Results 71 to 80 of about 2,563,850 (352)
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud [PDF]
Computer Vision and Pattern Recognition, 2020 In this paper, we propose a graph neural network to detect objects from a LiDAR point cloud. Towards this end, we encode the point cloud efficiently in a fixed radius near-neighbors graph. We design a graph neural network, named Point-GNN, to predict the
Weijing Shi, R. Rajkumar
semanticscholar +1 more source
On finite subnormal Cayley graphs [PDF]
arXiv, 2021 In this paper we introduce and study a type of Cayley graph -- subnormal Cayley graph. We prove that a subnormal 2-arc transitive Cayley graph is a normal Cayley graph or a normal cover of a complete bipartite graph $K_{p^d,p^d}$ with $p$ prime.
arxiv
Attribute and Structure Preserving Graph Contrastive Learning
AAAI Conference on Artificial Intelligence, 2023 Graph Contrastive Learning (GCL) has drawn much research interest due to its strong ability to capture both graph structure and node attribute information in a self-supervised manner.
Jialu Chen, Gang Kou
semanticscholar +1 more source
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters [PDF]
International Conference on Information and Knowledge Management, 2020 Graph Neural Networks (GNNs) have been widely applied to fraud detection problems in recent years, revealing the suspiciousness of nodes by aggregating their neighborhood information via different relations.
Yingtong Dou +5 more
semanticscholar +1 more source
Nature Communications, 2021 To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data.
Tongxin Wang +6 more
semanticscholar +1 more source
Key technologies and research progress of medical knowledge graph construction
大数据, 2021 With the continuous iterative updating of Internet technology, the semantic understanding of massive data is becoming more and more important.Knowledge graph is a kind of semantic network that reveals the relationship between entities.Medicine is one of ...
Ling TAN +10 more
doaj
Deep Learning with Word Embedding Improves Kazakh Named-Entity Recognition
Information, 2022 Named-entity recognition (NER) is a preliminary step for several text extraction tasks. In this work, we try to recognize Kazakh named entities by introducing a hybrid neural network model that leverages word semantics with multidimensional features and ...
Gulizada Haisa, Gulila Altenbek
doaj +1 more source
Named Graphs as a Mechanism for Reasoning About Provenance [PDF]
, 2006 Named Graphs is a simple, compatible extension to the RDF abstract syntax that enables statements to be made about RDF graphs. This approach is in contrast to earlier attempts such as RDF reification, or knowledge-base specific extensions including quads and contexts.
Watkins, E. Rowland, Nicole, Denis A.
openaire +3 more sources
REDEN: Named Entity Linking in Digital Literary Editions Using Linked Data Sets
Complex Systems Informatics and Modeling Quarterly, 2016 This paper proposes a graph-based Named Entity Linking (NEL) algorithm named REDEN for the disambiguation of authors’ names in French literary criticism texts and scientific essays from the 19th and early 20th centuries.
Carmen Brando +2 more
doaj +1 more source
Graph Hybrid Summarization [PDF]
Journal of Artificial Intelligence and Data Mining, 2018 One solution to process and analysis of massive graphs is summarization. Generating a high quality summary is the main challenge of graph summarization.
N. Ashrafi Payaman, M.R. Kangavari
doaj +1 more source