Graph Neural Network Operators: a Review
Graph Neural Networks (GNN) is one of the promising machine learning areas in solving real world problems such as social networks, recommender systems, computer vision and pattern recognition. One of the important component of GNN is GNN operators which are responsible to train GNN graph structured...
Uložené v:
| Vydané v: | Multimedia tools and applications Ročník 83; číslo 8; s. 23413 - 23436 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
Springer US
01.03.2024
Springer Nature B.V |
| Predmet: | |
| ISSN: | 1573-7721, 1380-7501, 1573-7721 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Graph Neural Networks (GNN) is one of the promising machine learning areas in solving real world problems such as social networks, recommender systems, computer vision and pattern recognition. One of the important component of GNN is GNN operators which are responsible to train GNN graph structured data and forward learning nodes information to other layers. This review focus on recent advancements of GNN operators in detail. The rich Mathematical nature of GNN operators has been discussed for selected GNN operators. The review also highlights different benchmark graph structured datasets and presents results using different GNN operators. We have included thorough discussion for state-of-the-art in this field including limitations and future directions. Overall, the review covers important areas of GNN as GNN operators from future research directions point of view and real world applications perspective. |
|---|---|
| AbstractList | Graph Neural Networks (GNN) is one of the promising machine learning areas in solving real world problems such as social networks, recommender systems, computer vision and pattern recognition. One of the important component of GNN is GNN operators which are responsible to train GNN graph structured data and forward learning nodes information to other layers. This review focus on recent advancements of GNN operators in detail. The rich Mathematical nature of GNN operators has been discussed for selected GNN operators. The review also highlights different benchmark graph structured datasets and presents results using different GNN operators. We have included thorough discussion for state-of-the-art in this field including limitations and future directions. Overall, the review covers important areas of GNN as GNN operators from future research directions point of view and real world applications perspective. |
| Author | Singh, Sukhdeep Sharma, Anuj Ratna, S. |
| Author_xml | – sequence: 1 givenname: Anuj orcidid: 0000-0003-2219-2528 surname: Sharma fullname: Sharma, Anuj email: anujs@pu.ac.in organization: Department of Computer Science and Applications, Panjab University – sequence: 2 givenname: Sukhdeep surname: Singh fullname: Singh, Sukhdeep organization: Computer Science Department, D.M.College – sequence: 3 givenname: S. surname: Ratna fullname: Ratna, S. organization: Department of Computer Science and Applications, Panjab University |
| BookMark | eNp9kMFOwzAMhiM0JLbBC3CqxIVLwU7SpuGGJhhIE5PQ7lHaJdAxmpK0TLw9gSKBOOxg2Yf_s61vQkaNawwhpwgXCCAuAyJwmgJlKeacQ8oPyBgzwVIhKI7-zEdkEsIGAPOM8jE5n3vdPicPpvd6G1u3c_4lWbbG6875cJXo5NG812Z3TA6t3gZz8tOnZHV7s5rdpYvl_H52vUgrhrJLucZSlHwtJeZrWwBwbSW1KAWrMtSSokFpBFhTQiFoJgtp1yJnvBRY2oxNydmwtvXurTehUxvX-yZeVFQygFhMxlQxpCrvQvDGqqrudFe7pvO63ioE9aVFDVpU1KK-tSgeUfoPbX39qv3HfogNUIjh5sn436_2UJ9naXQ8 |
| CitedBy_id | crossref_primary_10_1016_j_cpc_2025_109719 crossref_primary_10_1111_exsy_70091 crossref_primary_10_1098_rspa_2024_0819 crossref_primary_10_1016_j_csbj_2025_03_014 crossref_primary_10_1145_3694784 crossref_primary_10_1007_s13042_024_02482_z crossref_primary_10_1016_j_asoc_2025_112923 crossref_primary_10_1016_j_neunet_2025_108027 crossref_primary_10_1016_j_oregeorev_2024_106399 crossref_primary_10_1016_j_rser_2024_114647 crossref_primary_10_1038_s41598_025_05225_4 crossref_primary_10_1007_s42791_025_00101_8 |
| Cites_doi | 10.1145/3394486.3403088 10.1109/TNN.2008.2005605 10.1109/TKDE.2020.2981333 10.1007/978-3-031-01588-5 10.1609/aimag.v29i3.2157 10.1093/bioinformatics/btx252 10.1109/ICDMW58026.2022.00094 10.1609/icwsm.v14i1.7347 10.1016/j.aiopen.2021.01.001 10.2200/S01045ED1V01Y202009AIM046 10.1155/2023/1566123 10.1145/3534678.3539387 10.1038/s41598-021-96723-8 10.1007/s41019-023-00206-x 10.1016/j.neunet.2021.11.001 10.1109/TNN.2008.2005141 10.1609/aaai.v29i1.9277 10.1609/aaai.v34i04.5997 10.1145/3575637.3575646 10.24963/ijcai.2021/214 10.1109/CVPR42600.2020.00178 10.1016/j.patcog.2020.107637 10.1109/CVPR.2018.00097 10.1109/TNNLS.2020.2978386 10.1103/PhysRevLett.120.145301 10.1609/aaai.v24i1.7519 10.1613/jair.1.13225 10.1609/aaai.v33i01.33014602 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| DBID | AAYXX CITATION 3V. 7SC 7WY 7WZ 7XB 87Z 8AL 8AO 8FD 8FE 8FG 8FK 8FL 8G5 ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FRNLG F~G GNUQQ GUQSH HCIFZ JQ2 K60 K6~ K7- L.- L7M L~C L~D M0C M0N M2O MBDVC P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI Q9U |
| DOI | 10.1007/s11042-023-16440-4 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ABI-INFORM Complete ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Global (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni Edition) Research Library (Alumni Edition) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Business Premium Collection Technology collection ProQuest One Community College ProQuest Central Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student Research Library Prep SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database ABI/INFORM Professional Advanced Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global (OCUL) Computing Database Research Library (subscription) Research Library (Corporate) AAdvanced Technologies & Aerospace Database (subscription) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Business ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central Basic |
