The Graph Neural Network Model

IEEE Transactions on Neural Networks - Tập 20 Số 1 - Trang 61-80 - 2009
Franco Scarselli1, M. Gori1, Ah Chung Tsoi2, Markus Hagenbuchner3, Gabriele Monfardini1
1Fac. of Inf. Eng., Univ. of Siena, Siena
2[Hong Kong Baptist Univ., Hong Kong]
3University of Wollongong, Wollongong NSW

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Tài liệu tham khảo

bunke, 2000, graph matching: theoretical foundations, algorithms, and applications, Proc Vis Interface, 82

graham, 1982, Kronecker Products and Matrix Calculus With Applications

rumelhart, 1986, Parallel Distributed Processing Explorations in the Microstructure of Cognition, 1, 10.7551/mitpress/5236.001.0001

10.1007/0-387-32792-4_4

10.1145/1150402.1150409

di massa, 2006, a comparison between recursive neural networks and graph neural networks, Proc Int Joint Conf Neural Netw, 778

10.1109/ICIP.2003.1246606

10.1016/S0169-7552(98)00108-1

10.1142/S0218001404003228

10.3115/1218955.1218971

monfardini, 2006, graph neural networks for object localization, Proc 17th Eur Conf Artif Intell, 665

10.1109/WI.2005.67

kondor, 2002, diffusion kernels on graphs and other discrete structures, Proc 19th Int Conf Mach Learn, 315

10.1023/B:MACH.0000039777.23772.30

10.1145/959242.959248

10.1109/ICMLA.2004.1383538

suzuki, 2003, kernels for structured natural language data, Proc Conf Neural Inf Process Syst

collins, 2002, Advances in neural information processing systems, 14, 625

mah�, 2004, extensions of marginalized graph kernels, Proc 21st Int Conf Mach Learn, 552

kashima, 2003, marginalized kernels between labeled graphs, Proc 20th Int Conf Mach Learn, 321

rudin, 1987, Real and Complex Analysis

bishop, 1995, Neural Networks for Pattern Recognition, 10.1093/oso/9780198538493.001.0001

10.1109/ICNN.1993.298623

10.1109/72.248452

gori, 2000, learning user profiles in nautilus, Proc Int l Conf Adaptive Hypermedia and Adaptive Web-Based Systems (AH2000), 323, 10.1007/3-540-44595-1_39

fahlman, 1989, Advances in neural information processing systems, 2, 524

10.1016/j.patrec.2005.03.009

10.1109/72.572102

10.1109/TNN.2006.875977

bianchini, 2003, face spotting in color images using recursive neural networks, Proc Italian Workshop Neural Networks 95

10.1016/S0893-6080(97)00097-X

bianucci, 2001, analysis of the internal representations developed by neural networks for structures applied to quantitative structure-activity relationship studies of benzodiazepines, J Chem Inf Comput Sci, 41, 202, 10.1021/ci9903399

10.1007/978-1-4471-0715-6_4

francesconi, 1997, Graphics Recognition Lecture Notes in Computer Science

10.1162/153244304773936054

10.1109/TNN.2003.810735

10.1145/775152.775203

10.1145/324133.324140

10.1109/72.963781

bianchini, 2003, face spotting in color images using recursive neural networks, Proc Int l Workshop Artificial Neural Networks on Pattern Recognition, 76

schmitt, 1998, relating chemical structure to activity: an application of the neural folding architecture, Proc Workshop Fuzzy-Neuro Syst /Conf Eng Appl Neural Netw, 170

k�chler, 1996, Lecture Notes in Computer Science, 1137

10.1109/CNNA.2005.1543160

10.1073/pnas.79.8.2554

10.1145/775152.775191

10.1109/TKDE.2003.1208999

10.1145/511446.511512

de raedt, 1997, Lecture Notes in Artificial Intelligence, 1297, 133

wo?nica, 2006, matching based kernels for labeled graphs, Proc Int Workshop Mining Learn Graphs/ECML/PKDD, 97

10.1007/11536314_27

10.1145/1013367.1013486

10.1103/PhysRevLett.59.2229

almeida, 1987, a learning rule for asynchronous perceptrons with feedback in a combinatorial environment, Proc IEEE Int Conf Neural Netw, 2, 609

tsoi, 1998, Lecture Notes in Computer Science, 1387, 27, 10.1007/BFb0053994

10.1007/978-3-642-97239-3

10.1093/comjnl/7.2.155

10.1016/j.neunet.2005.07.003

10.1002/9781118033074

10.1109/TNN.2008.2005141

2005, Proc Int Workshop Sub-Symbol Paradigms Structured Domains

2005, Proc Int l Workshop Mining Graphs Trees and Sequences

10.1145/1183463.1183466

2006, Proc Int l Workshop Mining Graphs Trees and Sequences

srinivasan, 1994, mutagenesis: ilp experiments in a non-determinate biological domain, Proc 9th Int l Workshop Inductive Logic Programming, 217

10.1007/978-3-642-88304-0

10.1016/j.patrec.2005.03.010

scarselli, 0, The GNN Toolbox

haykin, 1994, Neural Networks A Comprehensive Foundation

10.1007/978-3-540-73888-6_43

10.1109/72.712151

kramer, 2001, feature construction with version spaces for biochemical applications, Proc 18th Int Conf Mach Learn, 258

10.1016/S0169-7552(98)00110-X

10.1021/jm00106a046

10.1109/72.572108

uwents, 2006, two connectionist models for graph processing: an experimental comparison on relational data, Proc Eur Conf Mach Learn, 213

krogel, 2003, comparative evaluation of approaches to propositionalization, Proc 12th Int Conf Inductive Logic Program, 197, 10.1007/978-3-540-39917-9_14

10.1109/2945.910824

10.1162/089120103321337430

10.1145/774841.774844

baresi, 2002, Lecture Notes in Computer Science, 2505, 402, 10.1007/3-540-45832-8_30

quinlan, 1993, foil: a midterm report, Proc Eur Conf Mach Learn, 3

de raedt, 2008, Logical and Relational Learning, 10.1007/978-3-540-68856-3

quinlan, 1996, Lecture Notes in Computer Science, 1160, 143, 10.1007/3-540-61863-5_42

bua, 2002, Lecture Notes in Computer Science, 2415

10.1109/CNNA.1992.274345

ramon, 2002, Clustering and instance based learning in first order logic

kirsten, 2002, Multirelational distance-based clustering

2004, Proc Int Workshop Statist Relat Learn Connect Other Fields

10.7551/mitpress/9780262033589.001.0001

vapnik, 1998, Statistical Learning Theory

10.1109/31.7601

10.1109/31.7600

lafferty, 2001, conditional random fields: probabilistic models for segmenting and labeling sequence data, Proc 18th Int Conf Mach Learn, 282

chang, 2000, learning to create customized authority lists, Proc 17th Int Conf Mach Learn, 127

getoor, 2007, Introduction to Statistical Relational Learning, 10.7551/mitpress/7432.001.0001

jensen, 1996, Introduction to Bayesian networks