Graph sparsification via meta learning
WebJan 7, 2024 · MGAE has two core designs. First, we find that masking a high ratio of the input graph structure, e.g., $70\%$, yields a nontrivial and meaningful self-supervisory task that benefits downstream ... WebDec 2, 2024 · A Generic Graph Sparsification Framework using Deep Reinforcement Learning. The interconnectedness and interdependence of modern graphs are growing …
Graph sparsification via meta learning
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WebTalk 2: Graph Sparsification via Meta-Learning . Guihong Wan, Harsha Kokel. 15:00-15:15 Coffee Break/Social Networking: 15:15-15:45: Keynote talk 8 : Learning Symbolic Logic Rules for Reasoning on Knowledge Graphs. Abstract: In this talk, I am going to introduce our latest progress on learning logic rules for reasoning on knowledge graphs. WebAdversarial Attacks on Graph Neural Networks via Meta Learning. Daniel Zugner, Stephan Gunnemann. ICLR 2024. Attacking Graph Convolutional Networks via Rewiring. ... Robust Graph Representation Learning via Neural Sparsification. ICML 2024. Robust Collective Classification against Structural Attacks. Kai Zhou, Yevgeniy Vorobeychik. UAI 2024.
WebWe present a novel edge sparsification approach for semi-supervised learning on undirected and attributed graphs. The main challenge is to retain few edges while … 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 minimizing the loss of node classification accuracy. The task can be mathematically formulated as a bi-level optimization problem. We propose to use meta-gradients, which ...
WebOct 13, 2024 · Graphs are ubiquitous across the globe and within science and engineering. Some powerful classifiers are proposed to classify nodes in graphs, such as Graph Convolutional Networks (GCNs). However, as graphs are growing in size, node classification on large graphs can be space and time consuming due to using whole … WebJun 11, 2024 · Improving the Robustness of Graphs through Reinforcement Learning and Graph Neural Networks. arXiv:2001.11279 [cs.LG] Google Scholar. Wai Shing Fung, …
WebGraph Sparsification via Meta-Learning Guihong Wan, Harsha Kokel The University of Texas at Dallas 800 W. Campbell Road, Richardson, Texas 75080 {Guihong.Wan, …
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 … howellyhttp://bytemeta.vip/index.php/repo/extreme-assistant/ECCV2024-Paper-Code-Interpretation howell wrestling tournamentWebDeep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning: SJTU: ICML 🎓: 2024: GAMF 3 : Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting kg. ZJU: IJCAI 🎓: 2024: MaKEr 4 : Personalized Federated Learning With a Graph: UTS: IJCAI 🎓: 2024: SFL 5 hideaway folding wall mounted tableWebFeb 6, 2024 · In this letter, we propose an algorithm for learning a sparse weighted graph by estimating its adjacency matrix under the assumption that the observed signals vary … howell youtubeWebIn this paper, we present NeuralSparse, a supervised graph sparsification technique that improves generalization power by learning to remove potentially task-irrelevant edges … howell youth soccerWebmeaningful neighborhoods from noisy graphs. Sparsification.Most graph diffusions result in a dense matrix S. This happens even if we do not sum to k= ∞in Eq. 1 due to the “four/six degrees of separation” in real-world graphs [5]. However, the values in S represent the influence between all pairs of nodes, which typically are highly ... hideaway fontWebMay 31, 2024 · Graph sparsification aims to reduce the number of edges of a graph while maintaining its structural properties. In this paper, we propose the first general and effective information-theoretic formulation of graph sparsification, by taking inspiration from the Principle of Relevant Information (PRI). To this end, we extend the PRI from a standard … hideaway folding teak desk