| DatabaseTitle | CrossRef ABI/INFORM Global (Corporate) ProQuest Business Collection (Alumni Edition) ProQuest One Business Research Library Prep Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College Research Library (Alumni Edition) ProQuest Pharma Collection ABI/INFORM Complete ProQuest Central ABI/INFORM Professional Advanced ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Research Library ProQuest Central (New) Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global ProQuest Computing ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection ProQuest Business Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Business (Alumni) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) Business Premium Collection (Alumni) |
| DatabaseTitleList | ABI/INFORM Global (Corporate) |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 1573-7721 |
| EndPage | 23436 |
| ExternalDocumentID | 10_1007_s11042_023_16440_4 |
| GroupedDBID | -4Z -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29M 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 3EH 3V. 4.4 406 408 409 40D 40E 5QI 5VS 67Z 6NX 7WY 8AO 8FE 8FG 8FL 8G5 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFO ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACREN ACSNA ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBLON EBS EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GUQSH GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITG ITH ITM IWAJR IXC IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV KOW LAK LLZTM M0C M0N M2O M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 P9O PF0 PQBIZ PQBZA PQQKQ PROAC PT4 PT5 Q2X QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TEORI TH9 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z7R Z7S Z7W Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8Q Z8R Z8S Z8T Z8U Z8W Z92 ZMTXR ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADKFA AEZWR AFDZB AFHIU AFOHR AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION 7SC 7XB 8AL 8FD 8FK AFFHD JQ2 L.- L7M L~C L~D MBDVC PHGZM PHGZT PKEHL PQEST PQGLB PQUKI Q9U |
| ID | FETCH-LOGICAL-c319t-4a1b7b4d9916df8004af92f1973c51a921e19e70feb08725989fd7634b71bf53 |
| IEDL.DBID | M0C |
| ISICitedReferencesCount | 17 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001051759100008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1573-7721 1380-7501 |
| IngestDate | Wed Nov 05 09:06:01 EST 2025 Sat Nov 29 06:20:33 EST 2025 Tue Nov 18 21:51:52 EST 2025 Fri Feb 21 02:41:57 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 8 |
| Keywords | GNN convolutional operators Deep learning Graph structural representation Graph neural networks |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c319t-4a1b7b4d9916df8004af92f1973c51a921e19e70feb08725989fd7634b71bf53 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0003-2219-2528 |
| PQID | 2930093039 |
| PQPubID | 54626 |
| PageCount | 24 |
| ParticipantIDs | proquest_journals_2930093039 crossref_citationtrail_10_1007_s11042_023_16440_4 crossref_primary_10_1007_s11042_023_16440_4 springer_journals_10_1007_s11042_023_16440_4 |
| PublicationCentury | 2000 |
| PublicationDate | 20240300 |
| PublicationDateYYYYMMDD | 2024-03-01 |
| PublicationDate_xml | – month: 3 year: 2024 text: 20240300 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York – name: Dordrecht |
| PublicationSubtitle | An International Journal |
| PublicationTitle | Multimedia tools and applications |
| PublicationTitleAbbrev | Multimed Tools Appl |
| PublicationYear | 2024 |
| Publisher | Springer US Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer Nature B.V |
| References | Jin Y, JaJa JF (2022) Improving graph neural network with learnable permutation pooling. In: 2022 IEEE international conference on data mining workshops (ICDMW), pp 682–689 Ong E, Veličkoviá P (2022) Learnable commutative monoids for graph neural networks. In: Proceedings of the first learning on graphs conference, vol 198, pp 43–14322 Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka Jr ER, Mitchell TM (2010) Toward an architecture for never-ending language learning. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence Rossi R, Ahmed N (2015) The network data repository with interactive graph analytics and visualization. In: AAAI Conference on Artificial Intelligence, vol 29, pp 4292–4293 Hamilton WL (2020) Graph representation learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 14(3): 1–159 Shi W, Rajkumar RR (2020) Point-gnn: Graph neural network for 3d object detection in a point cloud. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Transactions on Neural Networks 20(1): 61–80 Weisfeiler BY, Leman AA (1968) The reduction of a graph to canonical form and the algebra which appears therein. Nauchno-Tekhnicheskaya Informatsiya 9, 12–16 Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: A review of methods and applications. AI Open 1, 57–81 LiuYLiuQZhangJ-WFengHWangZZhouZChenWMulti-variate time-series forecasting with temporal polynomial graph neural networksAdvances in Neural Information Processing Systems2022351941419426 Chen M, Wei Z, Huang Z, Ding B, Li Y (2020) Simple and deep graph convolutional networks. arXiv:2007.02133 Deng C, Li X, Feng Z, Zhang Z (2022) Garnet: Reduced-rank topology learning for robust and scalable graph neural networks. In: Proceedings of the first learning on graphs conference, pp 3–1323 ScarselliFGoriMTsoiACHagenbuchnerMMonfardiniGThe graph neural network modelIEEE Transactions on Neural Networks2009201618010.1109/TNN.2008.200560519068426 bitcoin otc. http://www.bitcoin-otc.com Zitnik M, Leskovec J (2017) Predicting multicellular function through multi-layer tissue networks. Bioinformatics 33(14): 190–198 Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X, Xiao J (2015) 3d shapenets: A deep representation for volumetric shapes. Computer Vision and Pattern Recognition (CVPR) bitcoin alpha. http://www.btc-alpha.com LeCun Y, Cortes C, Burges CJ (1998) The mnist database of handwritten digits. http://yann.lecun.com/exdb/mnist Baumgartner J, Zannettou S, Keegan B, Squire M, Blackburn J (2020) The pushshift reddit dataset. arXiv:2001.08435 Hamilton WL, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, pp 1024–1034 RanjanRGroverSMedyaSChakaravarthyVSabharwalYRanuSGreed: A neural framework for learning graph distance functionsAdvances in Neural Information Processing Systems2022352251822530 Shi Y, Huang Z, Feng S, Zhong H, Wang W, Sun Y (2021) Masked label prediction: Unified message passing model for semi-supervised classification. In: Proceedings of the thirtieth international joint conference on artificial intelligence, IJCAI-21, pp 1548–1554 Li Y, Tarlow D, Brockschmidt M, Zemel R (2016) Gated graph sequence neural networks. In: International Conference on Learning Representations Qi CR, Su H, Mo K, Guibas LJ (2017) Pointnet: Deep learning on point sets for 3d classification and segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, 652–660 Zuo S, Jiang H, Yin Q, Tang X, Yin B, Zhao T (2022) Dip-gnn: Discriminative pre-training of graph neural networks. arXiv:2209.07499 Qin Y, Zhang Z, Wang X, Zhang Z, Zhu W (2022) Nas-bench-graph: Benchmarking graph neural architecture search. Advances in Neural Information Processing Systems 35:54–69 ZitnikMLeskovecJPredicting multicellular function through multi-layer tissue networksBioinformatics2017331419019810.1093/bioinformatics/btx252 Yuen B, Hoang MT, Dong X, Lu T (2021) Universal activation function for machine learning. Scientific Reports 11(1) Fey M, Lenssen J, Weichert F, Muller H (2018) Splinecnn: Fast geometric deep learning with continuous b-spline kernels. In: 2018 IEEE/CVF Conference on computer vision and pattern recognition (CVPR), pp 869–877 Dudzik AJ, Veličković, P (2022) Graph neural networks are dynamic programmers. In: Advances in neural information processing systems, vol 35, pp 20635–20647 Parvathaneni Naga S, Balamurali K, Shakeel A, Naif A, Fawaz Khaled A, Nasser A (2023) Variational autoencoders-basedself-learning model for tumor identification and impact analysis from 2-d mri images. Journal of Healthcare Engineering Qin Y, Zhang Z, Wang X, Zhang Z, Zhu W (2022) Nas-bench-graph: Benchmarking graph neural architecture search. In: Advances in neural information processing systems, vol 35, pp 54–69 Xie T, Grossman JC (2018) Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett 120 ZhangZWangXZhangZLiHQinZZhuWDynamic graph neural networks under spatio-temporal distribution shiftAdvances in Neural Information Processing Systems20223560746089 Buterez D, Janet JP, Kiddle SJ, Oglic D, Lió P (2022) Graph neural networks with adaptive readouts. In: Advances in Neural Information Processing Systems, vol 35, pp 19746–19758 Zhu Z, Galkin M, Zhang Z, Tang J (2022) Neural-symbolic models for logical queries on knowledge graphs. In: Proceedings of the 39th international conference on machine learning, vol 162, pp 27454–27478 OngEVeličkoviáPLearnable commutative monoids for graph neural networksProceedings of the First Learning on Graphs Conference20221984314322 Du J, Zhang S, Wu G, Moura JMF, Kar S (2018) Topology adaptive graph convolutional networks. arXiv:1710.10370 HamiltonWLGraph representation learningSynthesis Lectures on Artificial Intelligence and Machine Learning2020143115910.1007/978-3-031-01588-5 Tang J, Li J, Gao Z, Li J (2022) Rethinking graph neural networks for anomaly detection. In: Proceedings of the 39th international conference on machine learning, vol 162, pp 21076–21089 ItohTDKuboTIkedaKMulti-level attention pooling for graph neural networks: Unifying graph representations with multiple localitiesNeural Networks202214535637310.1016/j.neunet.2021.11.00134808587 Sanchez-Gonzalez A, Godwin J, Pfaff T, Ying R, Leskovec J, Battaglia PW (2020) Learning to simulate complex physics with graph networks. In: Proceedings of the 37th international conference on machine learning. JMLR.org, ??? GasteigerJQianCGünnemannSInfluence-based mini-batching for graph neural networksProceedings of the First Learning on Graphs Conference202219891919 Lan S, Ma Y, Huang W, Wang W, Yang H, Li P (2022) DSTAGNN: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In: Proceedings of the 39th international conference on machine learning, vol 162, pp 11906–11917 He Y, Perlmutter M, Reinert G, Cucuringu M (2022) Msgnn: A spectral graph neural network based on a novel magnetic signed laplacian. In: Proceedings of the first learning on graphs conference, vol 198, pp 40–14039 Liu J, Hooi B, Kawaguchi K, Xiao X (2022) Mgnni: Multiscale graph neural networks with implicit layers. In: Advances in Neural Information Processing Systems, vol 35, pp 21358–21370 Gasteiger J, Qian C, Günnemann S (2022) Influence-based mini-batching for graph neural networks. In: Proceedings of the first learning on graphs conference, vol 198, pp 9–1919 Hengshuang Z, Li J, Jiaya J, Philip T, Vladlen K (2021) Point transformer. arXiv:2012.09164 Zhang H, Dai G, Liu Z, Wang R, Hamilton W (2022) Understanding gnn computational graph: A coordinated computation, io, and memory perspective. In: Proceedings of 5th MLSys, pp 1–15 LiuJHooiBKawaguchiKXiaoXMgnni: Multiscale graph neural networks with implicit layersAdvances in Neural Information Processing Systems2022352135821370 Ding K, Xu Z, Tong H, Liu H (2022) Data augmentation for deep graph learning: A survey. SIGKDD Explor Newsl 24(2):61–77 Carselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) Computational capabilities of graph neural networks. IEEE Transactions on Neural Networks 20(1): 