K-plex cover pooling for graph neural networks
Tóm tắt
Từ khóa
Tài liệu tham khảo
Bacciu D, Di Sotto L (2019) A non-negative factorization approach to node pooling in graph convolutional neural networks. In: Alviano M, Greco G, Scarcello F (eds) AI*IA 2019-advances in artificial intelligence, Lecture notes in computer science. Springer, Cham, pp 294–306, https://doi.org/10.1007/978-3-030-35166-3_21
Bacciu D, Errica F, Micheli A (2018) Contextual graph markov model: a deep and generative approach to graph processing. In: International Conference on Machine Learning, pp 294–303, ISSN: 1938-7228
Bacciu D, Errica F, Micheli A, Podda M (2020) A gentle introduction to deep learning for graphs. Neural Netw 129:203–221. https://doi.org/10.1016/j.neunet.2020.06.006
Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, Tacchetti A, Raposo D, Santoro A, Faulkner R, Gulcehre C, Song F, Ballard A, Gilmer J, Dahl G, Vaswani A, Allen K, Nash C, Langston V, Dyer C, Heess N, Wierstra D, Kohli P, Botvinick M, Vinyals O, Li Y, Pascanu R (2018) Relational inductive biases, deep learning, and graph networks. arXiv:1806.01261
Bianchi FM, Grattarola D, Alippi C (2020) Spectral clustering with graph neural networks for graph pooling. Proc Int Conf Mach Learn 1
Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp. https://doi.org/10.1088/1742-5468/2008/10/P10008
Borgwardt KM, Ong CS, Schönauer S, Vishwanathan SVN, Smola AJ, Kriegel HP (2005) Protein function prediction via graph kernels. Bioinform (Oxford, Engl) 21(Suppl 1):i47–56. https://doi.org/10.1093/bioinformatics/bti1007
Bron C, Kerbosch J (1973) Algorithm 457: finding all cliques of an undirected graph. Commun ACM 16(9):575–577. https://doi.org/10.1145/362342.362367
Bruna J, Zaremba W, Szlam A, LeCun Y (2014) Spectral networks and locally connected networks on graphs. In: Bengio Y, LeCun Y (eds) Proceedings of the 2nd international conference on learning representations, ICLR 2014, Banff, AB, Canada, April 14–16, 2014, Conference Track Proceedings
Cangea C, Veličković P, Jovanović N, Kipf T, Liò P (2018) Towards sparse hierarchical graph classifiers. arXiv:1811.01287
Cazals F, Karande C (2008) A note on the problem of reporting maximal cliques. Theor Comput Sci 407(1):564–568. https://doi.org/10.1016/j.tcs.2008.05.010
Conte A, Grossi R, Marino A (2016) Clique covering of large real-world networks. In: Proceedings of the 31st annual ACM symposium on applied computing, association for computing machinery, Pisa, Italy, SAC ’16, pp 1134–1139, https://doi.org/10.1145/2851613.2851816
Conte A, De Matteis T, De Sensi D, Grossi R, Marino A, Versari L (2018) D2K: scalable community detection in massive networks via small-diameter k-plexes. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, association for computing machinery, London, United Kingdom, KDD ’18, pp 1272–1281, https://doi.org/10.1145/3219819.3220093
Conte A, Grossi R, Marino A (2020) Large-scale clique cover of real-world networks. Inform Comput 270: https://doi.org/10.1016/j.ic.2019.104464
Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. In: Proceedings of the 30th international conference on neural information processing systems, Curran Associates Inc., Red Hook, NY, USA, NIPS’16, pp 3844–3852
Dhillon IS, Guan Y, Kulis B (2007) Weighted graph cuts without eigenvectors a multilevel approach. IEEE Trans Pattern Anal Mach Intel 29(11):1944–1957. https://doi.org/10.1109/TPAMI.2007.1115
Diehl F (2019) Edge contraction pooling for graph neural networks. arXiv:1905.10990
Diehl F, Brunner T, Le MT, Knoll A (2019) Towards graph pooling by edge contraction. In: ICML 2019 workshop on learning and reasoning with graph-structured data
Dobson PD, Doig AJ (2003) Distinguishing enzyme structures from non-enzymes without alignments. J Mol Biol 330(4):771–783. https://doi.org/10.1016/s0022-2836(03)00628-4
Errica F, Podda M, Bacciu D, Micheli A (2020) A fair comparison of graph neural networks for graph classification. In: International conference on learning representations
Fey M, Lenssen JE (2019) Fast graph representation learning with PyTorch geometric. In: ICLR workshop on representation learning on graphs and manifolds
Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36(4):193–202. https://doi.org/10.1007/BF00344251
Gama F, Marques AG, Leus G, Ribeiro A (2019) Convolutional neural network architectures for signals supported on graphs. IEEE Trans Signal Process 67(4):1034–1049. https://doi.org/10.1109/TSP.2018.2887403
