Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art
Graphs have been widely used to represent complex data in many applications, such as e-commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO) probl...
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| Published in: | Data Science and Engineering Vol. 6; no. 2; pp. 119 - 141 |
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01.06.2021
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| Abstract | Graphs have been widely used to represent complex data in many applications, such as e-commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO) problems, which are NP-hard. Recent studies have focused a lot on the potential of using machine learning (ML) to solve graph-based CO problems. Most recent methods follow the two-stage framework. The first stage is graph representation learning, which embeds the graphs into low-dimension vectors. The second stage uses machine learning to solve the CO problems using the embeddings of the graphs learned in the first stage. The works for the first stage can be classified into two categories, graph embedding methods and end-to-end learning methods. For graph embedding methods, the learning of the the embeddings of the graphs has its own objective, which may not rely on the CO problems to be solved. The CO problems are solved by independent downstream tasks. For end-to-end learning methods, the learning of the embeddings of the graphs does not have its own objective and is an intermediate step of the learning procedure of solving the CO problems. The works for the second stage can also be classified into two categories, non-autoregressive methods and autoregressive methods. Non-autoregressive methods predict a solution for a CO problem in one shot. A non-autoregressive method predicts a matrix that denotes the probability of each node/edge being a part of a solution of the CO problem. The solution can be computed from the matrix using search heuristics such as beam search. Autoregressive methods iteratively extend a partial solution step by step. At each step, an autoregressive method predicts a node/edge conditioned to current partial solution, which is used to its extension. In this survey, we provide a thorough overview of recent studies of the graph learning-based CO methods. The survey ends with several remarks on future research directions. |
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| AbstractList | Graphs have been widely used to represent complex data in many applications, such as e-commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO) problems, which are NP-hard. Recent studies have focused a lot on the potential of using machine learning (ML) to solve graph-based CO problems. Most recent methods follow the two-stage framework. The first stage is graph representation learning, which embeds the graphs into low-dimension vectors. The second stage uses machine learning to solve the CO problems using the embeddings of the graphs learned in the first stage. The works for the first stage can be classified into two categories, graph embedding methods and end-to-end learning methods. For graph embedding methods, the learning of the the embeddings of the graphs has its own objective, which may not rely on the CO problems to be solved. The CO problems are solved by independent downstream tasks. For end-to-end learning methods, the learning of the embeddings of the graphs does not have its own objective and is an intermediate step of the learning procedure of solving the CO problems. The works for the second stage can also be classified into two categories, non-autoregressive methods and autoregressive methods. Non-autoregressive methods predict a solution for a CO problem in one shot. A non-autoregressive method predicts a matrix that denotes the probability of each node/edge being a part of a solution of the CO problem. The solution can be computed from the matrix using search heuristics such as beam search. Autoregressive methods iteratively extend a partial solution step by step. At each step, an autoregressive method predicts a node/edge conditioned to current partial solution, which is used to its extension. In this survey, we provide a thorough overview of recent studies of the graph learning-based CO methods. The survey ends with several remarks on future research directions. |
| Audience | Academic |
| Author | Peng, Yun Choi, Byron Xu, Jianliang |
