WebMay 29, 2024 · The key idea behind Meta-Graph is that we use gradient-based meta-learning to optimize shared global parameters θ, used to initialize the parameters of the … WebFeb 22, 2024 · Deep learning models for graphs have advanced the state of the art on many tasks. Despite their recent success, little is known about their robustness. We investigate training time attacks on graph neural networks for node classification that perturb the discrete graph structure. Our core principle is to use meta-gradients to solve …
Heterogeneous Graph Contrastive Learning with Meta-Path …
WebNov 3, 2024 · Towards this, we propose a novel graph meta-learning framework -- Meta-GNN -- to tackle the few-shot node classification problem in graph meta-learning … WebNov 1, 2024 · Although meta-learning has been widely used in vision and language domains to address few-shot learning, its adoption on graphs has been limited. In particular, graph nodes in a few-shot task are ... cargo trailer 6x12 craigslist
Graph Meta Learning via Local Subgraphs - Zitnik Lab
WebMoreover, we propose a task-adaptive meta-learning algorithm to provide meta knowledge customization for different tasks in few-shot scenarios. Experiments on multiple real-life … WebJul 9, 2024 · It contains multiple sub-networks corresponding to multiple graphs, learning a unified metric space, where one can easily link entities across different graphs. In addition to the performance lift, Meta-NA greatly improves the anchor linking generalization, significantly reduces the computational overheads, and is easily extendable to multi ... WebFeb 22, 2024 · Few-shot Network Anomaly Detection via Cross-network Meta-learning. Network anomaly detection aims to find network elements (e.g., nodes, edges, subgraphs) with significantly different behaviors from the vast majority. It has a profound impact in a variety of applications ranging from finance, healthcare to social network analysis. cargo trailer brugt