Gao H, Ji S (2019) Graph U-Nets. In: International Conference on Machine Learning, pp 2083–2092, ISSN: 1938-7228 Section: Machine Learning
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, vol 70, JMLR.org, Sydney, NSW, Australia, ICML’17, pp 1263–1272
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge
Gori M, Monfardini G, Scarselli F (2005) A new model for learning in graph domains. In: Proceedings of the 2005 IEEE international joint conference on neural networks, vol 2, pp 729–734. https://doi.org/10.1109/IJCNN.2005.1555942, ISSN: 2161-4407
Hagberg AA, Schult DA, Swart PJ (2008) Exploring network structure, dynamics, and function using networkx. In: Varoquaux G, Vaught T, Millman J (eds) Proceedings of the 7th Python in Science Conference, Pasadena, CA USA, pp 11–15
Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. In: Proceedings of the 31st international conference on neural information processing systems, Curran Associates Inc., Long Beach, California, USA, NIPS’17, pp 1025–1035
Hammond DK, Vandergheynst P, Gribonval R (2011) Wavelets on graphs via spectral graph theory. Appl Comput Harmonic Anal 30(2):129–150. https://doi.org/10.1016/j.acha.2010.04.005
Karypis G, Kumar V (1998) A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J Sci Comput 20(1):359–392. https://doi.org/10.1137/S1064827595287997
Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th international conference on learning representations, ICLR 2017, Toulon, France, April 24–26, 2017, Conference Track Proceedings, OpenReview.net
Knyazev B, Taylor GW, Amer M (2019) Understanding attention and generalization in graph neural networks. In: Wallach H, Larochelle H, Beygelzimer A, Alché-Buc F, Fox E, Garnett R (eds) Advances in neural information processing systems 32, Curran Associates, Inc., pp 4202–4212
Kriege NM, Johansson FD, Morris C (2020) A survey on graph kernels. Appl Netw Sci 5(1):6. https://doi.org/10.1007/s41109-019-0195-3
Le Gall F (2014) Powers of tensors and fast matrix multiplication. In: Proceedings of the 39th international symposium on symbolic and algebraic computation, pp 296–303
LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551. https://doi.org/10.1162/neco.1989.1.4.541
Lee J, Lee I, Kang J (2019) Self-attention graph pooling. In: International conference on machine learning, pp 3734–3743, ISSN: 1938-7228 Section: Machine Learning
Li M, Ma Z, Wang YG, Zhuang X (2020) Fast Haar transforms for graph neural networks. Neural Netw 128:188–198. https://doi.org/10.1016/j.neunet.2020.04.028
Li Q, Han Z, Wu XM (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: McIlraith SA, Weinberger KQ (eds) Proceedings of the thirty-second AAAI conference on artificial intelligence, (AAAI-18), the 30th innovative applications of artificial intelligence (IAAI-18), and the 8th AAAI symposium on educational advances in artificial intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2–7, 2018, AAAI Press, pp 3538–3545
Li Y, Tarlow D, Brockschmidt M, Zemel RS (2016) Gated graph sequence neural networks. In: Proceedings of the 4th international conference on learning representations, ICLR 2016, San Juan, Puerto Rico, May 2–4, 2016, Conference Track Proceedings
Luzhnica E, Day B, Lio’ P (2019) Clique pooling for graph classification. arXiv:1904.00374
Ma Y, Wang S, Aggarwal CC, Tang J (2019) Graph convolutional networks with EigenPooling. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, Association for Computing Machinery, Anchorage, AK, USA, KDD ’19, pp 723–731, https://doi.org/10.1145/3292500.3330982
Micheli A (2009) Neural network for graphs: a contextual constructive approach. IEEE Trans Neural Netw 20(3):498–511. https://doi.org/10.1109/TNN.2008.2010350
Monti F, Boscaini D, Masci J, Rodola E, Svoboda J, Bronstein MM (2017) Geometric deep learning on graphs and manifolds using mixture model CNNs. pp 5115–5124
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. Proc AAAI Conf Artif Intel 33(01):4602–4609. https://doi.org/10.1609/aaai.v33i01.33014602
Morris C, Kriege NM, Bause F, Kersting K, Mutzel P, Neumann M (2020) TUDataset: a collection of benchmark datasets for learning with graphs. In: ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020), arXiv:2007.08663