81–102 Prithviraj Sen MBLGBG Galileo Mark Namata Eliassi-Rad T (2008) Collective classification in network data. AI Magazine 29(3): 93–106 RossiRAhmedNThe network data repository with interactive graph analytics and visualizationAAAI Conference on Artificial Intelligence20152942924293 HuangTChenTFangMMenkovskiVZhaoJYinLPeiYMocanuDCWangZPechenizkiyMLiuSYou can have better graph neural networks by not training weights at all: Finding untrained gnns ticketsProceedings of the First Learning on Graphs Conference202219881817 Brody S, Alon U, Yahav E (2022) How attentive are graph attention networks? arXiv:2105.14491 Brockschmidt M (2020) GNN-FiLM: Graph neural networks with feature-wise linear modulation. In: Proceedings of the 37th international conference on machine learning, pp 1144–1152 You J, Ying R, Leskovec J (2019) Position-aware graph neural networks. In: Proceedings of the 36th international conference on machine learning. Proceedings of machine learning research, vol 97, pp 7134–7143 Li G, Müller M, Ghanem B, Koltun V (2021) Training graph neural networks with 1000 layers. In: Proceedings of the 38th international conference on machine learning, vol 139, pp 6437–6449 ZekunLQianchengYXiaLXiaoningLQinwenYLearning weight signed network embedding with graph neural networksData Science and Engineering20238364610.1007/s41019-023-00206-x DudzikAJVeličkovićPGraph neural networks are dynamic programmersAdvances in Neural Information Processing Systems2022352063520647 Ranjan E, Sanyal S, Talukdar PP (2020) Asap: Adaptive structure aware pooling for learning hierarchical graph representations. In: Proceedings of the thirty-fourth AAAI conference on artificial in 16440_CR65 16440_CR22 Y He (16440_CR29) 2022; 198 16440_CR66 16440_CR63 16440_CR20 16440_CR64 Z Zhang (16440_CR80) 2022; 35 16440_CR25 16440_CR26 16440_CR23 16440_CR67 16440_CR24 16440_CR68 16440_CR27 J Liu (16440_CR44) 2022; 35 Z Hu (16440_CR33) 2020; 2020 F Scarselli (16440_CR61) 2009; 20 16440_CR73 16440_CR71 16440_CR32 16440_CR76 16440_CR30 16440_CR74 16440_CR31 16440_CR75 16440_CR37 16440_CR2 TD Itoh (16440_CR36) 2022; 145 16440_CR78 16440_CR1 16440_CR35 J Zhou (16440_CR83) 2020; 1 16440_CR38 16440_CR39 E Ong (16440_CR48) 2022; 198 F Carselli (16440_CR11) 2009; 20 Z Zhang (16440_CR79) 2022; 34 R Ranjan (16440_CR56) 2022; 35 AJ Dudzik (16440_CR17) 2022; 35 L Zekun (16440_CR77) 2023; 8 R Rossi (16440_CR58) 2015; 29 16440_CR4 16440_CR3 16440_CR6 16440_CR5 16440_CR7 16440_CR40 16440_CR84 16440_CR81 16440_CR9 16440_CR82 M Zitnik (16440_CR86) 2017; 33 16440_CR87 T Xie (16440_CR72) 2018 16440_CR88 16440_CR41 16440_CR85 16440_CR42 16440_CR45 16440_CR46 Y Liu (16440_CR43) 2022; 35 WL Hamilton (16440_CR28) 2020; 14 16440_CR49 D Buterez (16440_CR8) 2022; 35 Z Wu (16440_CR70) 2021; 32 16440_CR50 J Gasteiger (16440_CR21) 2022; 198 16440_CR51 16440_CR10 16440_CR54 16440_CR55 16440_CR52 16440_CR53 16440_CR14 16440_CR15 16440_CR59 16440_CR12 16440_CR13 16440_CR57 16440_CR18 16440_CR19 16440_CR16 G Nikolentzos (16440_CR47) 2021; 72 BY Weisfeiler (16440_CR69) 1968; 9 T Huang (16440_CR34) 2022; 198 16440_CR62 16440_CR60 |
| References_xml | – reference: GasteigerJQianCGünnemannSInfluence-based mini-batching for graph neural networksProceedings of the First Learning on Graphs Conference202219891919 – reference: Hamilton WL (2020) Graph representation learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 14(3): 1–159 – reference: Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Transactions on Neural Networks 20(1): 61–80 – reference: Prithviraj Sen MBLGBG Galileo Mark Namata Eliassi-Rad T (2008) Collective classification in network data. AI Magazine 29(3): 93–106 – reference: XieTGrossmanJCCrystal graph convolutional neural networks for an accurate and interpretable prediction of material properties2018LettPhys Rev120 – reference: Xie T, Grossman JC (2018) Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett 120 – reference: Ying Z, Hamilton WL, Leskovec J (2019) Weisfeiler and leman go neural: Higher order graph neural networks. In: Proceedings of the 33rd AAAI conference on artificial intelligence, pp 10848–10855. arXiv:1812.08434 – reference: OngEVeličkoviáPLearnable commutative monoids for graph neural networksProceedings of the First Learning on Graphs Conference20221984314322 – reference: WuZPanSChenFLongGZhangCYuPSA comprehensive survey on graph neural networksIEEE Transactions on Neural Networks and Learning Systems2021321424420549510.1109/TNNLS.2020.297838632217482 – reference: Li Y, Tarlow D, Brockschmidt M, Zemel R (2016) Gated graph sequence neural networks. In: International Conference on Learning Representations – reference: Buterez D, Janet JP, Kiddle SJ, Oglic D, Lió P (2022) Graph neural networks with adaptive readouts. In: Advances in Neural Information Processing Systems, vol 35, pp 19746–19758 – reference: HamiltonWLGraph representation learningSynthesis Lectures on Artificial Intelligence and Machine Learning2020143115910.1007/978-3-031-01588-5 – reference: Ding K, Xu Z, Tong H, Liu H (2022) Data augmentation for deep graph learning: A survey. SIGKDD Explor Newsl 24(2):61–77 – reference: Gravina A, Bacciu D, Gallicchio C (2023) Anti-symmetric dgn: a stable architecture for deep graph networks. In: International conference on learning representations – reference: Qi CR, Su H, Mo K, Guibas LJ (2017) Pointnet: Deep learning on point sets for 3d classification and segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, 652–660 – reference: Zhu Z, Galkin M, Zhang Z, Tang J (2022) Neural-symbolic models for logical queries on knowledge graphs. In: Proceedings of the 39th international conference on machine learning, vol 162, pp 27454–27478 – reference: Wang X, Zhang M (2022) How powerful are spectral graph neural networks. In: Proceedings of the 39th international conference on machine