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A subgraph is constructed by using a subset of vertices and edges of a given graph.
Jiang H, Song Y, Wang C, Zhang M, Sun Y (2017) Semi-supervised learning over heterogeneous information networks by ensemble of meta-graph guided random walks. In: 26th IJCAI, pp 1944–1950
1950
Earlier work this paper cites.
Harary F, Norman RZ (1953) Graph theory as a mathematical model in social science. University of Michigan, Institute for Social Research, Ann Arbor
1953
Earlier work this paper cites.
Erdős P, Rényi A (1960) On the evolution of random graphs. Publ Math Inst Hung Acad Sci 5(1):17–60
1960
Earlier work this paper cites.
Weisfeiler B, Leman A (1968) The reduction of a graph to canonical form and the algebra which appears therein. Nauchno-Technicheskaya Informatsia 2(9):12–16
1968
Earlier work this paper cites.
Liu X, Pan H, He M, Song Y, Jiang X, Shang L (2020a) Neural subgraph isomorphism counting. In: Proceedings of the 26th ACM SIGKDD, pp 1959–1969, doi: 10.1145/3394486.3403247
1969
Earlier work this paper cites.
Cook SA (1971) The complexity of theorem-proving procedures. In: Proceedings of the third annual ACM STOC, pp 151–158, doi: 10.1145/800157.805047
1971
Earlier work this paper cites.
Levi G (1973) A note on the derivation of maximal common subgraphs of two directed or undirected graphs. Calcolo 9:341–352, doi: 10.1007/BF02575586
1973
Earlier work this paper cites.
Bondy JA, Murty USR (1976) Graph theory with applications. The Macmillan Press Ltd, New York
1976
Earlier work this paper cites.
Garey MR, Johnson DS (1979) Computers and intractability: A guide to the theory of NP-completeness. W. H. Freeman and Co, San Francisco, USA
1979
Earlier work this paper cites.
Bokhari (1981) On the mapping problem. IEEE Transactions on Computers C-30(3):207–214, doi: 10.1109/TC.1981.1675756
1981
Earlier work this paper cites.
Seidman SB (1983) Network structure and minimum degree. Social Networks 5(3):269–287, doi: 10.1016/0378-8733(83)90028-X
1983
Earlier work this paper cites.
Goldberg AV (1984) Finding a maximum density subgraph. Technical Report , University of California, Berkeley
1984
Earlier work this paper cites.
Gibbons A (1985) Algorithmic graph theory. Cambridge University Press
1985
Earlier work this paper cites.
Whitney H (1992) Congruent graphs and the connectivity of graphs. In: Hassler Whitney Collected Papers. Contemporary Mathematicians, pp 61–79, doi: 10.1007/978-1-4612-2972-8_4
1992
Earlier work this paper cites.
Bunke H (1997) On a relation between graph edit distance and maximum common subgraph. Pattern Recognition Letters 18(8):689–694, doi: 10.1016/S0167-8655(97)00060-3
1997
Earlier work this paper cites.
Djoko S, Cook DJ, Holder LB (1997) An empirical study of domain knowledge and its benefits to substructure discovery. IEEE Transactions on Knowledge and Data Engineering 9(4):575–586, doi: 10.1109/69.617051
1997
Earlier work this paper cites.
Asahiro Y, Iwama K, Tamaki H, Tokuyama T (2000) Greedily finding a dense subgraph. Journal of Algorithms 34(2):203–221, doi: 10.1006/jagm.1999.1062
1999
Earlier work this paper cites.
Hayes B (2000) Graph theory in practice: Part I. Amer Scientist 88(1):9–13
2000
Earlier work this paper cites.
Karp RM (1972) Reducibility among combinatorial problems. In: Complexity of computer computations, Springer, Boston, MA, pp 85–103, doi: 10.1007/978-1-4684-2001-2_9
2001
Earlier work this paper cites.
Nowicki K, Snijders TAB (2001) Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association 96(455):1077–1087, doi: 10.1198/016214501753208735
2001
Earlier work this paper cites.
Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Reviews of modern physics 74(1):47
2002
Earlier work this paper cites.
