Zobrazeno 1 - 10
of 31 146
pro vyhledávání: '"LI, RONG"'
Multi-view clustering can partition data samples into their categories by learning a consensus representation in an unsupervised way and has received more and more attention in recent years. However, there is an untrusted fusion problem. The reasons
Externí odkaz:
http://arxiv.org/abs/2412.16487
Counting the number of $(p, q)$-bicliques (complete bipartite subgraphs) in a bipartite graph is a fundamental problem which plays a crucial role in numerous bipartite graph analysis applications. However, existing algorithms for counting $(p, q)$-bi
Externí odkaz:
http://arxiv.org/abs/2412.16485
With the increasing prevalence of cross-domain Text-Attributed Graph (TAG) Data (e.g., citation networks, recommendation systems, social networks, and ai4science), the integration of Graph Neural Networks (GNNs) and Large Language Models (LLMs) into
Externí odkaz:
http://arxiv.org/abs/2412.12456
Autor:
He, Hao-Ning, Kido, Eiji, Duan, Kai-Kai, Yang, Yang, Higuchi, Ryo, Fan, Yi-Zhong, Wang, Tao, Jiang, Lu-Yao, Li, Rong-Lan, Zhu, Ben-Yang, Li, Xiang, Xia, Zi-Qing, Nagataki, Shigehiro, Wei, Da-Ming, Kusenko, Alexander
Ultrahigh-energy cosmic rays (UHECRs) are the highest energy messenger from space, with energies exceeding 1 EeV. Although UHECRs were discovered over 60 years ago, their origin still remains a mystery. Pinpointing sources of UHECRs is crucial for un
Externí odkaz:
http://arxiv.org/abs/2412.11966
Graph propagation (GP) computation plays a crucial role in graph data analysis, supporting various applications such as graph node similarity queries, graph node ranking, graph clustering, and graph neural networks. Existing methods, mainly relying o
Externí odkaz:
http://arxiv.org/abs/2412.10789
3D Visual Grounding (3DVG) aims to locate objects in 3D scenes based on textual descriptions, which is essential for applications like augmented reality and robotics. Traditional 3DVG approaches rely on annotated 3D datasets and predefined object cat
Externí odkaz:
http://arxiv.org/abs/2412.04383
In recent years, Graph Neural Networks (GNNs) have made significant advances in processing structured data. However, most of them primarily adopted a model-centric approach, which simplifies graphs by converting them into undirected formats and empha
Externí odkaz:
http://arxiv.org/abs/2412.01849
Autor:
Li, Rong-Lan, Yuan, Chengchao, He, Hao-Ning, Wang, Yun, Zhu, Ben-Yang, Liang, Yun-Feng, Jiang, Ning, Wei, Da-Ming
Tidal disruption events (TDEs), where stars are captured or tidally disrupted by supermassive black holes, are potential sources of high-energy neutrinos. We report the discovery of a potential neutrino flare that is spatially and temporally associat
Externí odkaz:
http://arxiv.org/abs/2411.06440
Recently, graph neural network (GNN) has emerged as a powerful representation learning tool for graph-structured data. However, most approaches are tailored for undirected graphs, neglecting the abundant information embedded in the edges of directed
Externí odkaz:
http://arxiv.org/abs/2410.10320
Autor:
Li, Xunkai, Zhu, Yinlin, Pang, Boyang, Yan, Guochen, Yan, Yeyu, Li, Zening, Wu, Zhengyu, Zhang, Wentao, Li, Rong-Hua, Wang, Guoren
Federated graph learning (FGL) has emerged as a promising distributed training paradigm for graph neural networks across multiple local systems without direct data sharing. This approach is particularly beneficial in privacy-sensitive scenarios and o
Externí odkaz:
http://arxiv.org/abs/2408.16288