learning, vol 162, pp 23341–23362 – reference: Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: Proceedings of the 34th international conference on machine learning (ICML), vol 70, pp 1263–1272 – reference: Zhang H, Dai G, Liu Z, Wang R, Hamilton W (2022) Understanding gnn computational graph: A coordinated computation, io, and memory perspective. In: Proceedings of 5th MLSys, pp 1–15 – reference: Itoh TD, Kubo T, Ikeda K (2022) Multi-level attention pooling for graph neural networks: Unifying graph representations with multiple localities. Neural Networks 145, 356–373 – reference: Li G, Müller M, Ghanem B, Koltun V (2021) Training graph neural networks with 1000 layers. In: Proceedings of the 38th international conference on machine learning, vol 139, pp 6437–6449 – reference: Brody S, Alon U, Yahav E (2022) How attentive are graph attention networks? arXiv:2105.14491 – reference: ZhouJCuiGHuSZhangZYangCLiuZWangLLiCSunMGraph neural networks: A review of methods and applicationsAI Open20201578110.1016/j.aiopen.2021.01.001 – reference: He Y, Gan Q, Wipf D, Reinert GD, Yan J, Cucuringu M (2022) GNNRank: Learning global rankings from pairwise comparisons via directed graph neural networks. In: Proceedings of the 39th International Conference on Machine Learning, vol 162, pp 8581–8612 – reference: Shi Y, Huang Z, Feng S, Zhong H, Wang W, Sun Y (2021) Masked label prediction: Unified message passing model for semi-supervised classification. In: Proceedings of the thirtieth international joint conference on artificial intelligence, IJCAI-21, pp 1548–1554 – reference: Ong E, Veličkoviá P (2022) Learnable commutative monoids for graph neural networks. In: Proceedings of the first learning on graphs conference, vol 198, pp 43–14322 – reference: You J, Ying R, Leskovec J (2019) Position-aware graph neural networks. In: Proceedings of the 36th international conference on machine learning. Proceedings of machine learning research, vol 97, pp 7134–7143 – reference: ScarselliFGoriMTsoiACHagenbuchnerMMonfardiniGThe graph neural network modelIEEE Transactions on Neural Networks2009201618010.1109/TNN.2008.200560519068426 – reference: Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X, Xiao J (2015) 3d shapenets: A deep representation for volumetric shapes. Computer Vision and Pattern Recognition (CVPR) – reference: bitcoin otc. http://www.bitcoin-otc.com – reference: CarselliFGoriMTsoiACHagenbuchnerMMonfardiniGComputational capabilities of graph neural networksIEEE Transactions on Neural Networks20092018110210.1109/TNN.2008.2005141 – reference: ItohTDKuboTIkedaKMulti-level attention pooling for graph neural networks: Unifying graph representations with multiple localitiesNeural Networks202214535637310.1016/j.neunet.2021.11.00134808587 – reference: Sanchez-Gonzalez A, Godwin J, Pfaff T, Ying R, Leskovec J, Battaglia PW (2020) Learning to simulate complex physics with graph networks. In: Proceedings of the 37th international conference on machine learning. JMLR.org, ??? – reference: Fey M, Lenssen J, Weichert F, Muller H (2018) Splinecnn: Fast geometric deep learning with continuous b-spline kernels. In: 2018 IEEE/CVF Conference on computer vision and pattern recognition (CVPR), pp 869–877 – reference: LiuYLiuQZhangJ-WFengHWangZZhouZChenWMulti-variate time-series forecasting with temporal polynomial graph neural networksAdvances in Neural Information Processing Systems2022351941419426 – reference: Qin Y, Zhang Z, Wang X, Zhang Z, Zhu W (2022) Nas-bench-graph: Benchmarking graph neural architecture search. In: Advances in neural information processing systems, vol 35, pp 54–69 – reference: ZhangZWangXZhangZLiHQinZZhuWDynamic graph neural networks under spatio-temporal distribution shiftAdvances in Neural Information Processing Systems20223560746089 – reference: Zitnik M, Leskovec J (2017) Predicting multicellular function through multi-layer tissue networks. Bioinformatics 33(14): 190–198 – reference: RanjanRGroverSMedyaSChakaravarthyVSabharwalYRanuSGreed: A neural framework for learning graph distance functionsAdvances in Neural Information Processing Systems2022352251822530 – reference: Lan S, Ma Y, Huang W, Wang W, Yang H, Li P (2022) DSTAGNN: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In: Proceedings of the 39th international conference on machine learning, vol 162, pp 11906–11917 – reference: bitcoin alpha. http://www.btc-alpha.com – reference: Wang Z, Wei Z, Li Y, Kuang W, Ding B (2022) Graph neural networks with node-wise architecture. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 1949–1958 – reference: Zekun L, Qiancheng Y, Xia L, Xiaoning L, Qinwen Y (2023) Learning weight signed network embedding with graph neural networks. Data Science and Engineering 8, 36–46 – reference: Bai S, Zhang F, Torr PHS (2020) Hypergraph convolution and hypergraph attention. arXiv:1901.08150 – reference: Parvathaneni Naga S, Balamurali K, Shakeel A, Naif A, Fawaz Khaled A, Nasser A (2023) Variational autoencoders-basedself-learning model for tumor identification and impact analysis from 2-d mri images. Journal of Healthcare Engineering – reference: Qin Y, Zhang Z, Wang X, Zhang Z, Zhu W (2022) Nas-bench-graph: Benchmarking graph neural architecture search. Advances in Neural Information Processing Systems 35:54–69 – reference: Yuen B, Hoang MT, Dong X, Lu T (2021) Universal activation function for machine learning. Scientific Reports 11(1) – reference: Brockschmidt M (2020) GNN-FiLM: Graph neural networks with feature-wise linear modulation. In: Proceedings of the 37th international conference on machine learning, pp 1144–1152 – reference: Rossi R, Ahmed N (2015) The network data repository with interactive graph analytics and visualization. In: AAAI Conference on Artificial Intelligence, vol 29, pp 4292–4293 – reference: Bianchi FM, Grattarola D, Livi L, Alippi C (2019) Graph