnerable to small adversarial perturbations, which becomes a severe threat and largely limits their applications in security-critical scenarios. To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against adversarial … WebJun 28, 2024 · Designing benchmarks is highly challenging as we must make robust decisions for coding framework, experimental settings and appropriate datasets. The … imperial tools halfords