Results 1 to 10 of about 533,476 (267)
Text-Graph Enhanced Knowledge Graph Representation Learning [PDF]
Frontiers in Artificial Intelligence, 2021 Knowledge Graphs (KGs) such as Freebase and YAGO have been widely adopted in a variety of NLP tasks. Representation learning of Knowledge Graphs (KGs) aims to map entities and relationships into a continuous low-dimensional vector space.
Linmei Hu +6 more
doaj +5 more sources
Graph Geometric Algebra networks for graph representation learning [PDF]
Scientific ReportsGraph neural networks (GNNs) have emerged as a prominent approach for capturing graph topology and modeling vertex-to-vertex relationships. They have been widely used in pattern recognition tasks including node and graph label prediction.
Jianqi Zhong, Wenming Cao
doaj +4 more sources
Graph Representation Learning and Its Applications: A Survey [PDF]
Sensors, 2023 Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc.
Van Thuy Hoang +5 more
doaj +2 more sources
Graph representation learning for structural proteomics. [PDF]
Emerg Top Life Sci, 2021 The field of structural proteomics, which is focused on studying the structure–function relationship of proteins and protein complexes, is experiencing rapid growth. Since the early 2000s, structural databases such as the Protein Data Bank are storing increasing amounts of protein structural data, in addition to modeled structures becoming increasingly
Fasoulis R, Paliouras G, Kavraki LE.
europepmc +3 more sources
Graph Representation Learning on Street Networks
ISPRS International Journal of Geo-Information, 2023 Street networks provide an invaluable source of information about the different temporal and spatial patterns emerging in our cities. These streets are often represented as graphs where intersections are modeled as nodes and streets as edges between them.
Mateo Neira, Roberto Murcio
doaj +3 more sources
ESANN 2023 proceesdings, 2023 AbstractGraph structure, which can represent objects and their relationships, is ubiquitous in big data including natural languages. Besides original text as a sequence of word tokens, massive additional information in NLP is in the graph structure, such as syntactic relations between words in a sentence, hyperlink relations between documents, and ...
Cheng Yang +3 more
openaire +4 more sources
Temporal Knowledge Graph Representation Learning [PDF]
Jisuanji kexue, 2022 As a structured form of human knowledge,knowledge graphs have played a great supportive role in supporting the semantic intercommunication of massive,multi-source,heterogeneous data,and effectively support tasks such as data analysis,attracting the ...
XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai
doaj +1 more source
Learning Graph Representations [PDF]
, 2021 Social and information networks are gaining huge popularity recently due to their various applications. Knowledge representation through graphs in the form of nodes and edges should preserve as many characteristics of the original data as possible.
Rucha Bhalchandra Joshi +1 more
openaire +2 more sources
Self-supervised Dynamic Graph Representation Learning Approach Based on Contrastive Prediction [PDF]
Jisuanji kexue, 2023 In recent years,graph self-supervised learning represented by graph contrastive learning has become a hot research to-pic in the field of graph learning.This learning paradigm does not depend on node labels and has good generalization ability.However ...
JIANG Linpu, CHEN Kejia
doaj +1 more source
An End-to-End Multiplex Graph Neural Network for Graph Representation Learning
IEEE Access, 2021 Research on graph classification tasks based on graph neural networks has attracted wide attention. The graphs to be classified may have various graph sizes (i.e., different numbers of nodes and edges) and have various graph properties (e.g., average ...
Yanyan Liang +3 more
doaj +1 more source