neural networks with convolutional arma filters. arXiv:1901.01343 – reference: ZekunLQianchengYXiaLXiaoningLQinwenYLearning weight signed network embedding with graph neural networksData Science and Engineering20238364610.1007/s41019-023-00206-x – reference: Zuo S, Jiang H, Yin Q, Tang X, Yin B, Zhao T (2022) Dip-gnn: Discriminative pre-training of graph neural networks. arXiv:2209.07499 – reference: Lai K-H, Zha D, Zhou K, Hu X (2020) Policy-gnn: Aggregation optimization for graph neural networks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 461–471 – reference: ZitnikMLeskovecJPredicting multicellular function through multi-layer tissue networksBioinformatics2017331419019810.1093/bioinformatics/btx252 – reference: Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: A review of methods and applications. AI Open 1, 57–81 – reference: Zhang Z, Wang X, Zhang Z, Li H, Qin Z, Zhu W (2022) Dynamic graph neural networks under spatio-temporal distribution shift. In: Advances in neural information processing systems, vol 35, pp 6074–6089 – reference: Jin Y, JaJa JF (2022) Improving graph neural network with learnable permutation pooling. In: 2022 IEEE international conference on data mining workshops (ICDMW), pp 682–689 – reference: ZhangZCuiPZhuWDeep learning on graphs: A surveyIEEE Transactions on Knowledge and Data Engineering202234124927010.1109/TKDE.2020.2981333 – reference: Liu J, Hooi B, Kawaguchi K, Xiao X (2022) Mgnni: Multiscale graph neural networks with implicit layers. In: Advances in Neural Information Processing Systems, vol 35, pp 21358–21370 – reference: He Y, Perlmutter M, Reinert G, Cucuringu M (2022) Msgnn: A spectral graph neural network based on a novel magnetic signed laplacian. In: Proceedings of the first learning on graphs conference, vol 198, pp 40–14039 – reference: NikolentzosGSiglidisGVazirgiannisMGraph kernels: A surveyJournal of Artificial Intelligence Research202172943102436669310.1613/jair.1.13225 – reference: Weisfeiler BY, Leman AA (1968) The reduction of a graph to canonical form and the algebra which appears therein. Nauchno-Tekhnicheskaya Informatsiya 9, 12–16 – reference: Carselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) Computational capabilities of graph neural networks. IEEE Transactions on Neural Networks 20(1): 81–102 – reference: Gasteiger J, Qian C, Günnemann S (2022) Influence-based mini-batching for graph neural networks. In: Proceedings of the first learning on graphs conference, vol 198, pp 9–1919 – reference: Du J, Zhang S, Wu G, Moura JMF, Kar S (2018) Topology adaptive graph convolutional networks. arXiv:1710.10370 – reference: DudzikAJVeličkovićPGraph neural networks are dynamic programmersAdvances in Neural Information Processing Systems2022352063520647 – reference: HuangTChenTFangMMenkovskiVZhaoJYinLPeiYMocanuDCWangZPechenizkiyMLiuSYou can have better graph neural networks by not training weights at all: Finding untrained gnns ticketsProceedings of the First Learning on Graphs Conference202219881817 – reference: Fu G, Zhao P, Bian Y (2022) p-Laplacian based graph neural networks. In: Proceedings of the 39th international conference on machine learning, vol 162, pp 6878–6917 – reference: RossiRAhmedNThe network data repository with interactive graph analytics and visualizationAAAI Conference on Artificial Intelligence20152942924293 – reference: Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka Jr ER, Mitchell TM (2010) Toward an architecture for never-ending language learning. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence – reference: LeCun Y, Cortes C, Burges CJ (1998) The mnist database of handwritten digits. http://yann.lecun.com/exdb/mnist/ – reference: HeYPerlmutterMReinertGCucuringuMMsgnn: A spectral graph neural network based on a novel magnetic signed laplacianProceedings of the First Learning on Graphs Conference20221984014039 – reference: LiuJHooiBKawaguchiKXiaoXMgnni: Multiscale graph neural networks with implicit layersAdvances in Neural Information Processing Systems2022352135821370 – reference: Hamilton WL, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, pp 1024–1034 – reference: Guan C, Wang X, Chen H, Zhang Z, Zhu W (2022) Large-scale graph neural architecture search. In: Proceedings of the 39th international conference on machine learning, vol 162, pp 7968–7981 – reference: Shi W, Rajkumar RR (2020) Point-gnn: Graph neural network for 3d object detection in a point cloud. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – reference: Dudzik AJ, Veličković, P (2022) Graph neural networks are dynamic programmers. In: Advances in neural information processing systems, vol 35, pp 20635–20647 – reference: Ranjan E, Sanyal S, Talukdar PP (2020) Asap: Adaptive structure aware pooling for learning hierarchical graph representations. In: Proceedings of the thirty-fourth AAAI conference on artificial intelligence (AAAI), pp 5470–5477 – reference: Liu Y, Liu Q, Zhang J-W, Feng H, Wang Z, Zhou Z, Chen W (2022) Multi-variate time-series forecasting with temporal polynomial graph neural networks. In: Advances in neural information processing systems, vol 35, pp 19414–19426 – reference: ButerezDJanetJPKiddleSJOglicDLióPGraph neural networks with adaptive readoutsAdvances in Neural Information Processing Systems2022351974619758 – reference: Baumgartner J, Zannettou S, Keegan B, Squire M, Blackburn J (2020) The pushshift reddit dataset. arXiv:2001.08435 – reference: Deng C, Li X, Feng Z, Zhang Z (2022) Garnet: Reduced-rank topology learning for robust and scalable graph neural networks. In: Proceedings of the first learning on graphs conference, pp 3–1323 – reference: HuZDongYWangKSunYHeterogeneous graph transformerProceedings of The Web Conference2020202027042710 – reference: Tang J, Li J, Gao Z, Li J (2022) Rethinking graph neural networks for anomaly