Results 21 to 30 of about 2,805,373 (250)
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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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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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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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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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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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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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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Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014 Sampling is a standard approach in big-graph analytics; the goal is to efficiently estimate the graph properties by consulting a sample of the whole population. A perfect sample is assumed to mirror every property of the whole population. Unfortunately, such a perfect sample is hard to collect in complex populations such as graphs (e.g.
Nesreen K. Ahmed +3 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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Quantitative estimation of sampling uncertainties for mycotoxins in cereal shipments [PDF]
, 2012 Many countries receive shipments of bulk cereals from primary producers. There is a volume of work that is ongoing that seeks to arrive at appropriate standards for the quality of the shipments and the means to assess the shipments as they are out-loaded.
Bourgeois, Florent +2 more
core +1 more source