Asahiro Y, Hassin R, Iwama K (2002) Complexity of finding dense subgraphs. Discrete Applied Mathematics 121(1-3):15–26, doi: 10.1016/S0166-218X(01)00243-8
2002
Earlier work this paper cites.
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2):182–197, doi: 10.1109/4235.996017
2002
Earlier work this paper cites.
Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99(12):7821–7826, doi: 10.1073/pnas.122653799
2002
Earlier work this paper cites.
Hutter F, Tompkins DAD, Hoos HH (2002) Scaling and probabilistic smoothing: Efficient dynamic local search for SAT. In: Principles and Practice of Constraint Programming–CP 2002, pp 233–248, doi: 10.1007/3-540-46135-3_16
2002
Earlier work this paper cites.
Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U (2002) Network motifs: Simple building blocks of complex networks. Science 298(5594):824–827, doi: 10.1126/science.298.5594.824
2002
Earlier work this paper cites.
Huan J, Wang W, Prins J (2003) Efficient mining of frequent subgraphs in the presence of isomorphism. In: Third IEEE ICDM, pp 549–552, doi: 10.1109/ICDM.2003.1250974
2003
Earlier work this paper cites.
Cordella LP, Foggia P, Sansone C, Vento M (2004) A (sub)graph isomorphism algorithm for matching large graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(10):1367–1372, doi: 10.1109/TPAMI.2004.75
2004
Earlier work this paper cites.
Kuramochi M, Karypis G (2004) GREW–A scalable frequent subgraph discovery algorithm. In: Fourth IEEE ICDM, pp 439–442, doi: 10.1109/ICDM.2004.10024
2004
Earlier work this paper cites.
Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026,113, doi: 10.1103/PhysRevE.69.026113
2004
Earlier work this paper cites.
Faccioli P, Provero P, Herrmann C, Stanca AM, Morcia C, Terzi V (2005) From single genes to co-expression networks: Extracting knowledge from barley functional genomics. Plant Molecular Biology 58(5):739–750, doi: 10.1007/s11103-005-8159-7
2005
Earlier work this paper cites.
Yan X, Yu PS, Han J (2005) Substructure similarity search in graph databases. In: Proceedings of the 2005 ACM SIGMOD, pp 766–777, doi: 10.1145/1066157.1066244
2005
Earlier work this paper cites.
Applegate D, Bixby R, Chvátal V, Cook W (2006) Concorde TSP solver http://www.math.uwaterloo.ca/tsp/concorde/
2006
Earlier work this paper cites.
Cootes AP, Muggleton SH, Sternberg MJE (2007) The identification of similarities between biological networks: Application to the metabolome and interactome. Journal of Molecular Biology 369(4):1126–1139, doi: 10.1016/j.jmb.2007.03.013
2007
Earlier work this paper cites.
2007
Earlier work this paper cites.
Alon N, Dao P, Hajirasouliha I, Hormozdiari F, Sahinalp SC (2008) Biomolecular network motif counting and discovery by color coding. Bioinformatics 24(13):i241–i249
2008
Earlier work this paper cites.
Bang-Jensen J, Gutin GZ (2008) Digraphs: Theory, algorithms and applications ( 2 n d 2^{nd} edition). Springer
2008
Earlier work this paper cites.
Cohen J (2008) Trusses: Cohesive subgraphs for social network analysis. National security agency technical report 16(3.1)
2008
Earlier work this paper cites.
Cohen R, Katzir L (2008) The generalized maximum coverage problem. Information Processing Letters 108(1):15–22, doi: 10.1016/j.ipl.2008.03.017
2008
Earlier work this paper cites.
Vismara P, Valery B (2008) Finding maximum common connected subgraphs using clique detection or constraint satisfaction algorithms. In: International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences, pp 358–368, doi: 10.1007/978-3-540-87477-5_39
2008
Earlier work this paper cites.
Fortunato S (2010) Community detection in graphs. Physics reports 486(3-5):75–174, doi: 10.1016/j.physrep.2009.11.002
2009
Earlier work this paper cites.
Khuller S, Saha B (2009) On finding dense subgraphs. In: Proceedings of 36th International Colloquium on Automata, Languages and Programming (ICALP 2009), pp 597–608
2009
Earlier work this paper cites.