detection. In: Proceedings of the 39th international conference on machine learning, vol 162, pp 21076–21089 – reference: WeisfeilerBYLemanAAThe reduction of a graph to canonical form and the algebra which appears thereinNauchno-Tekhnicheskaya Informatsiya196891216 – reference: Prithviraj Sen MBLGBG, Eliassi-Rad Galileo Mark Namata, T, (2008) Collective classification in network data. AI Magazine 29(3):93–106 – reference: Chen M, Wei Z, Huang Z, Ding B, Li Y (2020) Simple and deep graph convolutional networks. arXiv:2007.02133 – reference: Hengshuang Z, Li J, Jiaya J, Philip T, Vladlen K (2021) Point transformer. arXiv:2012.09164 – volume: 198 start-page: 43 year: 2022 ident: 16440_CR48 publication-title: Proceedings of the First Learning on Graphs Conference – volume: 35 start-page: 6074 year: 2022 ident: 16440_CR80 publication-title: Advances in Neural Information Processing Systems – ident: 16440_CR38 doi: 10.1145/3394486.3403088 – ident: 16440_CR65 – ident: 16440_CR88 – volume: 20 start-page: 61 issue: 1 year: 2009 ident: 16440_CR61 publication-title: IEEE Transactions on Neural Networks doi: 10.1109/TNN.2008.2005605 – volume: 34 start-page: 249 issue: 1 year: 2022 ident: 16440_CR79 publication-title: IEEE Transactions on Knowledge and Data Engineering doi: 10.1109/TKDE.2020.2981333 – ident: 16440_CR3 – ident: 16440_CR32 – volume: 14 start-page: 1 issue: 3 year: 2020 ident: 16440_CR28 publication-title: Synthesis Lectures on Artificial Intelligence and Machine Learning doi: 10.1007/978-3-031-01588-5 – ident: 16440_CR51 doi: 10.1609/aimag.v29i3.2157 – ident: 16440_CR55 – ident: 16440_CR71 – volume: 35 start-page: 19414 year: 2022 ident: 16440_CR43 publication-title: Advances in Neural Information Processing Systems – volume: 33 start-page: 190 issue: 14 year: 2017 ident: 16440_CR86 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btx252 – ident: 16440_CR7 – ident: 16440_CR13 – volume: 35 start-page: 19746 year: 2022 ident: 16440_CR8 publication-title: Advances in Neural Information Processing Systems – ident: 16440_CR62 doi: 10.1109/TNN.2008.2005605 – ident: 16440_CR75 – ident: 16440_CR27 – ident: 16440_CR37 doi: 10.1109/ICDMW58026.2022.00094 – ident: 16440_CR2 doi: 10.1609/icwsm.v14i1.7347 – volume: 35 start-page: 22518 year: 2022 ident: 16440_CR56 publication-title: Advances in Neural Information Processing Systems – volume: 1 start-page: 57 year: 2020 ident: 16440_CR83 publication-title: AI Open doi: 10.1016/j.aiopen.2021.01.001 – ident: 16440_CR46 – ident: 16440_CR23 – ident: 16440_CR42 – ident: 16440_CR26 doi: 10.2200/S01045ED1V01Y202009AIM046 – volume: 198 start-page: 40 year: 2022 ident: 16440_CR29 publication-title: Proceedings of the First Learning on Graphs Conference – ident: 16440_CR39 – volume: 35 start-page: 20635 year: 2022 ident: 16440_CR17 publication-title: Advances in Neural Information Processing Systems – volume: 29 start-page: 4292 year: 2015 ident: 16440_CR58 publication-title: AAAI Conference on Artificial Intelligence – ident: 16440_CR4 – ident: 16440_CR18 – ident: 16440_CR50 doi: 10.1155/2023/1566123 – start-page: 120 volume-title: Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties year: 2018 ident: 16440_CR72 – ident: 16440_CR14 – ident: 16440_CR31 – ident: 16440_CR66 doi: 10.1145/3534678.3539387 – ident: 16440_CR76 doi: 10.1038/s41598-021-96723-8 – ident: 16440_CR78 doi: 10.1007/s41019-023-00206-x – volume: 145 start-page: 356 year: 2022 ident: 16440_CR36 publication-title: Neural Networks doi: 10.1016/j.neunet.2021.11.001 – ident: 16440_CR49 – ident: 16440_CR12 doi: 10.1109/TNN.2008.2005141 – ident: 16440_CR59 doi: 10.1609/aaai.v29i1.9277 – volume: 2020 start-page: 2704 year: 2020 ident: 16440_CR33 publication-title: Proceedings of The Web Conference – ident: 16440_CR20 – ident: 16440_CR84 doi: 10.1016/j.aiopen.2021.01.001 – volume: 198 start-page: 8 year: 2022 ident: 16440_CR34 publication-title: Proceedings of the First Learning on Graphs Conference – ident: 16440_CR45 – ident: 16440_CR41 – ident: 16440_CR57 doi: 10.1609/aaai.v34i04.5997 – ident: 16440_CR24 – volume: 8 start-page: 36 year: 2023 ident: 16440_CR77 publication-title: Data Science and Engineering doi: 10.1007/s41019-023-00206-x – ident: 16440_CR15 doi: 10.1145/3575637.3575646 – ident: 16440_CR53 – ident: 16440_CR63 doi: 10.24963/ijcai.2021/214 – volume: 9 start-page: 12 year: 1968 ident: 16440_CR69 publication-title: Nauchno-Tekhnicheskaya Informatsiya – ident: 16440_CR5 – ident: 16440_CR30 – volume: 198 start-page: 9 year: 2022 ident: 16440_CR21 publication-title: Proceedings of the First Learning on Graphs Conference – ident: 16440_CR25 – volume: 35 start-page: 21358 year: 2022 ident: 16440_CR44 publication-title: Advances in Neural Information Processing Systems – ident: 16440_CR64 doi: 10.1109/CVPR42600.2020.00178 – ident: 16440_CR67 – ident: 16440_CR82 – ident: 16440_CR40 – ident: 16440_CR1 doi: 10.1016/j.patcog.2020.107637 – ident: 16440_CR19 doi: 10.1109/CVPR.2018.00097 – ident: 16440_CR52 doi: 10.1609/aimag.v29i3.2157 – volume: 20 start-page: 81 issue: 1 year: 2009 ident: 16440_CR11 publication-title: IEEE Transactions on Neural Networks doi: 10.1109/TNN.2008.2005141 – ident: 16440_CR85 – volume: 32 start-page: 4 issue: 1 year: 2021 ident: 16440_CR70 publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2020.2978386 – ident: 16440_CR54 – ident: 16440_CR73 doi: 10.1103/PhysRevLett.120.145301 – ident: 16440_CR6 – ident: 16440_CR16 – ident: 16440_CR87 doi: 10.1093/bioinformatics/btx252 – ident: 16440_CR9 – ident: 16440_CR10 doi: 10.1609/aaai.v24i1.7519 – ident: 16440_CR35 doi: 10.1016/j.neunet.2021.11.001 – volume: 72 start-page: 943 year: 2021 ident: 16440_CR47 publication-title: Journal of Artificial Intelligence Research doi: 10.1613/jair.1.13225 – ident: 16440_CR68 – ident: 16440_CR81 – ident: 16440_CR74 doi: 10.1609/aaai.v33i01.33014602 – ident: 16440_CR60 – ident: 16440_CR22 |