| Author_xml | – sequence: 1 givenname: Yun surname: Peng fullname: Peng, Yun organization: Department of Computer Science, Hong Kong Baptist University – sequence: 2 givenname: Byron surname: Choi fullname: Choi, Byron organization: Department of Computer Science, Hong Kong Baptist University – sequence: 3 givenname: Jianliang surname: Xu fullname: Xu, Jianliang email: xujl@comp.hkbu.edu.hk organization: Department of Computer Science, Hong Kong Baptist University |
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| Cites_doi | 10.1145/2623330.2623732 10.1145/2783258.2783417 10.2200/S00928ED1V01Y201906DTM061 10.1038/nature14236 10.24963/ijcai.2019/312 10.1109/ICDE.2016.7498232 10.1109/TKDE.2014.2316818 10.1145/3308558.3313488 10.1145/3292500.3330986 10.1145/3178876.3186120 10.1609/aaai.v34i04.5720 10.1145/2939672.2939754 10.1109/SFCS.2001.959936 10.1145/3219819.3220068 10.24963/ijcai.2019/569 10.24963/ijcai.2020/679 10.1109/TBDATA.2018.2850013 10.1145/2939672.2939751 10.1609/aaai.v31i1.10750 10.1109/TSC.2013.42 10.1109/BigData47090.2019.9005670 10.1287/ijoc.11.1.15 10.1109/DSW.2018.8439919 10.1145/2806416.2806512 10.1007/978-3-319-93031-2_12 10.1017/CBO9780511815478 10.1093/bioinformatics/btz718 10.1007/s41019-019-0092-x 10.1007/s00493-006-0008-z 10.1145/2783258.2783307 10.1017/ATSIP.2020.13 10.1109/ICDE.2019.00059 10.1109/TKDE.2018.2849727 10.1109/ICDE.2019.00125 10.1145/2939672.2939753 10.1007/978-3-030-04221-9_48 10.1007/BF02392825 10.1109/ICCV.2019.00315 10.1145/2736277.2741093 10.1609/aaai.v33i01.33014731 10.1145/227683.227684 10.1007/BF00339943 10.1007/s41019-019-0097-5 10.1145/2939672.2939673 10.1109/TKDE.2018.2807452 10.1109/ICTAI.2019.00125 10.1145/3097983.3098061 10.1145/3289600.3290967 |
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| Keywords | Graph representation learning Combinational optimization Graph neural network |
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| References | Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. In: Proceedings of international conference on neural information processing systems (NIPS’17), pp 1025–1035 ChenFWangYCWangBKuoCCJGraph representation learning: a surveyAPSIPA Trans Signal Inf Process20209e1510.1017/ATSIP.2020.13 HopfieldJTankDNeural computation of decisions in optimisation problemsBiol Cybern1985521411520572.68041 FanZPengYChoiBXuJBhowmickSSTowards efficient authenticated subgraph query service in outsourced graph databasesIEEE Trans Serv Comput20147469671310.1109/TSC.2013.42 Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: Proceedings of ACM SIGKDD International conference on knowledge discovery and data mining (KDD’16), pp 1225–1234 Li Z, Chen Q, Koltun V (2018) Combinatorial optimization with graph convolutional networks and guided tree search. In: Proceedings of international conference on neural information processing systems (NIPS’18), pp 537–546 YueXWangZHuangJParthasarathySMoosavinasabSHuangYLinSMZhangWZhangPSunHGraph embedding on biomedical networks: methods, applications and evaluationsBioinformatics201936412411251 Chang L (2019) Efficient maximum clique computation over large sparse graphs. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’19), pp 529–538 Sato R, Yamada M, Kashima H (2019) Approximation ratios of graph neural networks for combinatorial problems. In: Proceedings of the neural information processing systems (NIPS’19) PapadimitriouCHVempalaSOn the approximability of the traveling salesman problemCombinatorica2006261101120220128710.1007/s00493-006-0008-z Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2019) A comprehensive survey on graph neural networks. CoRR arXiv:1901.00596 Tu K, Cui P, Wang X, Yu PS, Zhu W (2018) Deep recursive network embedding with regular equivalence. In: Proceedings of ACM SIGKDD international conference on knowledge discovery & data mining (KDD’18), pp 2357–2366 Nguyen H, Murata T (2017) Motif-aware graph embeddings. In: Proceedings of international joint conference on artificial intelligence (IJCAI’17), pp 1–1 Huang W, Zhang T, Rong Y, Huang J (2018) Adaptive sampling towards fast graph representation learning. In: Proceedings of international conference on neural information processing systems (NIPS’18), pp 4563–4572 Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th international conference on neural information processing systems (NIPS’13), pp 3111–3119 Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) LINE: Large-scale information network embedding. In: Proceedings of international conference on world wide