McFee B, Lanckriet G (2009) Partial order embedding with multiple kernels. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp 721–728, doi: 10.1145/1553374.1553467
2009
Earlier work this paper cites.
Settles B (2009) Active learning literature survey. Computer Sciences Technical Report 1648 , University of Wisconsin–Madison
2009
Earlier work this paper cites.
Tang W, Lu Z, Dhillon IS (2009) Clustering with multiple graphs. In: Ninth IEEE ICDM, pp 1016–1021, doi: 10.1109/ICDM.2009.125
2009
Earlier work this paper cites.
Diestel R (2010) Graph Theory ( 4 t h 4^{th} edn.). Springer, New York
2010
Earlier work this paper cites.
Leskovec J, Lang KJ, Mahoney M (2010) Empirical comparison of algorithms for network community detection. In: Proceedings of the 19th International Conference on World Wide Web (WWW ’10), pp 631–640, doi: 10.1145/1772690.1772755
2010
Earlier work this paper cites.
Sozio M, Gionis A (2010) The community-search problem and how to plan a successful cocktail party. In: Proceedings of the 16th ACM SIGKDD, pp 939–948, doi: 10.1145/1835804.1835923
2010
Earlier work this paper cites.
2011
Earlier work this paper cites.
Cao N, Yang Z, Wang C, Ren K, Lou W (2011) Privacy-preserving query over encrypted graph-structured data in cloud computing. In: 31st International Conference on Distributed Computing Systems, pp 393–402, doi: 10.1109/ICDCS.2011.84
2011
Earlier work this paper cites.
Latouche P, Birmelé E, Ambroise C (2011) Overlapping stochastic block models with application to the french political blogosphere. The Annals of Applied Statistics 5(1):309–336
2011
Earlier work this paper cites.
Ndiaye SN, Solnon C (2011) CP models for maximum common subgraph problems. In: International Conference on Principles and Practice of Constraint Programming, pp 637–644, doi: 10.1007/978-3-642-23786-7_48
2011
Earlier work this paper cites.
Park Y, Reeves D (2011) Deriving common malware behavior through graph clustering. In: Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security (ASIACCS ’11), pp 497–502, doi: 10.1145/1966913.1966986
2011
Earlier work this paper cites.
Pavlopoulos GA, Secrier M, Moschopoulos CN, Soldatos TG, Kossida S, Aerts J, Schneider R, Bagos PG (2011) Using graph theory to analyze biological networks. BioData Mining 4(1), doi: 10.1186/1756-0381-4-10
2011
Cited alongside, same era.
Wang F, Li T, Wang X, Zhu S, Ding C (2011) Community discovery using nonnegative matrix factorization. Data Mining and Knowledge Discovery 22:493–521, doi: 10.1007/s10618-010-0181-y
2011
Cited alongside, same era.
Bahiense L, Manić G, Piva B, de Souza CC (2012) The maximum common edge subgraph problem: A polyhedral investigation. Discrete Applied Mathematics 160(18):2523–2541, doi: 10.1016/j.dam.2012.01.026
2012
Cited alongside, same era.
Lee J, Han W, Kasperovics R, Lee J (2012) An in-depth comparison of subgraph isomorphism algorithms in graph databases. Proc VLDB Endow 6(2):133–144, doi: 10.14778/2535568.2448946
2012
Cited alongside, same era.
Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2018) Graph attention networks. In: Proceedings of the 6th ICLR
2018
Later among the works it cites.
2018
Later among the works it cites.
Bai Y, Xu D, Gu K, Wu X, Marinovic A, Ro C, Sun Y, Wang W (2019) Neural maximum common subgraph detection with guided subgraph extraction
2019
Later among the works it cites.
Lemos H, Prates M, Avelar P, Lamb L (2019) Graph colouring meets deep learning: Effective graph neural network models for combinatorial problems. In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pp 879–885, doi: 10.1109/ICTAI.2019.00125
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Pinheiro CAR (2012) Community detection to identify fraud events in telecommunications networks. In: SAS SUGI Proceedings: Customer Intelligence
2012
Cited alongside, same era.
2012
Cited alongside, same era.