| SSID | ssj0016524 |
| Score | 2.4770327 |
| Snippet | Graph Neural Networks (GNN) is one of the promising machine learning areas in solving real world problems such as social networks, recommender systems,... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 23413 |
| SubjectTerms | Algorithms Classification Computer Communication Networks Computer Science Computer vision Data Structures and Information Theory Datasets Graph neural networks Graphs Machine learning Multimedia Multimedia Information Systems Neural networks Operators (mathematics) Pattern recognition R&D Recommender systems Research & development Social networks Special Purpose and Application-Based Systems Structured data |
| SummonAdditionalLinks | – databaseName: Springer Standard Collection dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3NS8MwFH_o9KAHp1NxOiUHD4IGmjZdGm8iTg8yRcfYraRpAoLUsU7_fl_6saqooOcmIbzkvfdLf-8D4FiZ0CbaSCq1TShnyqPS-BaNoRHcpqwfFs1gxrdiOIwmE3lfJYXldbR7TUkWlrpJdmMulQR9DEWIzz3Kl2EF3V3kGjY8PI4X3AEuz6v0mO_nfXZBDa78QoUWHmbQ_t_eNmGjQpTkorwCW7Bksg60624NpFLeDqx_KD24DSfXrlI1cbU5cPKwDAYnd1NT0O75OVGkpA12YDS4Gl3e0KprAtWoTnPKFUtEwlMH_FKLeJArK33LpAh0yJT0mWHSCM-axIsEvn4iaVO0MjwRLLFhsAut7CUze0BkqoWvg8BIFXKDb1nhqBiNJsCqvqfDLrBajrGuKoq7xhbPcVML2cklRrnEhVxi3oXTxZxpWU_j19G9-njiSrfyGAGK-w_jBbILZ_VxNJ9_Xm3_b8MPYM1HBFMGnPWgNZ-9mkNY1W_zp3x2VNy5d2l_zlI priority: 102 providerName: Springer Nature |
| Title | Graph Neural Network Operators: a Review |
| URI | https://link.springer.com/article/10.1007/s11042-023-16440-4 https://www.proquest.com/docview/2930093039 |
| Volume | 83 |
| WOSCitedRecordID | wos001051759100008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: ABI/INFORM Collection customDbUrl: eissn: 1573-7721 dateEnd: 20241212 omitProxy: false ssIdentifier: ssj0016524 issn: 1573-7721 databaseCode: 7WY dateStart: 20240101 isFulltext: true titleUrlDefault: https://www.proquest.com/abicomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ABI/INFORM Global customDbUrl: eissn: 1573-7721 dateEnd: 20241212 omitProxy: false ssIdentifier: ssj0016524 issn: 1573-7721 databaseCode: M0C dateStart: 20240101 isFulltext: true titleUrlDefault: https://search.proquest.com/abiglobal providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1573-7721 dateEnd: 20241212 omitProxy: false ssIdentifier: ssj0016524 issn: 1573-7721 databaseCode: K7- dateStart: 20240101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest advanced technologies & aerospace journals customDbUrl: eissn: 1573-7721 dateEnd: 20241212 omitProxy: false ssIdentifier: ssj0016524 issn: 1573-7721 databaseCode: P5Z dateStart: 20240101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1573-7721 dateEnd: 20241212 omitProxy: false ssIdentifier: ssj0016524 issn: 1573-7721 databaseCode: BENPR dateStart: 20240101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest research library customDbUrl: eissn: 1573-7721 dateEnd: 20241212 omitProxy: false ssIdentifier: ssj0016524 issn: 1573-7721 databaseCode: M2O dateStart: 20240101 isFulltext: true titleUrlDefault: https://search.proquest.com/pqrl providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLink Journals customDbUrl: eissn: 1573-7721 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016524 issn: 1573-7721 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NS8MwFH_4ddCDH1NxfowePAgabNrULF5Ex6agzjHHnF5KmyYgyJx2-vf70qarCu7iJVDapOG95H3kvbwfwH6kAh1LJYiQOiaMRi4RytMoDBVnOqEnQQYG07_h7XZ9MBAde-CW2rTKQiZmgjp5leaM_BjVkvG-XV-cjd6IQY0y0VULoTEL88ayMSl9t25jEkXAH-WgtnWXoGak9tJMfnWOmospqLEIOgzMJeynYiqtzV8B0kzvtFb-O-NVWLYWp3OeL5E1mFHDCqwUaA6O3dwVWPpWmnAdDi5NJWvH1O7Azu08Wdy5G6ksLJ-eOpGThxU2oNdq9hpXxKIqEInbbUxYRGMes8QYholGe5FFWniaCu7LgEbCo4oKxV2tYrfO0TuqC52gFGIxp7EO_E2YG74O1RY4IpHck76vRBQwhb4uN6EaiSJCRyeuDKpAC4qG0lYcN8AXL2FZK9lwIUQuhBkXQlaFw0mfUV5vY-rXuwXpQ7v30rCkexWOCuaVr_8ebXv6aDuw6KFFkyeg7cLc-P1D7cGC_Bw_p-81mOUPjzWYv2i2O118uuaklq1G03p32HaCJ2y79_0vCnvgmg |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LS8NAEB5EBfXgoyrWZw4Kgi5mk42bFURErS2t1UMRb0uy2QVB2tpUxR_lf3Q2D6uC3jx4TnZJMrPfzGQeH8B2pAMTKy2IUCYmjEYuEdozCIaaM5PQwyAjg7lt8XY7vLsTN2PwVvbC2LLKEhMzoE56yv4jP0CzZKNv1xcn_UdiWaNsdrWk0MjVoqlfXzBkS48b5yjfHc-rXXTO6qRgFSAK1W1IWERjHrPEOkaJQX-JRUZ4hgruq4BGwqOaCs1do2M35BgdhMIkeApZzGlsLEkEIv4E80Nuj1WTk4-kBb5XzqEbugQNMS16dPJOPWr7YNBAEoxPmEvYVzs4cm6_5WMzM1eb-2cfaB5mC3_aOc0PwAKM6W4F5kquCqeArgrMfBq8uAi7l3ZOt2Mnk-Didl4K71z3dVZ0kB45kZMnTZag8xcPvwzj3V5Xr4AjEsU95ftaRAHTGMlzm4hSCIAmOnRVUAVaClCqYp66pfV4kKNJ0FboEoUuM6FLVoW9jzX9fJrIr3evl5KWBbKkciTmKuyXujK6_PNuq7_vtgVT9c5VS7Ya7eYaTHvou-WlduswPhw86Q2YVM_D-3SwmSm9A_KPdegdTIQ0pg |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bS8MwFD6IiuiD84rTqX1QEDSsadNlEUREncrG3IOI-BLaNAFBtrlNxZ_mv_Okl1UFffPB5zah7flyLj2XD2An1IGJlBZEKBMRRkOXCO0ZVIaaMxPTWpCQwdy2eLtdv7sTnQl4z3thbFllrhMTRR33lP1HXkWzZKNv1xdVk5VFdM4ax_0nYhmkbKY1p9NIIdLUb68Yvg2Prs5Q1rue1zi_Ob0kGcMAUQi9EWEhjXjEYuskxQZ9JxYa4RkquK8CGgqPaio0d42O3DrHSKEuTIwnkkWcRsYSRqD2n-IYYtpqwk5wP05g4DumfLp1l6BRplm_Ttq1R21PDBpLgrEKcwn7ahMLR_dbbjYxeY3SP_5YCzCf-dnOSXowFmFCd5eglHNYOJlKW4K5TwMZl2Hvws7vduzEElzcTkvkneu-TooRhodO6KTJlBW4-YuHX4XJbq-r18ARseKe8n0twoBpjPC5TVApVIwmrLkqKAPNhSlVNmfd0n08ymJCtAWARADIBACSlWF_vKafThn59e5KLnWZaZyhLERehoMcN8Xln3db_323bZhB6MjWVbu5AbMeunRpBV4FJkeDZ70J0-pl9DAcbCX4d0D-MYQ-AOkHPco |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Graph+Neural+Network+Operators%3A+a+Review&rft.jtitle=Multimedia+tools+and+applications&rft.au=Sharma%2C+Anuj&rft.au=Singh%2C+Sukhdeep&rft.au=Ratna%2C+S&rft.date=2024-03-01&rft.pub=Springer+Nature+B.V&rft.issn=1380-7501&rft.eissn=1573-7721&rft.volume=83&rft.issue=8&rft.spage=23413&rft.epage=23436&rft_id=info:doi/10.1007%2Fs11042-023-16440-4&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1573-7721&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1573-7721&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1573-7721&client=summon |