web (WWW’15), pp 1067–1077 Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2018) Graph Attention Networks. In: International conference on learning representations (ICLR) CaiHZhengVWChangKA comprehensive survey of graph embedding: problems, techniques, and applicationsIEEE Trans Knowl Data Eng201830091616163710.1109/TKDE.2018.2807452 Cao S, Lu W, Xu Q (2015) GraRep: learning graph representations with global structural information. In: Proceedings of ACM international on conference on information and knowledge management (CIKM’15), pp 891–900 DaveVSZhangBChenPYAl HasanMNeural-brane: neural bayesian personalized ranking for attributed network embeddingData Sci Eng20194211913110.1007/s41019-019-0092-x Ribeiro LF, Saverese PH, Figueiredo DR (2017) Struc2Vec: Learning node representations from structural identity. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’17), pp 385–394 Khot S (2001) Improved inapproximability results for maxclique, chromatic number and approximate graph coloring. In: Proceedings IEEE symposium on foundations of computer science, pp 600–609 JinWBarzilayRJaakkolaTJunction tree variational autoencoder for molecular graph generationProc Int Conf Mach Learn20188023232332 Li Y, Gu C, Dullien T, Vinyals O, Kohli P (2019) Graph matching networks for learning the similarity of graph structured objects. In: Proceedings of international conference on machine learning (ICML’19), pp 3835–3845 Wu Y, Song W, Cao Z, Zhang J, Lim A (2019) Learning improvement heuristics for solving the travelling salesman problem. CoRR arXiv:1912.05784 HåstadJClique is hard to approximate within n1-ε\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n^{1-\varepsilon }$$\end{document}Acta Math19991821105142168733110.1007/BF02392825 Ou M, Cui P, Pei J, Zhang Z, Zhu W (2016) Asymmetric transitivity preserving graph embedding. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’16), pp 1105–1114 Meng L, Zhang J (2019) IsoNN: Isomorphic neural network for graph representation learning and classification. CoRR arXiv:1907.09495 Tang J, Qu M, Mei Q (2015) PTE: Predictive text embedding through large-scale heterogeneous text networks. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’15), pp 1165–1174 Bengio Y, Lodi A, Prouvost A (2018) Machine learning for combinatorial optimization: A methodological tour d’horizon. arXiv:1811.06128 Wang R, Yan J, Yang X (2019) Learning combinatorial embedding networks for deep graph matching. In: Proceedings of the IEEE international conference on computer vision (ICCV’19), pp 3056–3065 Yang Y, Wang X, Song M, Yuan J, Tao D (2019) SPAGAN: Shortest path graph attention network. In: Proceedings of international joint conference on artificial intelligence (IJCAI’19), pp 4099–4105 Joshi CK, Cappart Q, Rousseau LM, Laurent T, Bresson X (2020) Learning TSP requires rethinking generalization. arXiv:2006.07054 Nazi A, Hang W, Goldie A, Ravi S, Mirhoseini A (2019) GAP : Generalizable approximate graph partitioning framework. URL ICLR workshop Chami I, Abu-El-Haija S, Perozzi B, Ré C, Murphy K (2020) Machine learning on graphs: a model and comprehensive taxonomy. arXiv:2005.03675 Kool W, van Hoof H, Welling M (2019) Attention, learn to solve routing problems! In: International conference on learning representations Yanardag P, Vishwanathan S (2015) Deep graph kernels. In: Proceedings of ACM sigkdd international conference on knowledge discovery and data mining (KDD’15), pp 1365–1374 PengYFanZChoiBXuJBhowmickSSAuthenticated subgraph similarity searchin outsourced graph databasesIEEE Trans Knowl Data Eng20152771838186010.1109/TKDE.2014.2316818 Deudon M, Cournut P, Lacoste A, Adulyasak Y, Rousseau LM (2018) Learning heuristics for the TSP by policy gradient. In: International conference on the integration of constraint programming, artificial intelligence, and operations research, pp 170–181 VinyalsOFortunatoMJaitlyNPointer networksAdv Neural Inf Process Syst20152826922700 Joshi CK, Laurent T, Bresson X (2019) An efficient graph convolutional network technique for the travelling salesman problem. CoRR arXiv:1906.01227 SuttonRSBartoAGReinforcement learning: an introduction2018CambridgeMIT Press1407.68009 Abe K, Xu Z, Sato I, Sugiyama M (2019) Solving np-hard problems on graphs with extended alphago zero. arXiv:1905.11623 HuangXLakshmananLVXuJCommunity search over big graphs2019New YorkMorgan & Claypool Publishers10.2200/S00928ED1V01Y201906DTM061 Mazyavkina N, Sviridov S, Ivanov S, Burnaev E (2020) Reinforcement learning for combinatorial optimization: a survey. arXiv:2003.03600 DevlinJChangMWLeeKToutanovaKBERT: Pre-training of deep bidirectional transformers for language understandingProc Conf N Am Chapter Assoc Comput Linguist Hum Lang Technol2019141714186 Hamilton WL, Ying R, Leskovec J (2017) Representation learning on graphs: methods and applications. IEEE Data Eng Bull Huang J, Patwary MMA, Diamos GF (2019) Coloring big graphs with alphagozero. CoRR arXiv:1902.10162 Grover A, Leskovec J (2016) Node2Vec: Scalable feature learning for networks. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’16), pp 855–864 Dareddy MR, Das M, Yang H (2019) Motif2Vec: Motif aware node representation learning for heterogeneous networks. In: 2019 IEEE international conference on big data (Big Data’19), pp 1052–1059 Peng Y, Choi B, He B, Zhou S, Xu R, Yu X (2016) VColor: A practical vertex-cut based approach for coloring large graphs. In: IEEE international conference on data engineering (ICDE’16), pp 97–108 DuvenaudDKMaclaurinDIparraguirreJBombarellRHirzelTAspuru-GuzikAAdamsRPConvolutional networks on graphs for learning molecular fingerprintsAdv Neural Inf Process Syst20152822242232 Yu Y, Lu Z, Liu J, Zhao G, Wen J (2019) RUM: Network representation learning using motifs. In: IEEE international conference on data engineering (ICDE’19), pp 1382–1393 WassermanSFaustKSocial network analysis: methods and applications1994CambridgeCambridge University Press10.1017/CBO9780511815478 MnihVKavukcuogluKSilverDRusuAAVenessJBellemareMGGravesARiedmillerMFidjelandAKOstrovskiGHuman-level control through deep reinforcement learningNature2015518754052953310.1038/nature14236 Dai H, Khalil EB, Zhang Y, Dilkina B, Song L (2017) Learning combinatorial optimization algorithms over graphs. In: Proceedings of international conference on neural information processing systems (NIPS’17), pp 6351–6361 Smith-MilesKNeural networks for combinatorial optimization: a review of more than a decade of researchINFORMS J Comput1999111534168812410.1287/ijoc.11.1.15 Nowak A, Villar S, Bandeira AS, Bruna J (2017) A note on learning algorithms for quadratic assignment with graph neural networks. In: Proceeding of international conference on machine learning (ICML’17), pp 22–22 Tsitsulin A, Mottin D, Karras P, Müller E (2018) VERSE: Versatile graph embeddings from similarity measures. In: Proceedings of RS Sutton (155_CR61) 2018 CH Papadimitriou (155_CR53) 2006; 26 H Cai (155_CR7) 2018; 30 S Wasserman (155_CR70) 1994 J Hopfield (155_CR28) 1985; 52 155_CR39 155_CR38 155_CR37 F Chen (155_CR11) 2020; 9 155_CR36 155_CR46 155_CR45 155_CR44 155_CR43 155_CR42 155_CR41 155_CR40 X Yue (155_CR76) 2019; 36 J Devlin (155_CR18) 2019; 1 D Zhang (155_CR77) 2020; 6 S Bonner (155_CR6) 2019; 4 155_CR49 VS Dave (155_CR16) 2019; 4 155_CR48 155_CR57 155_CR12 155_CR56 155_CR10 155_CR54 155_CR52 155_CR51 155_CR50 O Vinyals (155_CR67) 2015; 28 P Cui (155_CR13) 2019; 31 MX Goemans (155_CR23) 1995; 42 J Håstad (155_CR27) 1999; 182 Y Peng (155_CR55) 2015; 27 155_CR19 K Smith-Miles (155_CR60) 1999; 11 155_CR17 155_CR15 DK Duvenaud (155_CR20) 2015; 28 W Jin (155_CR32) 2018; 80 155_CR59 155_CR14 155_CR58 155_CR24 155_CR68 155_CR66 155_CR21 155_CR65 155_CR64 155_CR63 155_CR62 Z Fan (155_CR22) 2014; 7 155_CR5 155_CR8 155_CR9 155_CR29 155_CR2 155_CR1 155_CR4 155_CR26 155_CR3 155_CR25 155_CR69 155_CR35 155_CR34 155_CR78 155_CR33 V Mnih (155_CR47) 2015; 518 X Huang (155_CR31) 2019 155_CR75 155_CR30 155_CR74 155_CR73 155_CR72 155_CR71 |
| References_xml | – reference: Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th international conference on neural information processing systems (NIPS’13), pp 3111–3119 – reference: MnihVKavukcuogluKSilverDRusuAAVenessJBellemareMGGravesARiedmillerMFidjelandAKOstrovskiGHuman-level control through deep reinforcement learningNature2015518754052953310.1038/nature14236 – reference: Chami I, Abu-El-Haija S, Perozzi B, Ré C, Murphy K (2020) Machine learning on graphs: a model and comprehensive taxonomy. arXiv:2005.03675 – reference: Ou M, Cui P, Pei J, Zhang Z, Zhu W (2016) Asymmetric transitivity preserving graph embedding. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’16), pp 1105–1114 – reference: SuttonRSBartoAGReinforcement learning: an introduction2018CambridgeMIT Press1407.68009 – reference: Yang Y, Wang X, Song M, Yuan J, Tao D (2019) SPAGAN: Shortest path graph attention network. In: Proceedings of international joint conference on artificial intelligence (IJCAI’19), pp 4099–4105 – reference: Yanardag P, Vishwanathan S (2015) Deep graph kernels. In: Proceedings of ACM sigkdd international conference on knowledge discovery and data mining (KDD’15), pp 1365–1374 – reference: Milan A, Rezatofighi SH, Garg R, Dick A, Reid I (2017) Data-driven approximations to np-hard problems. In: Thirty-first AAAI conference on artificial intelligence – reference: Li Z, Chen Q, Koltun V (2018) Combinatorial optimization with graph convolutional networks and guided tree search. In: Proceedings of international conference on neural information processing systems (NIPS’18), pp 537–546 – reference: Kool W, van Hoof H, Welling M (2019) Attention, learn to solve routing problems! In: International conference on learning representations – reference: Chang L (2019) Efficient maximum clique computation over large sparse graphs. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’19), pp 529–538 – reference: Du X, Yan J, Zha H (2019) Joint link prediction and network alignment via cross-graph embedding. In: Proceedings of international joint conference on artificial intelligence, IJCAI’19, pp 2251–2257 – reference: HopfieldJTankDNeural computation of decisions in optimisation problemsBiol Cybern1985521411520572.68041 – reference: PapadimitriouCHVempalaSOn the approximability of the traveling salesman problemCombinatorica2006261101120220128710.1007/s00493-006-0008-z – reference: Deudon M, Cournut P, Lacoste A, Adulyasak Y, Rousseau LM (2018) Learning heuristics for the TSP by policy gradient. In: International conference on the integration of constraint programming, artificial intelligence, and operations research, pp 170–181 – reference: Prates M, Avelar P, Lemos H, Lamb L, Vardi M (2019) Learning to solve NP-Complete problems - a graph neural network for decision TSP. In: Proceedings of AAAI conference on artificial intelligence (AAAI’19), pp 4731–4738 – reference: Cao S, Lu W, Xu Q (2015) GraRep: learning graph representations with global structural information. In: Proceedings of ACM international on conference on information and knowledge management (CIKM’15), pp 891–900 – reference: Tang J, Qu M, Mei Q (2015) PTE: Predictive text embedding through large-scale heterogeneous text networks. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’15), pp 1165–1174 – reference: Grover A, Leskovec J (2016) Node2Vec: Scalable feature learning for networks. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’16), pp 855–864 – reference: Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: Proceedings of international conference on international conference on machine learning, vol 32, ICML’14, pp II–1188–II–1196 – reference: Peng Y, Choi B, He B, Zhou S, Xu R, Yu X (2016) VColor: A practical vertex-cut based approach for coloring large graphs. In: IEEE international conference on data engineering (ICDE’16), pp 97–108 – reference: Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. In: Proceedings of international conference on neural information processing systems (NIPS’17), pp 1025–1035 – reference: PengYFanZChoiBXuJBhowmickSSAuthenticated subgraph similarity searchin outsourced graph databasesIEEE Trans Knowl Data Eng20152771838186010.1109/TKDE.2014.2316818 – reference: Bai Y, Ding H, Bian S, Chen T, Sun Y, Wang W (2019) SimGNN: a neural network approach to fast graph similarity computation. In: Proceedings of the ACM international conference on web search and data mining (WSDM’19), pp 384–392 – reference: JinWBarzilayRJaakkolaTJunction tree variational autoencoder for molecular graph generationProc Int Conf Mach Learn20188023232332 – reference: Bengio Y, Lodi A, Prouvost A (2018) Machine learning for combinatorial optimization: A methodological tour d’horizon. arXiv:1811.06128 – reference: Huang W, Zhang T, Rong Y, Huang J (2018) Adaptive sampling towards fast graph representation learning. In: Proceedings of international conference on neural information processing systems (NIPS’18), pp 4563–4572 – reference: Joshi CK, Cappart Q, Rousseau LM, Laurent T, Bresson X (2020) Learning TSP requires rethinking generalization. arXiv:2006.07054 – reference: Perozzi B, Al-Rfou R, Skiena S (2014) DeepWalk: Online learning of social representations. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’14), pp 701–710 – reference: WassermanSFaustKSocial network analysis: methods and applications1994CambridgeCambridge University Press10.1017/CBO9780511815478 – reference: Abe K, Xu Z, Sato I, Sugiyama M (2019) Solving np-hard problems on graphs with extended alphago zero. arXiv:1905.11623 – reference: CaiHZhengVWChangKA comprehensive survey of graph embedding: problems, techniques, and applicationsIEEE Trans Knowl Data Eng201830091616163710.1109/TKDE.2018.2807452 – reference: Tsitsulin A, Mottin D, Karras P, Müller E (2018) VERSE: Versatile graph embeddings from similarity measures. In: Proceedings of world wide web conference (WWW’18), pp 539–548 – reference: Lamb LC, Garcez AD, Gori M, Prates MO, Avelar PH, Vardi MY (2020) Graph neural networks meet neural-symbolic computing: a survey and perspective. In: Proceedings of international joint conference on artificial intelligence (IJCAI’20), pp 4877–4884 – reference: VinyalsOFortunatoMJaitlyNPointer networksAdv Neural Inf Process Syst20152826922700 – reference: Zhang F, Yuan NJ, Lian D, Xie X, Ma WY (2016) Collaborative knowledge base embedding for recommender systems. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’16), pp 353–362 – reference: Nguyen H, Murata T (2017) Motif-aware graph embeddings. In: Proceedings of international joint conference on artificial intelligence (IJCAI’17), pp 1–1 – reference: Nazi A, Hang W, Goldie A, Ravi S, Mirhoseini A (2019) GAP : Generalizable approximate graph partitioning framework. URL ICLR workshop – reference: Fan W, Ma Y, Li Q, He Y, Zhao E, Tang J, Yin D (2019) Graph neural networks for social recommendation. In: The world wide web conference, pp 417–426 – reference: Hamilton WL, Ying R, Leskovec J (2017) Representation learning on graphs: methods and applications. IEEE Data Eng Bull – reference: Lemos H, Prates MOR, Avelar PHC, Lamb LC (2019) Graph colouring meets deep learning: Effective graph neural network models for combinatorial problems. CoRR arXiv:1903.04598 – reference: Nowak A, Villar S, Bandeira AS, Bruna J (2017) A note on learning algorithms for quadratic assignment with graph neural networks. In: Proceeding of international conference on machine learning (ICML’17), pp 22–22 – reference: Ribeiro LF, Saverese PH, Figueiredo DR (2017) Struc2Vec: Learning node representations from structural identity. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’17), pp 385–394 – reference: Bai Y, Xu D, Wang A, Gu K, Wu X, Marinovic A, Ro C, Sun Y, Wang W (2020) Fast detection of maximum common subgraph via deep Q-learning. arXiv:2002.03129 – reference: Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2019) A comprehensive survey on graph neural networks. CoRR arXiv:1901.00596 – reference: Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) LINE: Large-scale information network embedding. In: Proceedings of international conference on world wide web (WWW’15), pp 1067–1077 – reference: Tu K, Cui P, Wang X, Yu PS, Zhu W (2018) Deep recursive network embedding with regular equivalence. In: Proceedings of ACM SIGKDD international conference on knowledge discovery & data mining (KDD’18), pp 2357–2366 – reference: ZhangDYinJZhuXZhangCNetwork representation learning: a surveyIEEE Trans Big Data20206132810.1109/TBDATA.2018.2850013 – reference: Huang J, Patwary MMA, Diamos GF (2019) Coloring big graphs with alphagozero. CoRR arXiv:1902.10162 – reference: CuiPWangXPeiJZhuWA survey on network embeddingIEEE Trans Knowl Data Eng201931583385210.1109/TKDE.2018.2849727 – reference: BonnerSKureshiIBrennanJTheodoropoulosGMcGoughASObaraBExploring the semantic content of unsupervised graph embeddings: an empirical studyData Sci Eng20194326928910.1007/s41019-019-0097-5 – reference: Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations (ICLR’17) – reference: Wu Y, Song W, Cao Z, Zhang J, Lim A (2019) Learning improvement heuristics for solving the travelling salesman problem. CoRR arXiv:1912.05784 – reference: ChenFWangYCWangBKuoCCJGraph representation learning: a surveyAPSIPA Trans Signal Inf Process20209e1510.1017/ATSIP.2020.13 – reference: Yu Y, Lu Z, Liu J, Zhao G, Wen J (2019) RUM: Network representation learning using motifs. In: IEEE international conference on data engineering (ICDE’19), pp 1382–1393 – reference: Mazyavkina N, Sviridov S, Ivanov S, Burnaev E (2020) Reinforcement learning for combinatorial optimization: a survey. arXiv:2003.03600 – reference: Joshi CK, Laurent T, Bresson X (2019) An efficient graph convolutional network technique for the travelling salesman problem. CoRR arXiv:1906.01227 – reference: Li Y, Gu C, Dullien T, Vinyals O, Kohli P (2019) Graph matching networks for learning the similarity of graph structured