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2VOSviewer - Visualizing scientific landscapes
https://www.vosviewer.com/
2 лет назад , @becker
network
visualization
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1Understanding Convolutions on Graphs
https://distill.pub/2021/understanding-gnns/
3 лет назад , @becker
graph
convolution
neural
network
nn
gcn
gnn
toread
graphconvolutionneuralnetworknngcngnntoread
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1LOGML - Marinka Zitnik: Graph Representation Learning for Biomedical Discovery - YouTube
https://www.youtube.com/watch?v=hOfcOak-moU&t=1s
3 лет назад , @becker
data
dataset
graph
representation
learning
biomedical
medical
discovery
subgraph
graphs
machine
network
networks
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1HipGraph/Force2Vec: Implementation of Force2Vec method for ICDM 2020 paper titled "Force2Vec: Parallel force-directed graph embedding"
https://github.com/HipGraph/Force2Vec
4 лет назад , @becker
force
graph
network
embeddng
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parallel
python
multicore
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forcegraphnetworkembeddnglayoutefficientparallelpythonmulticoremulticoredirected
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1WangYueFt/dgcnn
https://github.com/WangYueFt/dgcnn
4 лет назад , @becker
dgcnn
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3Subgroup generalization and fairness of graph neural networks
J. Ma, J. Deng, и Q. Mei. Advances in Neural Information Processing Systems, (2021)
2 лет назад , @becker
neurips
subgroup
subgroups
fairness
graph
neural
network
gnn
nn
themis
citedby:scholar:count:22
citedby:scholar:timestamp:2022-12-13
neuripssubgroupsubgroupsfairnessgraphneuralnetworkgnnnnthemiscitedby:scholar:count:22citedby:scholar:timestamp:2022-12-13
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2A gesture system for graph visualization in virtual reality environments
Y. Huang, T. Fujiwara, Y. Lin, W. Lin, и K. Ma. 2017 ieee pacific visualization symposium (pacificvis), стр. 41--45. IEEE, (2017)
2 лет назад , @becker
immermind
gesture
graph
visualization
virtual
reality
network
citedby:scholar:count:40
citedby:scholar:timestamp:2022-11-25
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m:id:immermind:node
m:id:immermind:coordinate:x:-515.505973815918
m:id:immermind:coordinate:y:-42.4267578125
immermindgesturegraphvisualizationvirtualrealitynetworkcitedby:scholar:count:40citedby:scholar:timestamp:2022-11-25m:m:id:immermind:nodem:id:immermind:coordinate:x:-515.505973815918m:id:immermind:coordinate:y:-42.4267578125
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2A generalization of transformer networks to graphs
V. Dwivedi, и X. Bresson. arXiv preprint arXiv:2012.09699, (2020)
2 лет назад , @becker
citedby:scholar:count:85
citedby:scholar:timestamp:2022-6-29
generalization
graph
graphs
network
neural
toread
transformer
citedby:scholar:count:85citedby:scholar:timestamp:2022-6-29generalizationgraphgraphsnetworkneuraltoreadtransformer
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1A biomedical open knowledge network harnesses the power of AI to understand deep human biology
S. Baranzini, K. Börner, J. Morris, C. Nelson, K. Soman, E. Schleimer, M. Keiser, M. Musen, R. Pearce, T. Reza и 1 other автор(ы). Ai Magazine, 43 (1): 46--58 (2022)
2 лет назад , @becker
ai
background
biology
biomedical
citedby:scholar:count:1
citedby:scholar:timestamp:2022-6-21
graph
human
knowledge
learning
machine
network
open
aibackgroundbiologybiomedicalcitedby:scholar:count:1citedby:scholar:timestamp:2022-6-21graphhumanknowledgelearningmachinenetworkopen
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2GreaseLM: Graph REASoning Enhanced Language Models
X. Zhang, A. Bosselut, M. Yasunaga, H. Ren, P. Liang, C. Manning, и J. Leskovec. International Conference on Learning Representations, (2021)
2 лет назад , @becker
toread
knowledge
graph
reasoning
neural
network
language
model
nlp
jure
toreadknowledgegraphreasoningneuralnetworklanguagemodelnlpjure
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1A knowledge graph to interpret clinical proteomics data
A. Santos, A. Colaco, A. Nielsen, L. Niu, M. Strauss, P. Geyer, F. Coscia, N. Albrechtsen, F. Mundt, L. Jensen и 1 other автор(ы). Nature Biotechnology, (2022)
2 лет назад , @becker
knowledge
graph
background
clinical
medicine
proteomics
data
network
domain
knowledgegraphbackgroundclinicalmedicineproteomicsdatanetworkdomain
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3Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
M. Jin, Y. Zheng, Y. Li, C. Gong, C. Zhou, и S. Pan. arXiv preprint arXiv:2105.05682, (2021)
3 лет назад , @becker
graph
neural
network
self
supervised
representation
learning
gnn
multi
scale
contrastive
siamese
citedby:scholar:count:6
citedby:scholar:timestamp:2021-10-17
graphneuralnetworkselfsupervisedrepresentationlearninggnnmultiscalecontrastivesiamesecitedby:scholar:count:6citedby:scholar:timestamp:2021-10-17
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1scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
J. Wang, A. Ma, Y. Chang, J. Gong, Y. Jiang, R. Qi, C. Wang, H. Fu, Q. Ma, и D. Xu. Nature communications, 12 (1): 1--11 (2021)
3 лет назад , @becker
single
cell
gnn
graph
neural
network
rna
seq
rna-seq
paper:fastcor
citedby:scholar:count:3
citedby:scholar:timestamp:2021-6-4
singlecellgnngraphneuralnetworkrnaseqrna-seqpaper:fastcorcitedby:scholar:count:3citedby:scholar:timestamp:2021-6-4
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2Generalized hypergeometric ensembles: Statistical hypothesis testing in complex networks
G. Casiraghi, V. Nanumyan, I. Scholtes, и F. Schweitzer. arXiv preprint arXiv:1607.02441, (2016)
3 лет назад , @becker
citedby:scholar:count:15
citedby:scholar:timestamp:2021-4-15
ensemble
geometric
graph
graphs
hyper
hyptrails
network
networks
citedby:scholar:count:15citedby:scholar:timestamp:2021-4-15ensemblegeometricgraphgraphshyperhyptrailsnetworknetworks