Giugno R, Bonnici V, Bombieri N, Pulvirenti A, Ferro A, Shasha D (2013) GRAPES: A software for parallel searching on biological graphs targeting multi-core architectures. PLoS ONE 8(10):e76,911, doi: 10.1371/journal.pone.0076911
2013
Cited alongside, same era.
Papalexakis EE, Akoglu L, Ience D (2013) Do more views of a graph help? community detection and clustering in multi-graphs. In: Proceedings of the 16th International Conference on Information Fusion, pp 899–905
2013
Cited alongside, same era.
Xie J, Kelley S, Szymanski BK (2013) Overlapping community detection in networks: The state-of-the-art and comparative study. ACM Computing Surveys 45(4):1–35, doi: 10.1145/2501654.2501657
2013
Cited alongside, same era.
Yang J, Leskovec J (2013) Overlapping community detection at scale: A nonnegative matrix factorization approach. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp 587–596, doi: 10.1145/2433396.2433471
2013
Cited alongside, same era.
Galbrun E, Gionis A, Tatti N (2014) Overlapping community detection in labeled graphs. Data Mining and Knowledge Discovery 28(5):1586–1610, doi: 10.1007/s10618-014-0373-y
2014
Cited alongside, same era.
Huang X, Cheng H, Qin L, Tian W, Yu JX (2014) Querying k-truss community in large and dynamic graphs. In: Proceedings of the 2014 ACM SIGMOD, pp 1311–1322, doi: 10.1145/2588555.2610495
2014
Cited alongside, same era.
Morris C, Ritzert M, Fey M, Hamilton WL, Lenssen JE, Rattan G, Grohe M (2019) Weisfeiler and Leman go neural: Higher-order graph neural networks. Proceedings of the AAAI Conference on Artificial Intelligence 33(01):4602–4609, doi: 10.1609/aaai.v33i01.33014602
2019
Later among the works it cites.
Prates M, Avelar PHC, Lemos H, Lamb LC, Vardi MY (2019) Learning to solve NP-complete problems: A graph neural network for decision TSP. Proceedings of the AAAI Conference on Artificial Intelligence 33(1):4731–4738, doi: 10.1609/aaai.v33i01.33014731
2019
Later among the works it cites.
Sato R, Yamada M, Kashima H (2019) Approximation ratios of graph neural networks for combinatorial problems. In: Advances in Neural Information Processing Systems, vol 32
2019
Later among the works it cites.
Seshadhri C, Tirthapura S (2019) Scalable subgraph counting: The methods behind the madness. In: Companion Proceedings of the 2019 World Wide Web Conference (WWW ’19), pp 1317–1318, doi: 10.1145/3308560.3320092
2019
Later among the works it cites.
Shchur O, Günnemann S (2019) Overlapping community detection with graph neural networks. In: Proceedings of the First International Workshop on Deep Learning for Graphs (DLG ’19), pp 1–7
2019
Later among the works it cites.
Sun Y, Li X, Ernst A (2021) Using statistical measures and machine learning for graph reduction to solve maximum weight clique problems. IEEE Transactions on Pattern Analysis and Machine Intelligence 43(5):1746–1760, doi: 10.1109/TPAMI.2019.2954827
2019
Later among the works it cites.
Wang Y, Pan S, Li C, Yin M (2020) A local search algorithm with reinforcement learning based repair procedure for minimum weight independent dominating set. Information Sciences 512:533–548, doi: 10.1016/j.ins.2019.09.059
2019
Later among the works it cites.
Xu K, Hu W, Leskovec J, Jegelka S (2019) How powerful are graph neural networks? In: Proceedings of the 7th ICLR
2019
Later among the works it cites.
Alpaydin E (2020) Introduction to machine learning ( 4 t h 4^{th} edition). MIT Press
2020
Later among the works it cites.
Arvind V, Fuhlbrück F, Köbler J, Verbitsky O (2020) On Weisfeiler-Leman invariance: Subgraph counts and related graph properties. Journal of Computer and System Sciences 113:42–59, doi: 10.1016/j.jcss.2020.04.003
2020
Later among the works it cites.