objects. In: Proceedings of international conference on machine learning (ICML’19), pp 3835–3845 – reference: DaveVSZhangBChenPYAl HasanMNeural-brane: neural bayesian personalized ranking for attributed network embeddingData Sci Eng20194211913110.1007/s41019-019-0092-x – reference: HuangXLakshmananLVXuJCommunity search over big graphs2019New YorkMorgan & Claypool Publishers10.2200/S00928ED1V01Y201906DTM061 – reference: Meng L, Zhang J (2019) IsoNN: Isomorphic neural network for graph representation learning and classification. CoRR arXiv:1907.09495 – reference: Wang R, Yan J, Yang X (2019) Learning combinatorial embedding networks for deep graph matching. In: Proceedings of the IEEE international conference on computer vision (ICCV’19), pp 3056–3065 – reference: YueXWangZHuangJParthasarathySMoosavinasabSHuangYLinSMZhangWZhangPSunHGraph embedding on biomedical networks: methods, applications and evaluationsBioinformatics201936412411251 – reference: DevlinJChangMWLeeKToutanovaKBERT: Pre-training of deep bidirectional transformers for language understandingProc Conf N Am Chapter Assoc Comput Linguist Hum Lang Technol2019141714186 – reference: HåstadJClique is hard to approximate within n1-ε\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n^{1-\varepsilon }$$\end{document}Acta Math19991821105142168733110.1007/BF02392825 – reference: Bai Y, Ding H, Gu K, Sun Y, Wang W (2020) Learning-based efficient graph similarity computation via multi-scale convolutional set matching. In: Proceedings of the AAAI conference on artificial intelligence (AAAI’20), pp 3219–3226 – reference: DuvenaudDKMaclaurinDIparraguirreJBombarellRHirzelTAspuru-GuzikAAdamsRPConvolutional networks on graphs for learning molecular fingerprintsAdv Neural Inf Process Syst20152822242232 – reference: Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2018) Graph Attention Networks. In: International conference on learning representations (ICLR) – reference: GoemansMXWilliamsonDPImproved approximation algorithms for maximum cut and satisfiability problems using semidefinite programmingJ ACM199542611151145141222810.1145/227683.227684 – reference: Chen H, Yin H, Chen T, Nguyen QVH, Peng W, Li X (2019) Exploiting centrality information with graph convolutions for network representation learning. In: IEEE international conference on data engineering (ICDE’19), pp 590–601 – reference: FanZPengYChoiBXuJBhowmickSSTowards efficient authenticated subgraph query service in outsourced graph databasesIEEE Trans Serv Comput20147469671310.1109/TSC.2013.42 – reference: Dareddy MR, Das M, Yang H (2019) Motif2Vec: Motif aware node representation learning for heterogeneous networks. In: 2019 IEEE international conference on big data (Big Data’19), pp 1052–1059 – reference: Narayanan A, Chandramohan M, Chen L, Liu Y, Saminathan S, Subgraph2Vec: learning distributed representations of rooted sub-graphs from large graphs – reference: Smith-MilesKNeural networks for combinatorial optimization: a review of more than a decade of researchINFORMS J Comput1999111534168812410.1287/ijoc.11.1.15 – reference: Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: Proceedings of ACM SIGKDD International conference on knowledge discovery and data mining (KDD’16), pp 1225–1234 – reference: Khot S (2001) Improved inapproximability results for maxclique, chromatic number and approximate graph coloring. In: Proceedings IEEE symposium on foundations of computer science, pp 600–609 – reference: Dai H, Khalil EB, Zhang Y, Dilkina B, Song L (2017) Learning combinatorial optimization algorithms over graphs. In: Proceedings of international conference on neural information processing systems (NIPS’17), pp 6351–6361 – reference: Sato R, Yamada M, Kashima H (2019) Approximation ratios of graph neural networks for combinatorial problems. In: Proceedings of the neural information processing systems (NIPS’19) – ident: 155_CR56 doi: 10.1145/2623330.2623732 – ident: 155_CR73 doi: 10.1145/2783258.2783417 – ident: 155_CR4 – volume-title: Community search over big graphs year: 2019 ident: 155_CR31 doi: 10.2200/S00928ED1V01Y201906DTM061 – ident: 155_CR33 – volume: 518 start-page: 529 issue: 7540 year: 2015 ident: 155_CR47 publication-title: Nature doi: 10.1038/nature14236 – ident: 155_CR19 doi: 10.24963/ijcai.2019/312 – ident: 155_CR54 doi: 10.1109/ICDE.2016.7498232 – volume: 27 start-page: 1838 issue: 7 year: 2015 ident: 155_CR55 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2014.2316818 – ident: 155_CR21 