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Everything about Sparse network
In network science, a sparse network has much fewer links than the possible maximum number of links within that network. The study of sparse networks is a relatively new area primarily stimulated by the study of real networks, such as social and computer networks. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Sparse networkA 5-minute orientation
- Skim a recent paper: SUnCNN: Sparse Unmixing Using Unsupervised Convolutional Neural NetworkInfluential work since 2022
- Pick up a classic book: Communications, Signal Processing, and SystemsThe most-reprinted book on the subject
- Read the landmark paper: Sparse Brain Network Recovery Under Compressed SensingCited 157 times
- Go sideways: Configuration modelA closely related topic
- Go sideways: Degree distributionA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Sparse Brain Network Recovery Under Compressed Sensing
Causal Network Inference Via Group Sparse Regularization
SNAP: An Efficient Sparse Neural Acceleration Processor for Unstructured Sparse Deep Neural Network Inference
Message-Passing Algorithms for Sparse Network Alignment
Sparse network identifiability via Compressed Sensing
Network pruning using sparse learning and genetic algorithm
Sparse fully convolutional network for face labeling
Robust and sparse banking network estimation
🆕 Influential since 2022
SUnCNN: Sparse Unmixing Using Unsupervised Convolutional Neural Network
SCNIC: Sparse correlation network investigation for compositional data
Robust echo state network with sparse online learning
Sparse‐Dyn: Sparse dynamic graph multirepresentation learning via event‐based sparse temporal attention network
SRNet: Sparse representation-based network for image denoising
📚 Books worth reading
Communications, Signal Processing, and SystemsQilian Liang · 2012
Bioinformatics Research and ApplicationsZhipeng Cai · 2013
Intelligent Information and Database SystemsAli Selamat · 2013
Fundamentals of data structures in CEllis Horowitz · 1993
Information Processing in Medical ImagingJames C. Gee · 2013
An introduction to data structures with applicationsJean-Paul Tremblay, Paul Gordon Sorenson · 1976
Information Theory, Inference & Learning AlgorithmsDavid J.C. MacKay · 2003
Advances in Knowledge Discovery and Data MiningJian Pei · 2013
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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