Bai Y, Xu D, Sun Y, Wang W (2020) GLSearch: Maximum common subgraph detection via learning to search. In: Proceedings of the 38th International Conference on Machine Learning, pp 588–598
2020
Later among the works it cites.
Bengio Y, Lodi A, Prouvost A (2021) Machine learning for combinatorial optimization: A methodological tour d’horizon. European Journal of Operational Research 290(2):405–421, doi: 10.1016/j.ejor.2020.07.063
2020
Later among the works it cites.
Chen Z, Chen L, Villar S, Bruna J (2020) Can graph neural networks count substructures? In: Advances in Neural Information Processing Systems, vol 33
2020
Later among the works it cites.
Kazemi SM, Goel R, Jain K, Kobyzev I, Sethi A, Forsyth P, Poupart P (2020) Representation learning for dynamic graphs: A survey. Journal of Machine Learning Research 21(70):1–73
2020
Later among the works it cites.
Park Y, Ko S, Bhowmick SS, Kim K, Hong K, Han WS (2020) G-CARE: A framework for performance benchmarking of cardinality estimation techniques for subgraph matching. In: Proceedings of the 2020 ACM SIGMOD, pp 1099–1114, doi: 10.1145/3318464.3389702
2020
Later among the works it cites.
Sun S, Luo Q (2020) In-memory subgraph matching: An in-depth study. In: Proceedings of the 2020 ACM SIGMOD, pp 1083–1098, doi: 10.1145/3318464.3380581
2020
Later among the works it cites.
Todeschini A, Miscouridou X, Caron F (2020) Exchangeable random measures for sparse and modular graphs with overlapping communities. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82(2):487–520
2020
Later among the works it cites.
Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2021) A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems 32(1):4–24, doi: 10.1109/TNNLS.2020.2978386
2020
Later among the works it cites.
Zhang Z, Cui P, Zhu W (2022) Deep learning on graphs: A survey. IEEE Transactions on Knowledge and Data Engineering 34(1):249–270, doi: 10.1109/TKDE.2020.2981333
2020
Later among the works it cites.
2021
Later among the works it cites.
Chaudhary L, Singh B (2021) Community detection using unsupervised machine learning techniques on COVID-19 dataset. Soc Netw Anal Min 11:28, doi: 10.1007/s13278-021-00734-2
2021
Later among the works it cites.
2021
Later among the works it cites.
Gao J, Chen J, Li Z, Zhang J (2021) ICS-GNN: Lightweight interactive community search via graph neural network. Proc VLDB Endow 14(6):1006–1018, doi: 10.14778/3447689.3447704
2021
Later among the works it cites.
Ge Y, Bertozzi AL (2021) Active learning for the subgraph matching problem. In: 2021 IEEE International Conference on Big Data, pp 2641–2649, doi: 10.1109/BigData52589.2021.9671760
2021
Later among the works it cites.
Karimi-Mamaghan M, Mohammadi M, Meyer P, Karimi-Mamaghan AM, Talbi EG (2022) Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art. European Journal of Operational Research 296(2):393–422, doi: 10.1016/j.ejor.2021.04.032
2021
Later among the works it cites.
Ma C, Fang Y, Cheng R, Lakshmanan LVS, Zhang W, Lin X (2021) On directed densest subgraph discovery. ACM Transactions on Database Systems 46(4):1–45, doi: 10.1145/3483940
2021
Later among the works it cites.
Mazyavkina N, Sviridov S, Ivanov S, Burnaev E (2021) Reinforcement learning for combinatorial optimization: A survey. Computers & Operations Research 134:105,400, doi: 10.1016/j.cor.2021.105400
2021
Later among the works it cites.
Moorman JD, Tu TK, Chen Q, He X, Bertozzi AL (2021) Subgraph matching on multiplex networks. IEEE Transactions on Network Science and Engineering 8(2):1367–1384, doi: 10.1109/TNSE.2021.3056329
2021
Later among the works it cites.
Peng Y, Choi B, Xu J (2021) Graph learning for combinatorial optimization: A survey of state-of-the-art. Data Science and Engineering 6:119–141, doi: 10.1007/s41019-021-00155-3
2021
Later among the works it cites.