doi: 10.1145/3308558.3313488 – ident: 155_CR10 doi: 10.1145/3292500.3330986 – ident: 155_CR14 – ident: 155_CR64 doi: 10.1145/3178876.3186120 – ident: 155_CR3 doi: 10.1609/aaai.v34i04.5720 – ident: 155_CR24 doi: 10.1145/2939672.2939754 – ident: 155_CR37 – ident: 155_CR71 – ident: 155_CR35 doi: 10.1109/SFCS.2001.959936 – ident: 155_CR65 doi: 10.1145/3219819.3220068 – ident: 155_CR74 doi: 10.24963/ijcai.2019/569 – ident: 155_CR1 – ident: 155_CR38 doi: 10.24963/ijcai.2020/679 – ident: 155_CR26 – volume: 6 start-page: 3 issue: 1 year: 2020 ident: 155_CR77 publication-title: IEEE Trans Big Data doi: 10.1109/TBDATA.2018.2850013 – ident: 155_CR52 doi: 10.1145/2939672.2939751 – ident: 155_CR46 doi: 10.1609/aaai.v31i1.10750 – volume: 7 start-page: 696 issue: 4 year: 2014 ident: 155_CR22 publication-title: IEEE Trans Serv Comput doi: 10.1109/TSC.2013.42 – ident: 155_CR43 – ident: 155_CR15 doi: 10.1109/BigData47090.2019.9005670 – volume: 11 start-page: 15 year: 1999 ident: 155_CR60 publication-title: INFORMS J Comput doi: 10.1287/ijoc.11.1.15 – ident: 155_CR51 doi: 10.1109/DSW.2018.8439919 – volume: 28 start-page: 2692 year: 2015 ident: 155_CR67 publication-title: Adv Neural Inf Process Syst – ident: 155_CR8 doi: 10.1145/2806416.2806512 – ident: 155_CR17 doi: 10.1007/978-3-319-93031-2_12 – ident: 155_CR36 – volume-title: Social network analysis: methods and applications year: 1994 ident: 155_CR70 doi: 10.1017/CBO9780511815478 – ident: 155_CR29 – volume: 36 start-page: 1241 issue: 4 year: 2019 ident: 155_CR76 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btz718 – volume: 1 start-page: 4171 year: 2019 ident: 155_CR18 publication-title: Proc Conf N Am Chapter Assoc Comput Linguist Hum Lang Technol – ident: 155_CR48 – ident: 155_CR25 – volume: 4 start-page: 119 issue: 2 year: 2019 ident: 155_CR16 publication-title: Data Sci Eng doi: 10.1007/s41019-019-0092-x – volume: 26 start-page: 101 issue: 1 year: 2006 ident: 155_CR53 publication-title: Combinatorica doi: 10.1007/s00493-006-0008-z – ident: 155_CR62 doi: 10.1145/2783258.2783307 – volume: 9 start-page: e15 year: 2020 ident: 155_CR11 publication-title: APSIPA Trans Signal Inf Process doi: 10.1017/ATSIP.2020.13 – ident: 155_CR12 doi: 10.1109/ICDE.2019.00059 – volume: 31 start-page: 833 issue: 5 year: 2019 ident: 155_CR13 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2018.2849727 – ident: 155_CR44 – ident: 155_CR75 doi: 10.1109/ICDE.2019.00125 – ident: 155_CR9 – ident: 155_CR50 – ident: 155_CR39 – volume-title: Reinforcement learning: an introduction year: 2018 ident: 155_CR61 – ident: 155_CR68 doi: 10.1145/2939672.2939753 – ident: 155_CR30 – ident: 155_CR42 doi: 10.1007/978-3-030-04221-9_48 – volume: 182 start-page: 105 issue: 1 year: 1999 ident: 155_CR27 publication-title: Acta Math doi: 10.1007/BF02392825 – ident: 155_CR5 – ident: 155_CR69 doi: 10.1109/ICCV.2019.00315 – ident: 155_CR63 doi: 10.1145/2736277.2741093 – ident: 155_CR57 doi: 10.1609/aaai.v33i01.33014731 – ident: 155_CR45 – ident: 155_CR49 – volume: 42 start-page: 1115 issue: 6 year: 1995 ident: 155_CR23 publication-title: J ACM doi: 10.1145/227683.227684 – ident: 155_CR66 – volume: 52 start-page: 141 year: 1985 ident: 155_CR28 publication-title: Biol Cybern doi: 10.1007/BF00339943 – ident: 155_CR41 – volume: 4 start-page: 269 issue: 3 year: 2019 ident: 155_CR6 publication-title: Data Sci Eng doi: 10.1007/s41019-019-0097-5 – ident: 155_CR78 doi: 10.1145/2939672.2939673 – volume: 28 start-page: 2224 year: 2015 ident: 155_CR20 publication-title: Adv Neural Inf Process Syst – volume: 80 start-page: 2323 year: 2018 ident: 155_CR32 publication-title: Proc Int Conf Mach Learn – volume: 30 start-page: 1616 issue: 09 year: 2018 ident: 155_CR7 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2018.2807452 – ident: 155_CR40 doi: 10.1109/ICTAI.2019.00125 – ident: 155_CR72 – ident: 155_CR34 – ident: 155_CR58 doi: 10.1145/3097983.3098061 – ident: 155_CR2 doi: 10.1145/3289600.3290967 – ident: 155_CR59 |
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| SubjectTerms | Algorithm Analysis and Problem Complexity Artificial Intelligence Bioinformatics Carbon monoxide Chemistry and Earth Sciences Combinatorial analysis Computer Science Data Mining and Knowledge Discovery Database Management Embedding Graph representations Graphical representations Graphs Machine learning Optimization Physics Social networks Statistics for Engineering Surveys Systems and Data Security |
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