Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In Proceedings of the 2019 International Conference on Machine Learning (ICML), volume 97, pp. 21–29. PMLR, 2019.
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Çaglar Gülçehre, H. Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey R. Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu. Relational inductive biases, deep learning, and graph networks. CoRR, abs/1806.01261, 2018.
Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. In Proceedings of the 2001 Annual Conference on Neural Information Processing Systems (NIPS), pp. 585–591, 2001.
Piotr Bielak, Tomasz Kajdanowicz, and Nitesh V. Chawla. Graph barlow twins: A self-supervised representation learning framework for graphs. CoRR, abs/2106.02466, 2021.
Deyu Bo, Xiao Wang, Chuan Shi, Meiqi Zhu, Emiao Lu, and Peng Cui. Structural deep clustering network. In Proceedings of the 2020 International Conference on World Wide Web (WWW), pp. 1400–1410. ACM, 2020.
Deyu Bo, Xiao Wang, Chuan Shi, and Huawei Shen. Beyond low-frequency information in graph convolutional networks. In Proceedings of the 2021 AAAI Conference on Artificial Intelligence (AAAI), pp. 3950–3957, 2021.
Deli Chen, Yankai Lin, Wei Li, Peng Li, Jie Zhou, and Xu Sun. Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In Proceedings of the 2020 AAAI Conference on Artificial Intelligence (AAAI), pp. 3438–3445, 2020.
Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. Adaptive universal generalized pagerank graph neural network. In Proceedings of the 2021 International Conference on Learning Representations (ICLR), 2021.
Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu. A survey on network embedding. IEEE Transactions on Knowledge and Data Engineering (TKDE), 31(5):833–852, 2019.
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. In Proceedings of the 2016 Annual Conference on Neural Information Processing Systems (NIPS), pp. 3837–3845, 2016.
Xinyu Fu, Jiani Zhang, Ziqiao Meng, and Irwin King. MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding. In Proceedings of the 2020 International Conference on World Wide Web (WWW), pp. 2331–2341. ACM, 2020.
Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of the 2016 ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 855–864. ACM, 2016.
William L. Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Proceedings of the 2017 Annual Conference on Neural Information Processing Systems (NIPS), pp. 1025–1035, 2017.
Jun Han and Claudio Moraga. The influence of the sigmoid function parameters on the speed of backpropagation learning. In Proceedings of the 1995 International Workshop on Artificial Neural Networks (IWANN), pp. 195–201. Springer, 1995.
John A Hartigan and Manchek A Wong. Algorithm as 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1):100–108, 1979.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Delving deep into rectifiers: Surpassing humanlevel performance on imagenet classification. In Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1026–1034, 2015.
Geoffrey E Hinton and Ruslan R Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313:504–507, 2006.
Dongkwan Kim and Alice Oh. How to find your friendly neighborhood: Graph attention design with selfsupervision. In Proceedings of the 2021 International Conference on Learning Representations (ICLR), 2021.
Thomas N. Kipf and Max Welling. Variational graph auto-encoders. CoRR, abs/1611.07308, 2016.
Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In Proceedings of the 2017 International Conference on Learning Representations (ICLR), 2017.
Ron Levie, Federico Monti, Xavier Bresson, and Michael M. Bronstein. Cayleynets: Graph convolutional neural networks with complex rational spectral filters. IEEE Signal Processing Magazine, 67(1):97–109, 2017.
Michelle M. Li, Kexin Huang, and Marinka Zitnik. healthcare. Nature Biomedical Engineering, 2022. Graph representation learning in biomedicine and
Pan Li, Yanbang Wang, Hongwei Wang, and Jure Leskovec. 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), pp. 4465–4478, 2020.
Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, and Ser-Nam Lim. Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods. In Proceedings of the 2021 Annual Conference on Neural Information Processing Systems (NeurIPS), pp. 20887–20902, 2021.
Yao Ma, Xiaorui Liu, Neil Shah, and Jiliang Tang. Is homophily a necessity for graph neural networks? In Proceedings of the 2022 International Conference on Learning Representations (ICLR), 2022.