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1Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer
H. Chereda, A. Bleckmann, K. Menck, J. Perera-Bel, P. Stegmaier, F. Auer, F. Kramer, A. Leha, и T. Beißbarth. BioRxiv, (2020)
3 лет назад , @becker
p21
graph
neural
network
networks
relatedwork
citedby:scholar:count:1
citedby:scholar:timestamp:2021-2-14
p21graphneuralnetworknetworksrelatedworkcitedby:scholar:count:1citedby:scholar:timestamp:2021-2-14
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2Relational inductive biases, deep learning, and graph networks
P. Battaglia, J. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner и 1 other автор(ы). arXiv preprint arXiv:1806.01261, (2018)
4 лет назад , @becker
inductive
bias
bayes
bayesian
neural
network
deep
learning
relational
graph
networks
citedby:scholar:count:761
citedby:scholar:timestamp:2020-9-18
inductivebiasbayesbayesianneuralnetworkdeeplearningrelationalgraphnetworkscitedby:scholar:count:761citedby:scholar:timestamp:2020-9-18
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6Community structure in time-dependent, multiscale, and multiplex networks
P. Mucha, T. Richardson, K. Macon, M. Porter, и J. Onnela. science, 328 (5980): 876--878 (2010)
4 лет назад , @becker
citedby:scholar:count:1565
citedby:scholar:timestamp:2020-9-15
cluster
clustering
community
cordis
dependent
dis
funcdis
graph
multi
multiplex
multiscale
network
project:bmbf
scale
structure
time
citedby:scholar:count:1565citedby:scholar:timestamp:2020-9-15clusterclusteringcommunitycordisdependentdisfuncdisgraphmultimultiplexmultiscalenetworkproject:bmbfscalestructuretime
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2Dynamic graph cnn for learning on point clouds
Y. Wang, Y. Sun, Z. Liu, S. Sarma, M. Bronstein, и J. Solomon. Acm Transactions On Graphics (tog), 38 (5): 1--12 (2019)
4 лет назад , @becker
graph
cnn
point
cloud
classification
dynamic
single
cell
neural
network
nn
citedby:scholar:count:674
citedby:scholar:timestamp:2020-9-10
graphcnnpointcloudclassificationdynamicsinglecellneuralnetworknncitedby:scholar:count:674citedby:scholar:timestamp:2020-9-10
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1Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer
G. Jurman, V. Maggio, D. Fioravanti, Y. Giarratano, I. Landi, M. Francescatto, C. Agostinelli, M. Chierici, M. De Domenico, и C. Furlanello. arXiv preprint arXiv:1710.05918, (2017)
4 лет назад , @becker
multi
multiomics
structure
background
knowledge
omics
graph
cnn
convolutional
neural
network
citedby:scholar:count:2
citedby:scholar:timestamp:2020-8-31
multimultiomicsstructurebackgroundknowledgeomicsgraphcnnconvolutionalneuralnetworkcitedby:scholar:count:2citedby:scholar:timestamp:2020-8-31
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1Network-Based Approaches for Multi-omics Integration
G. Zhou, S. Li, и J. Xia. Computational Methods and Data Analysis for Metabolomics, Springer, (2020)
4 лет назад , @becker
anlaysis
citedby:scholar:count:2
citedby:scholar:timestamp:2020-7-29
graph
integration
multi
multiomics
network
omics
p21
relatedwork
anlaysiscitedby:scholar:count:2citedby:scholar:timestamp:2020-7-29graphintegrationmultimultiomicsnetworkomicsp21relatedwork
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1Sex Differences in Gene Expression and Regulatory Networks across 29 Human Tissues
C. Lopes-Ramos, C. Chen, M. Kuijjer, J. Paulson, A. Sonawane, M. Fagny, J. Platig, K. Glass, J. Quackenbush, и D. DeMeo. Cell Reports, 31 (12): 107795 (2020)
4 лет назад , @becker
correlation
difference
disruption
estimation
expression
gene
graph
human
lioness
network
networks
paper:cordis
regulatory
sample
sex
single
tissue
correlationdifferencedisruptionestimationexpressiongenegraphhumanlionessnetworknetworkspaper:cordisregulatorysamplesexsingletissue
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1Personalized characterization of diseases using sample-specific networks
X. Liu, Y. Wang, H. Ji, K. Aihara, и L. Chen. Nucleic acids research, 44 (22): e164--e164 (2016)
4 лет назад , @becker
correlation
disruption
single
sample
network
networks
graph
individual
estimation
perturbation
citedby:scholar:count:100
citedby:scholar:timestamp:2020-7-27
correlationdisruptionsinglesamplenetworknetworksgraphindividualestimationperturbationcitedby:scholar:count:100citedby:scholar:timestamp:2020-7-27
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2Learning important features from multi-view data to predict drug side effects
X. Liang, P. Zhang, J. Li, Y. Fu, L. Qu, Y. Chen, и Z. Chen. Journal of Cheminformatics, 11 (1): 1--17 (2019)
4 лет назад , @becker
multi
view
learning
machine
laplacian
laplace
matrix
constrained
lasso
constraint
drug
effect
side
effects
graph
network
correlation
cordis
overlap
citedby:scholar:count:1
citedby:scholar:timestamp:2020-7-8
multiviewlearningmachinelaplacianlaplacematrixconstrainedlassoconstraintdrugeffectsideeffectsgraphnetworkcorrelationcordisoverlapcitedby:scholar:count:1citedby:scholar:timestamp:2020-7-8
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2A fair comparison of graph neural networks for graph classification
F. Errica, M. Podda, D. Bacciu, и A. Micheli. arXiv preprint arXiv:1912.09893, (2019)
4 лет назад , @becker
graph
neural
network
classification
comparison
paper
citedby:scholar:count:9
citedby:scholar:timestamp:2020-6-3
graphneuralnetworkclassificationcomparisonpapercitedby:scholar:count:9citedby:scholar:timestamp:2020-6-3
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4Overlapping community detection at scale: a nonnegative matrix factorization approach
J. Yang, и J. Leskovec. Proceedings of the sixth ACM international conference on Web search and data mining, стр. 587--596. ACM, (2013)
5 лет назад , @becker
community
correlation
detection
graph
matrix
nalab
network
regression
communitycorrelationdetectiongraphmatrixnalabnetworkregression
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2VOSviewer - Visualizing scientific landscapes