Ng AY, Jordan MI, Weiss Y (2002) On spectral clustering: analysis and an algorithm. In: Dietterich TG, Becker S, Ghahramani Z (eds) Advances in neural information processing systems, vol 14. MIT Press, Cambridge, pp 849–856
Poulin V, Théberge F (2019) Ensemble clustering for graphs. In: Aiello LM, Cherifi C, Cherifi H, Lambiotte R, Lió P, Rocha LM (eds) Complex networks and their applications VII, Studies in Computational Intelligence. Springer, Cham, pp 231–243, https://doi.org/10.1007/978-3-030-05411-3_19
Ranjan E, Sanyal S, Talukdar P (2020) ASAP: adaptive structure aware pooling for learning hierarchical graph representations. Proc AAAI Conf Artif Intel 34(04):5470–5477. https://doi.org/10.1609/aaai.v34i04.5997
RAPIDS Development Team (2018) RAPIDS: collection of libraries for end to end GPU data science
Scarselli F, Yong SL, Gori M, Hagenbuchner M, Tsoi AC, Maggini M (2005) Graph neural networks for ranking Web pages. In: The 2005 IEEE/WIC/ACM international conference on web intelligence (WI’05), pp 666–672, https://doi.org/10.1109/WI.2005.67
Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans Neural Netw 20(1):61–80. https://doi.org/10.1109/TNN.2008.2005605
Schomburg I, Chang A, Ebeling C, Gremse M, Heldt C, Huhn G, Schomburg D (2004) BRENDA, the enzyme database: updates and major new developments. Nucl Acids Res. https://doi.org/10.1093/nar/gkh081
Shervashidze N, Schweitzer P, Leeuwen EJV, Mehlhorn K, Borgwardt KM (2011) Weisfeiler-Lehman graph kernels. J Mach Learn Res 12:2539–2561
Simonovsky M, Komodakis N (2017) Dynamic edge-conditioned filters in convolutional neural networks on graphs. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3693–3702
Tomita E, Tanaka A, Takahashi H (2006) The worst-case time complexity for generating all maximal cliques and computational experiments. Theor Comput Sci 363(1):28–42. https://doi.org/10.1016/j.tcs.2006.06.015
Traag VA, Waltman L, van Eck NJ (2019) From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep 9(1):5233. https://doi.org/10.1038/s41598-019-41695-z
Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2018) Graph attention networks. In: International conference on learning representations
Vinyals O, Bengio S, Kudlur M (2016) Order matters: sequence to sequence for sets. In: Proceedings of the 4th international conference on learning representations, ICLR 2016, San Juan, Puerto Rico, May 2–4, 2016, Conference Track Proceedings
Vishwanathan SVN, Schraudolph NN, Kondor R, Borgwardt KM (2010) Graph Kernels. J Mach Learn Res 11(40):1201–1242
Wale N, Watson IA, Karypis G (2008) Comparison of descriptor spaces for chemical compound retrieval and classification. Knowl Inform Syst 14(3):347–375. https://doi.org/10.1007/s10115-007-0103-5
Wang Y, Li M, Ma Z, Montufar G, Zhuang X, Fan Y (2020) Haar Graph Pooling. Proc Int Conf Mach Learn 1
Xu K, Li C, Tian Y, Sonobe T, Kawarabayashi K, Jegelka S (2018) Representation learning on graphs with jumping knowledge networks. In: International conference on machine learning, PMLR, pp 5453–5462, ISSN: 2640-3498
Xu K, Hu W, Leskovec J, Jegelka S (2019) How powerful are graph neural networks? In: International conference on learning representations
Yanardag P, Vishwanathan S (2015) Deep graph kernels. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, association for computing machinery, New York, NY, USA, KDD ’15, pp 1365–1374, https://doi.org/10.1145/2783258.2783417
Ying R, You J, Morris C, Ren X, Hamilton WL, Leskovec J (2018) Hierarchical graph representation learning with differentiable pooling. In: Proceedings of the 32nd international conference on neural information processing systems, Curran Associates Inc., Montréal, Canada, NIPS’18, pp 4805–4815
Yuan H, Ji S (2019) StructPool: structured graph pooling via conditional random fields
Zhang L, Wang X, Li H, Zhu G, Shen P, Li P, Lu X, Shah SAA, Bennamoun M (2020) Structure-feature based graph self-adaptive pooling. In: Proceedings of the web conference 2020, Association for Computing Machinery, New York, NY, USA, WWW ’20, pp 3098–3104, https://doi.org/10.1145/3366423.3380083
Zhang M, Cui Z, Neumann M, Chen Y (2018) An end-to-end deep learning architecture for graph classification. In: Proceedings of the thirty-second AAAI conference on artificial intelligence, (AAAI-18), the 30th innovative applications of artificial intelligence (IAAI-18), and the 8th AAAI symposium on educational advances in artificial intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2–7, 2018, pp 4438–4445