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, and Ian J. Goodfellow. Lstm-based encoder-decoder for multi-sensor anomaly detection. CoRR, abs/1511.05644, 2015.
Jonathan Masci, Ueli Meier, Dan C. Ciresan, and Jürgen Schmidhuber. Stacked convolutional auto-encoders for hierarchical feature extraction. In Proceedings of the 2011 International Conference on Artificial Neural Networks (ICANN), pp. 52–59. Springer, 2011.
Julian J. McAuley and Jure Leskovec. Learning to discover social circles in ego networks. In Proceedings of the 2012 Annual Conference on Neural Information Processing Systems (NIPS), pp. 548–556, 2012.
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeffrey Dean. Distributed representations of words and phrases and their compositionally. In Proceedings of the 2013 Annual Conference on Neural Information Processing Systems (NIPS), pp. 3111–3119, 2013.
Vinod Nair and Geoffrey E. Hinton. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 2010 International Conference on Machine Learning (ICML), pp. 807–814, 2010.
Andrew Y. Ng, Michael I. Jordan, and Yair Weiss. On spectral clustering: Analysis and an algorithm. In Proceedings of the 2001 Annual Conference on Neural Information Processing Systems (NIPS), pp. 849–856, 2001.
Shashank Pandit, Duen Horng Chau, Samuel Wang, and Christos Faloutsos. Netprobe: a fast and scalable system for fraud detection in online auction networks. In Proceedings of the 2007 International Conference on World Wide Web (WWW), pp. 201–210. ACM, 2007.
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. Geom-gcn: Geometric graph convolutional networks. In Proceedings of the 2020 International Conference on Learning Representations (ICLR), 2020.
Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, and Junzhou Huang. Graph representation learning via graphical mutual information maximization. In Proceedings of the 2020 International Conference on World Wide Web (WWW), pp. 259–270. ACM, 2020.
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social representations. In Proceedings of the 2014 ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 701–710. ACM, 2014.
Samira Pouyanfar, Saad Sadiq, Yilin Yan, Haiman Tian, Yudong Tao, Maria E. Presa Reyes, Mei-Ling Shyu, Shu-Ching Chen, and S. S. Iyengar. A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys, 51(5):92:1–92:36, 2019.
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. In Proceedings of the 2018 ACM International Conference on Web Search and Data Mining (WSDM), pp. 459–467. ACM, 2018.
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. GCC: graph contrastive coding for graph neural network pre-training. In Proceedings of the 2020 ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 1150–1160, 2020.
Leonardo Filipe Rodrigues Ribeiro, Pedro H. P. Saverese, and Daniel R. Figueiredo. struc2vec: Learning node representations from structural identity. In Proceedings of the 2017 ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 385–394, 2017.
Benedek Rozemberczki and Rik Sarkar. Characteristic functions on graphs: Birds of a feather, from statistical descriptors to parametric models. In Proceedings of the 2020 ACM International Conference on Information and Knowledge Management (CIKM), pp. 1325–1334, 2020.
Benedek Rozemberczki, Carl Allen, and Rik Sarkar. Multi-scale attributed node embedding. Journal of Complex Networks, 9(2), 2021.
Satu Elisa Schaeffer. Graph clustering. Computer science review, 1(1):27–64, 2007.
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. Line: Large-scale information network embedding. In Proceedings of the 2015 International Conference on World Wide Web (WWW), pp. 1067–1077. ACM, 2015.
Jie Tang, Jimeng Sun, Chi Wang, and Zi Yang. Social influence analysis in large-scale networks. In Proceedings of the 2009 ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 807–816, 2009.
Mingyue Tang, Pan Li, and Carl Yang. Graph auto-encoder via neighborhood wasserstein reconstruction. In Proceedings of the 2022 International Conference on Learning Representations (ICLR), 2022.
Shang-Hua Teng. Scalable algorithms for data and network analysis. Foundations and Trends in Theoretical Computer Science, 12(1-2):1–274, 2016.
