Zobrazeno 1 - 10
of 640
pro vyhledávání: '"Liu, JiaHong"'
Unsupervised graph-level anomaly detection (UGAD) has garnered increasing attention in recent years due to its significance. Most existing methods that rely on traditional GNNs mainly consider pairwise relationships between first-order neighbors, whi
Externí odkaz:
http://arxiv.org/abs/2407.02057
Hyperbolic geometry have shown significant potential in modeling complex structured data, particularly those with underlying tree-like and hierarchical structures. Despite the impressive performance of various hyperbolic neural networks across numero
Externí odkaz:
http://arxiv.org/abs/2407.01290
Learning good self-supervised graph representations that are beneficial to downstream tasks is challenging. Among a variety of methods, contrastive learning enjoys competitive performance. The embeddings of contrastive learning are arranged on a hype
Externí odkaz:
http://arxiv.org/abs/2310.18209
Publikováno v:
Cailiao gongcheng, Vol 52, Iss 6, Pp 59-68 (2024)
Metal oxides are often used as electrode materials for supercapacitors because of their high capacity， low cost， suitability for commercialization， and environmental friendliness. In this study， Mn-MOF was used as a precursor and placed in et
Externí odkaz:
https://doaj.org/article/be99bb46f8f54edcb7dd013ffa25dea7
Considering the prevalence of the power-law distribution in user-item networks, hyperbolic space has attracted considerable attention and achieved impressive performance in the recommender system recently. The advantage of hyperbolic recommendation l
Externí odkaz:
http://arxiv.org/abs/2207.09051
Autor:
Liu, Jiahong, Zhou, Min, Fournier-Viger, Philippe, Yang, Menglin, Pan, Lujia, Nouioua, Mourad
Graphs are a popular data type found in many domains. Numerous techniques have been proposed to find interesting patterns in graphs to help understand the data and support decision-making. However, there are generally two limitations that hinder thei
Externí odkaz:
http://arxiv.org/abs/2204.12704
In large-scale recommender systems, the user-item networks are generally scale-free or expand exponentially. The latent features (also known as embeddings) used to describe the user and item are determined by how well the embedding space fits the dat
Externí odkaz:
http://arxiv.org/abs/2204.08176
Publikováno v:
In Journal of Hydrology: Regional Studies December 2024 56
Autor:
Dong, Lirong, Liu, Jiahong, Zhou, Jinjun, Mei, Chao, Wang, Hao, Wang, Jia, Shi, Hongyuan, Nazli, Sana
Publikováno v:
In Journal of Hydrology: Regional Studies December 2024 56
Publikováno v:
In Journal of Cleaner Production 1 November 2024 478