Results 21 to 30 of about 184,942 (303)
European Journal of Remote Sensing, 2020 In recent years, deep learning has drawn increasing attention in the field of hyperspectral remote sensing image classification and has achieved great success.
Xibing Zuo +5 more
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SAGES: Scalable Attributed Graph Embedding With Sampling for Unsupervised Learning
, 2023 Unsupervised graph embedding method generates node embeddings to preserve structural and content features in a graph without human labeling burden. However, most unsupervised graph representation learning methods suffer issues like poor scalability or ...
Wang, Jialin +5 more
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Graph Sampling with Distributed In-Memory Dataflow Systems
, 2021 Given a large graph, graph sampling determines a subgraph with similar characteristics for certain metrics of the original graph. The samples are much smaller thereby accelerating and simplifying the analysis and visualization of large graphs.
Rostami, M. Ali +4 more
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LeL-GNN: Learnable Edge Sampling and Line Based Graph Neural Network for Link Prediction
IEEE Access, 2023 Graph neural networks lose a lot of their computing power when more network layers are added. As a result, the majority of existing graph neural networks have a shallow depth of learning. Over-smoothing and information loss are two of the key issues that
Md Golam Morshed +2 more
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Boosting Graph Contrastive Learning via Adaptive Sampling
, 2023 Contrastive learning (CL) is a prominent technique for self-supervised representation learning, which aims to contrast semantically similar (i.e., positive) and dissimilar (i.e., negative) pairs of examples under different augmented views.
Chen Gong +13 more
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Graph signal processing based pilot pattern design and channel estimation for OFDM system
物联网学报, 2022 Orthogonal frequency division multiplexing (OFDM) is one of the key technologies in the physical layer of the internet of things (IoT).Pilot design and channel estimation are key issues in OFDM systems.In view of the problem of performance loss by fixed ...
Bin HE +3 more
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Quantization-aware sampling set selection for bandlimited graph signals
EURASIP Journal on Advances in Signal Processing, 2022 We consider a scenario in which nodes of a graph are sampled for bandlimited graph signals which are uniformly quantized with optimal rate and original signals are reconstructed from the quantized signal values residing on the nodes in the sampling set ...
Yoon Hak Kim
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B2-Sampling: Fusing Balanced and Biased Sampling for Graph Contrastive Learning
, 2023 Graph contrastive learning (GCL), aiming for an embedding space where semantically similar nodes are closer, has been widely applied in graph-structured data.
Liu, J +5 more
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EOD Edge Sampling for Visualizing Dynamic Network via Massive Sequence View
IEEE Access, 2018 Dynamic network visualization is crucial to understand network evolving behavior. Massive sequence view (MSV) is a classic technique for visualizing dynamic networks and provides users with a fine-grained presentation of time-varying communication trend ...
Ying Zhao +7 more
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Sequential Sampling and Estimation of Approximately Bandlimited Graph Signals
Sensors, 2021 Graph signal sampling has been widely studied in recent years, but the accurate signal models required by most of the existing sampling methods are usually unavailable prior to any observations made in a practical environment. In this paper, a sequential
Sijie Lin, Ke Xu, Hui Feng, Bo Hu
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