Naftali Tishby and Noga Zaslavsky. Deep learning and the information bottleneck principle. In 2015 IEEE Information Theory Workshop (ITW), pp. 1–5. IEEE, 2015.
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. In Proceedings of the 2018 International Conference on Learning Representations (ICLR), 2018.
Petar Velickovic, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R. Devon Hjelm. Deep graph infomax. In Proceedings of the 2019 International Conference on Learning Representations (ICLR), 2019.
Rongzhe Wei, Haoteng Yin, Junteng Jia, Austin R. Benson, and Pan Li. Understanding non-linearity in graph neural networks from the bayesian-inference perspective. In Proceedings of the 2022 Annual Conference on Neural Information Processing Systems (NeurIPS), 2022.
Tailin Wu, Hongyu Ren, Pan Li, and Jure Leskovec. Graph information bottleneck. In Proceedings of the 2020 Annual Conference on Neural Information Processing Systems (NeurIPS), pp. 20437–20448, 2020.
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1): 4–24, 2021.
Feng Xia, Ke Sun, Shuo Yu, Abdul Aziz, Liangtian Wan, Shirui Pan, and Huan Liu. Graph learning: A survey. IEEE Transactions on Artificial Intelligence (TAI), 2(2):109–127, 2021.
Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, and Xueqi Cheng. Graph wavelet neural network. In Proceedings of the 2019 International Conference on Learning Representations (ICLR), 2019a.
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? In Proceedings of the 2019 International Conference on Learning Representations (ICLR), 2019b.
Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos, and Mingyi Hong. Towards k-means-friendly spaces: Simultaneous deep learning and clustering. In Proceedings of the 2017 International Conference on Machine Learning (ICML), volume 70, pp. 3861–3870. PMLR, 2017.
Zhen Yang, Ming Ding, Chang Zhou, Hongxia Yang, Jingren Zhou, and Jie Tang. Understanding negative sampling in graph representation learning. In Proceedings of the 2020 ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 1666–1676. ACM, 2020.
Jiaxuan You, Jonathan Michael Gomes Selman, Rex Ying, and Jure Leskovec. Identity-aware graph neural networks. In Proceedings of the 2021 AAAI Conference on Artificial Intelligence (AAAI), pp. 10737–10745, 2021.
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. Graph contrastive learning with augmentations. In Proceedings of the 2020 Annual Conference on Neural Information Processing Systems (NeurIPS), pp. 5812–5823, 2020.
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny. Barlow twins: Self-supervised learning via redundancy reduction. In Proceedings of the 2021 International Conference on Machine Learning (ICML), volume 139, pp. 12310–12320. PMLR, 2021.
Ziwei Zhang, Peng Cui, and Wenwu Zhu. Deep learning on graphs: A survey. IEEE Transactions on Knowledge and Data Engineering (TKDE), 34(1):249–270, 2020.
Xin Zheng, Yixin Liu, Shirui Pan, Miao Zhang, Di Jin, and Yu Philip S. Graph neural networks for graphs with heterophily: A survey. CoRR, abs/2202.07082, 2022.
Zhiqiang Zhong, Sergei Ivanov, and Jun Pang. Simplifying node classification on heterophilous graphs with compatible label propagation. Transactions on Machine Learning Research, 2022a.
Zhiqiang Zhong, Cheng-Te Li, and Jun Pang. Hierarchical message-passing graph neural networks. Data Mining and Knowledge Discovery, pp. 1–28, 2022b.
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. Beyond homophily in graph neural networks: Current limitations and effective designs. In Proceedings of the 2020 Annual Conference on Neural Information Processing Systems (NeurIPS), pp. 7793–7804, 2020.
Jiong Zhu, Ryan A. Rossi, Anup B. Rao, Tung Mai, Nedim Lipka, Nesreen K. Ahmed, and Danai Koutra. Graph neural networks with heterophily. In Proceedings of the 2021 AAAI Conference on Artificial Intelligence (AAAI), pp. 11168–11176, 2021.
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann. Adversarial attacks on neural networks for graph data. In Proceedings of the 2018 ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 2847–2856, 2018.