Pugliese R, Regondi S, Marini R (2021) Machine learning-based approach: Global trends, research directions, and regulatory standpoints. Data Science and Management 4:19–29, doi: 10.1016/j.dsm.2021.12.002
2021
Later among the works it cites.
Ribeiro P, Paredes P, Silva MEP, Aparicio D, Silva F (2021) A survey on subgraph counting: Concepts, algorithms, and applications to network motifs and graphlets. ACM Computing Surveys 54(2):1–36, doi: 10.1145/3433652
2021
Later among the works it cites.
Su X, Xue S, Liu F, et al (2022) A comprehensive survey on community detection with deep learning. IEEE Transactions on Neural Networks and Learning Systems pp 1–21, doi: 10.1109/TNNLS.2021.3137396
2021
Later among the works it cites.
Sun J, Zheng W, Zhang Q, Xu Z (2022) Graph neural network encoding for community detection in attribute networks. IEEE Transactions on Cybernetics 52(8):7791–7804, doi: 10.1109/TCYB.2021.3051021
2021
Later among the works it cites.
Yow KS, Morgan KJ, Farr GE (2021) Factorisation of greedoid polynomials of rooted digraphs. Graphs and Combinatorics 37(6):2245–2264, doi: 10.1007/s00373-021-02347-0
2021
Later among the works it cites.
Zhao K, Yu JX, Zhang H, Li Q, Rong Y (2021) A learned sketch for subgraph counting. In: Proceedings of the 2021 ACM SIGMOD, pp 2142–2155, doi: 10.1145/3448016.3457289
2021
Later among the works it cites.
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, doi: 10.1016/j.aiopen.2021.01.001
2021
Later among the works it cites.
2022
Closest in time.
Jiang Y, Rong Y, Cheng H, Huang X, Zhao K, Huang J (2022) Query driven-graph neural networks for community search: From non-attributed, attributed, to interactive attributed. Proc VLDB Endow 15(6):1243–1255, doi: 10.14778/3514061.3514070
2022
Closest in time.
Kim J, Luo S, Cong G, Yu W (2022) DMCS: Density modularity based community search. In: Proceedings of the 2022 ACM SIGMOD, pp 889–903, doi: 10.1145/3514221.3526137
2022
Closest in time.
2022
Closest in time.
Lan Z, Ma Y, Yu L, Yuan L, Ma F (2023) AEDNet: Adaptive edge-deleting network for subgraph matching. Pattern Recognition 133:109,033, doi: 10.1016/j.patcog.2022.109033
2022
Closest in time.
Liu X, Song Y (2022) Graph convolutional networks with dual message passing for subgraph isomorphism counting and matching. Proceedings of the AAAI Conference on Artificial Intelligence 36(7):7594–7602, doi: 10.1609/aaai.v36i7.20725
2022
Closest in time.
Mandal D, Medya S, Uzzi B, Aggarwal C (2022) Metalearning with graph neural networks: Methods and applications. ACM SIGKDD Explorations Newsletter 23(2):13–22, doi: 10.1145/3510374.3510379
2022
Closest in time.
Roy I, Velugoti VSBR, Chakrabarti S, De A (2022) Interpretable neural subgraph matching for graph retrieval. Proceedings of the AAAI Conference on Artificial Intelligence 36(7):8115–8123, doi: 10.1609/aaai.v36i7.20784
2022
Closest in time.
Wang H, Hu R, Zhang Y, Qin L, Wang W, Zhang W (2022a) Neural subgraph counting with Wasserstein estimator. In: Proceedings of the 2022 ACM SIGMOD, pp 160–175, doi: 10.1145/3514221.3526163
2022
Closest in time.
2022
Closest in time.
Li L, Luo S, Zhao Y, Shan C, Wang Z, Qin L (2023) COCLEP: Contrastive learning-based semi-supervised community search. In: IEEE 39th ICDE
2023
Closest in time.
Luo S, Zhu Z, Xiao X, Yang Y, Li C, Kao B (2023) Multi-task processing in vertex-centric graph systems: Evaluations and insights. In: Proceedings of the 26th International Conference on Extending Database Technology, pp 247–259, doi: 10.48786/edbt.2023.20
2023
Closest in time.