Zobrazeno 1 - 10
of 421
pro vyhledávání: '"Wang, Xiyuan"'
Various graph neural networks (GNNs) with advanced training techniques and model designs have been proposed for link prediction tasks. However, outdated baseline models may lead to an overestimation of the benefits provided by these novel approaches.
Externí odkaz:
http://arxiv.org/abs/2411.03845
Graph neural networks (GNNs) have achieved remarkable success in a variety of machine learning tasks over graph data. Existing GNNs usually rely on message passing, i.e., computing node representations by gathering information from the neighborhood,
Externí odkaz:
http://arxiv.org/abs/2410.09737
Recent advancements in molecular generative models have demonstrated substantial potential in accelerating scientific discovery, particularly in drug design. However, these models often face challenges in generating high-quality molecules, especially
Externí odkaz:
http://arxiv.org/abs/2410.03655
Temporal graphs are ubiquitous in real-world scenarios, such as social network, trade and transportation. Predicting dynamic links between nodes in a temporal graph is of vital importance. Traditional methods usually leverage the temporal neighborhoo
Externí odkaz:
http://arxiv.org/abs/2406.07926
Let $(\rho_\lambda\colon G_{\mathbb Q}\to \operatorname{GL}_5(\overline{E}_\lambda))_\lambda$ be a strictly compatible system of Galois representations such that no Hodge--Tate weight has multiplicity $5$. We show that if $\rho_{\lambda_0}$ is irredu
Externí odkaz:
http://arxiv.org/abs/2406.03617
Graph is a fundamental data structure to model interconnections between entities. Set, on the contrary, stores independent elements. To learn graph representations, current Graph Neural Networks (GNNs) primarily use message passing to encode the inte
Externí odkaz:
http://arxiv.org/abs/2405.02795
Invariant models, one important class of geometric deep learning models, are capable of generating meaningful geometric representations by leveraging informative geometric features in point clouds. These models are characterized by their simplicity,
Externí odkaz:
http://arxiv.org/abs/2402.04836
In this paper, we propose the first framework that enables solving graph learning tasks of all levels (node, edge and graph) and all types (generation, regression and classification) using one formulation. We first formulate prediction tasks includin
Externí odkaz:
http://arxiv.org/abs/2402.02518
Node-level random walk has been widely used to improve Graph Neural Networks. However, there is limited attention to random walk on edge and, more generally, on $k$-simplices. This paper systematically analyzes how random walk on different orders of
Externí odkaz:
http://arxiv.org/abs/2310.19285
We establish several refined strong multiplicity one results for paramodular cusp forms by using the spinor and standard $L$-functions with the combination of the methods from both of automorphic side and Galois side.
Comment: 28 pages
Comment: 28 pages
Externí odkaz:
http://arxiv.org/abs/2310.17144