Results 71 to 80 of about 2,836,177 (344)
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition [PDF]
Computer Vision and Pattern Recognition, 2020 Spatial-temporal graphs have been widely used by skeleton-based action recognition algorithms to model human action dynamics. To capture robust movement patterns from these graphs, long-range and multi-scale context aggregation and spatial-temporal ...
Ken Ziyu Liu +4 more
semanticscholar +1 more source
Towards the Temporal Streaming of Graph Data on Distributed Ledgers [PDF]
, 2017 We present our work-in-progress on handling temporal RDF graph data using the Ethereum distributed ledger. The motivation for this work are scenarios where multiple distributed consumers of streamed data may need or wish to verify that data has not been ...
Bastianelli, Emanuele +4 more
core +1 more source
Predicting traffic propagation flow in urban road network with multi-graph convolutional network
Complex & Intelligent Systems, 2023 Traffic volume propagating from upstream road link to downstream road link is the key parameter for designing intersection signal timing scheme. Recent works successfully used graph convolutional network (GCN) and specific time-series model to forecast ...
Haiqiang Yang, Zihan Li, Yashuai Qi
semanticscholar +1 more source
BMC Bioinformatics, 2022 Background Automatic and accurate recognition of various biomedical named entities from literature is an important task of biomedical text mining, which is the foundation of extracting biomedical knowledge from unstructured texts into structured formats.
Xiangwen Zheng +5 more
doaj +1 more source
Bilinear Graph Neural Network with Neighbor Interactions
, 2020 Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. Existing efforts on GNN have largely defined the graph convolution as a weighted sum of the features of the connected nodes to form the ...
Feng, Fuli +6 more
core +1 more source
Graph Data Condensation via Self-expressive Graph Structure Reconstruction [PDF]
Knowledge Discovery and Data MiningWith the increasing demands of training graph neural networks (GNNs) on large-scale graphs, graph data condensation has emerged as a critical technique to relieve the storage and time costs during the training phase.
Zhanyu Liu, Chaolv Zeng, Guanjie Zheng
semanticscholar +1 more source
Name Disambiguation in Anonymized Graphs using Network Embedding [PDF]
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017 The 26th ACM International Conference on Information and Knowledge Management (CIKM 2017) research track full ...
Mohammad Al Hasan, Baichuan Zhang
openaire +3 more sources
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
Complexity of some special named graphs with double edges [PDF]
Journal of Taibah University for Science, 2014 AbstractIn mathematics, one always tries to get new structures from given ones. This also applies to the realm of graphs, where one can generate many new graphs from a given set of graphs. In this paper we derive simple formulas of the complexity, number of spanning trees, of Some Special named Graphs with double edges such as Fan, Wheel and Mobius ...
Kamel Mohamed, S. N. Daoud, S. N. Daoud
openaire +2 more sources
FEBS Letters, EarlyView.Tuberculosis remains a global health challenge and new therapeutic targets are required. Here, we characterized SseA, a sulfurtransferase from Mycobacterium tuberculosis involved in macrophage infection, and its interaction with the newly identified protein SufEMtb that activates SseA enzymatic activity.
Giulia Di Napoli +10 more
wiley +1 more source