1Understanding Convolutions on Graphs

1LOGML - Marinka Zitnik: Graph Representation Learning for Biomedical Discovery - YouTube

1HipGraph/Force2Vec: Implementation of Force2Vec method for ICDM 2020 paper titled "Force2Vec: Parallel force-directed graph embedding"

1WangYueFt/dgcnn

публикации (спрятать)24
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3Subgroup generalization and fairness of graph neural networks

2A gesture system for graph visualization in virtual reality environments

2A generalization of transformer networks to graphs

1A biomedical open knowledge network harnesses the power of AI to understand deep human biology

2GreaseLM: Graph REASoning Enhanced Language Models

1A knowledge graph to interpret clinical proteomics data

3Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning

1scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses

2Generalized hypergeometric ensembles: Statistical hypothesis testing in complex networks

1Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer

2Relational inductive biases, deep learning, and graph networks

6Community structure in time-dependent, multiscale, and multiplex networks

2Dynamic graph cnn for learning on point clouds

1Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer

1Network-Based Approaches for Multi-omics Integration

1Sex Differences in Gene Expression and Regulatory Networks across 29 Human Tissues

1Personalized characterization of diseases using sample-specific networks

2Learning important features from multi-view data to predict drug side effects

2A fair comparison of graph neural networks for graph classification

4Overlapping community detection at scale: a nonnegative matrix factorization approach

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