Alon U, Yahav E (2021) On the bottleneck of graph neural networks and its practical implications. In: Proceedings of the 2021 international conference on learning representations (ICLR)
Bhowmick AK, Meneni K, Danisch M, Guillaume J, Mitra B (2020) Louvainne: Hierarchical louvain method for high quality and scalable network embedding. In: Proceedings of the 2020 ACM international conference on web search and data mining (WSDM), pp 43–51
Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008(10):10008 DOI: 10.1088/1742-5468/2008/10/P10008
Borgwardt KM, Ong CS, Schönauer S, Vishwanathan SVN, Smola AJ, Kriegel H (2005) Protein function prediction via graph kernels. Bioinformatics 21(suppl–1):47–56 DOI: 10.1093/bioinformatics/bti1007
Chen Z, Li L, Bruna J (2019) Supervised community detection with line graph neural networks. In: Proceedings of the 2019 international conference on learning representations (ICLR)
Chen H, Perozzi B, Hu Y, Skiena S (2018) HARP: Hierarchical representation learning for networks. In: Proceedings of the 2018 AAAI conference on artificial intelligence (AAAI), pp 2127–2134
Chen M, Wei Z, Huang Z, Ding B, Li Y (2020) Simple and deep graph convolutional networks. In: Proceedings of the 2020 international conference on machine learning (ICML)
Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry, 35–41
Gao H, Ji S (2019) Graph u-nets. In: Proceedings of the 2019 international conference on machine learning (ICML)
Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: Proceedings of the 2017 international conference on machine learning (ICML)
Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99:7821–7826 DOI: 10.1073/pnas.122653799
Gu F, Chang H, Zhu W, Sojoudi S, El Ghaoui L (2020) Implicit graph neural networks. In: Proceedings of the 2020 annual conference on neural information processing systems (NeurIPS)
Hamilton WL, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Proceedings of the 2017 annual conference on neural information processing systems (NIPS), pp 1025–1035
Huang J, Li Z, Li N, Liu S, Li G (2019) Attpool: Towards hierarchical feature representation in graph convolutional networks via attention mechanism. In: Proceedings of the 2019 IEEE international conference on computer vision (ICCV), pp 6480–6489
Huang X, Song Q, Li Y, Hu X (2019) Graph recurrent networks with attributed random walks. In: Proceedings of the 2019 ACM conference on knowledge discovery and data mining (KDD), pp 732–740
Hu W, Fey M, Zitnik M, Dong Y, Ren H, Liu B, Catasta M, Leskovec J (2020) Open graph benchmark: Datasets for machine learning on graphs. In: Proceedings of the 2020 annual conference on neural information processing systems (NeurIPS)
Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of the 2017 international conference on learning representations (ICLR)
LeCun Y, Bengio Y, Hinton GE (2015) Deep learning. Nature 521(7553):436–444 DOI: 10.1038/nature14539
Leskovec J, Krevl A (2014) SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data
Li M, Chen S, Zhang Y, Tsang IW (2020) Graph cross networks with vertex infomax pooling. In: Proceedings of the 2020 annual conference on neural information processing systems (NeurIPS)
Li Q, Han Z, Wu X (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the 2018 AAAI conference on artificial intelligence (AAAI), pp. 3538–3545
Li Z, Kovachki NB, Azizzadenesheli K, Liu B, Stuart AM, Bhattacharya K, Anandkumar A (2020) Multipole graph neural operator for parametric partial differential equations. In: Proceedings of the 2020 annual conference on neural information processing systems (NeurIPS)
Li P, Wang Y, Wang H, Leskovec J (2020) Distance encoding - design provably more powerful graph neural networks for structural representation learning. In: Proceedings of the 2020 annual conference on neural information processing systems (NeurIPS)
Min Y, Wenkel F, Wolf G (2020) Scattering GCN: overcoming oversmoothness in graph convolutional networks. In: Proceedings of the 2020 annual conference on neural information processing systems (NeurIPS)
Nadakuditi RR, Newman MEJ (2012) Graph spectra and the detectability of community structure in networks. Phys Rev Lett 108(18):188701 DOI: 10.1103/PhysRevLett.108.188701
Namata G, London B, Getoor L, Huang B (2012) Query-driven active surveying for collective classification. In: Proceedings of the 2012 international workshop on mining and learning with graphs, p 8
Rampásek L, Wolf G (2021) Hierarchical graph neural nets can capture long-range interactions. In: IEEE international workshop on machine learning for signal processing (MLSP), pp 1–6
Ranjan E, Sanyal S, Talukdar PP (2020) ASAP: adaptive structure aware pooling for learning hierarchical graph representations. In: Proceedings of the 2020 AAAI conference on artificial intelligence (AAAI), pp. 5470–5477
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Proceedings of the 2015 medical image computing and computer-assisted intervention (MICCAI). Lecture notes in computer science, vol 9351, pp 234–241
Rossetti G, Guidotti R, Pennacchioli D, Pedreschi D, Giannotti F (2015) Interaction prediction in dynamic networks exploiting community discovery. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), pp 553–558
Sen P, Namata G, Bilgic M, Getoor L, Galligher B, Eliassi-Rad T (2008) Collective classification in network data. AI Mag 29(3):93–93
Sun Y, Han J (2012) Mining heterogeneous information networks: a structural analysis approach. ACM SIGKDD explorations newsletter
Traag VA (2015) Faster unfolding of communities: Speeding up the louvain algorithm. Phys Rev E 92(3):032801 DOI: 10.1103/PhysRevE.92.032801
Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2018) Graph attention networks. In: Proceedings of the 2018 international conference on learning representations (ICLR)
Wang X, Cui P, Wang J, Pei J, Zhu W, Yang S (2017) Community preserving network embedding. In: Proceedings of the 2017 AAAI conference on artificial intelligence (AAAI), pp 203–209
Wang J, Peng J, Liu O (2014) An approach for hesitant node classification in overlapping community detection. In: Processings of the 2014 Pacific Asia conference on information systems (PACIS), p 47
Watts DJ, Strogatz SH (1998) Collective dynamics of small-world networks. Nature 393:440 DOI: 10.1038/30918
Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2021) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4–24 DOI: 10.1109/TNNLS.2020.2978386
Xu K, Hu W, Leskovec J, Jegelka S (2019) How powerful are graph neural networks? In: Proceedings of the 2019 international conference on machine learning (ICML)
Ying R, You J, Morris C, Ren X, Hamilton WL, Leskovec J (2018) Hierarchical graph representation learning with differentiable pooling. In: Proceedings of the 2018 annual conference on neural information processing systems (NeurIPS), pp 4805–4815
You J, Ying R, Leskovec J (2019) Position-aware graph neural networks. In: Proceedings of the 2019 international conference on machine learning (ICML)
Zhang Z, Cui P, Zhu W (2020) Deep learning on graphs: a survey. IEEE Trans Knowl Data Eng. 10.1109/TKDE.2020.2981333 DOI: 10.1109/TKDE.2020.2981333
Zhang M, Chen Y (2018) Link prediction based on graph neural networks. In: Proceedings of the 2018 annual conference on neural information processing systems (NeurIPS), pp 5171–5181
Zitnik M, Leskovec J (2017) Predicting multicellular function through multi-layer tissue networks. Bioinformatics 33:190–198 DOI: 10.1093/bioinformatics/btx252
Zitnik M, Agrawal M, Leskovec J (2018) Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34(13):457–466 DOI: 10.1093/bioinformatics/bty294
