{"type":"ScholarlyArticle","title":"Spiking Graph Convolutional Networks","authors":[{"name":"Zulun Zhu","slug":"zulun-zhu","given":"Zulun","family":"Zhu","affiliation":"Rochester Institute of Technology"},{"name":"Jiaying Peng","slug":"jiaying-peng","given":"Jiaying","family":"Peng","affiliation":""},{"name":"Jintang Li","slug":"jintang-li","given":"Jintang","family":"Li","affiliation":""},{"name":"Liang Chen","slug":"liang-chen","given":"Liang","family":"Chen","affiliation":""},{"name":"Qi Yu","slug":"qi-yu","given":"Qi","family":"Yu","affiliation":"Rochester Institute of Technology"},{"name":"Siqiang Luo","slug":"siqiang-luo","given":"Siqiang","family":"Luo","affiliation":"Nanyang Technological University"}],"abstract":"Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemented on a deep network, require expensive computation power, making them difficult to be deployed on battery-powered devices. In contrast, Spiking Neural Networks (SNNs), which perform a bio-fidelity inference process, offer an energy-efficient neural architecture. In this work, we propose SpikingGCN, an end-to-end framework that aims to integrate the embedding of GCNs with the biofidelity characteristics of SNNs. The original graph data are encoded into spike trains based on the incorporation of graph convolution. We further model biological information processing\r\nby utilizing a fully connected layer combined with neuron nodes. In a wide range of scenarios (e.g., citation networks, image graph classification, and recommender systems), our experimental results show that the proposed method could gain competitive performance against state-of-the-art approaches. Furthermore, we show that SpikingGCN on a neuromorphic chip can bring a clear advantage of energy efficiency into graph data analysis, which demonstrates its great potential to construct environment-friendly machine learning models.","doi":"10.24963/ijcai.2022/338","url":"https://ijcai.org/proceedings/2022/338","pdf":"https://ijcai.org/proceedings/2022/0338.pdf","markdown":"https://ijcai.org/proceedings/2022/338.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/338","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Data Mining","track":"Main Track","number":338,"pages":{"first":2434,"last":2440},"keywords":["Machine Learning: Semi-Supervised Learning","Data Mining: Mining Graphs","Humans and AI: Brain Sciences"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987106","length_sec":90,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985249","length_sec":723,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Anthony et al., 2020] Lasse F. Wolff Anthony, Benjamin Kanding, and Raghavendra Selvan. Carbontracker: Tracking and predict- ing the carbon footprint of training deep learning models. CoRR, abs/2007.03051, 2020.","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models","authors":"F Wolff Lasse, Benjamin Anthony, Raghavendra Kanding, Selvan","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Brette et al., 2007] Romain Brette, Michelle Rudolph, Ted Carnevale, et al. Simulation of networks of spiking neu- rons: a review of tools and strategies. J. Comput. Neurosci., 23(3):349-398, 2007.","title":"Simulation of networks of spiking neurons: a review of tools and strategies","authors":"Romain Brette, Michelle Rudolph, Ted Carnevale","year":"2007","venue":"J. Comput. Neurosci","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Candès and Recht, 2012] Emmanuel J. Candès and Benjamin Recht. Exact matrix completion via convex optimization. Com- mun. ACM, 55(6):111-119, 2012.","title":"Exact matrix completion via convex optimization","authors":"Emmanuel J Candès, Benjamin Recht","year":"2012","venue":"Commun. ACM","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cao et al., 2015] Yongqiang Cao, Yang Chen, and Deepak Khosla. Spiking deep convolutional neural networks for energy-efficient object recognition. In IJCV, 113(1):54-66, 2015.","title":"Spiking deep convolutional neural networks for energy-efficient object recognition","authors":"Yongqiang Cao, Yang Chen, Deepak Khosla","year":"2015","venue":"IJCV","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2018] Jie Chen, Tengfei Ma, and Cao Xiao. Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv:1801.10247, 2018.","title":"Fastgcn: fast learning with graph convolutional networks via importance sampling","authors":"Jie Chen, Tengfei Ma, Cao Xiao","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cheng et al., 2020] Xiang Cheng, Yunzhe Hao, Jiaming Xu, and Bo Xu. LISNN: improving spiking neural networks with lateral interactions for robust object recognition. In IJCAI, pages 1519- 1525. ijcai.org, 2020.","title":"LISNN: improving spiking neural networks with lateral interactions for robust object recognition","authors":"Xiang Cheng, Yunzhe Hao, Jiaming Xu, Bo Xu","year":"2020","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2020/211"},{"position":7,"raw":"[Chung and Lu, 2002] Fan Chung and Linyuan Lu. Connected components in random graphs with given expected degree se- quences. Annals of combinatorics, 6(2):125-145, 2002.","title":"Connected components in random graphs with given expected degree sequences","authors":"Chung, Lu ;, Fan Chung, Linyuan Lu","year":"2002","venue":"Annals of combinatorics","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Defferrard et al., 2016] Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. In NIPS, pages 3837-3845, 2016.","title":"Convolutional neural networks on graphs with fast localized spectral filtering","authors":"Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst","year":"2016","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Esser et al., 2016] Steven K Esser, Paul A Merolla, John V Arthur, et al. Convolutional networks for fast, energy-efficient neuro- morphic computing. Proceedings of the national academy of sci- ences, 113(41):11441-11446, 2016.","title":"Convolutional networks for fast, energy-efficient neuromorphic computing","authors":"Paul A Steven K Esser, John V Merolla, Arthur","year":"2016","venue":"Proceedings of the national academy of sciences","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Fey et al., 2018] Matthias Fey, Jan Eric Lenssen, Frank Weichert, and Heinrich Müller. Splinecnn: Fast geometric deep learning with continuous b-spline kernels. In CVPR, pages 869-877. IEEE Computer Society, 2018.","title":"Splinecnn: Fast geometric deep learning with continuous b-spline kernels","authors":"Matthias Fey, Jan Eric Lenssen, Frank Weichert, Heinrich Müller","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Gerstner and Kistler, 2002] Wulfram Gerstner and Werner M Kistler. Spiking neuron models: Single neurons, populations, plasticity. Cambridge university press, 2002.","title":"Spiking neuron models: Single neurons, populations, plasticity","authors":"Kistler Gerstner, Wulfram Gerstner, Werner M Kistler","year":"2002","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hu et al., 2018] Yangfan Hu, Huajin Tang, Yueming Wang, and Gang Pan. Spiking deep residual network. arXiv:1805.01352, 2018.","title":"Spiking deep residual network","authors":"Yangfan Hu, Huajin Tang, Yueming Wang, Gang Pan","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hunger, 2005] Raphael Hunger. Floating point operations in matrix-vector calculus. 2005.","title":"Raphael Hunger. Floating point operations in matrix-vector calculus","authors":"Hunger","year":"2005","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Indiveri et al., 2015] Giacomo Indiveri, Federico Corradi, and Ning Qiao. Neuromorphic architectures for spiking deep neural networks. In IEDM, pages 4-2, 2015.","title":"Neuromorphic architectures for spiking deep neural networks","authors":"Giacomo Indiveri, Federico Corradi, Ning Qiao","year":"2015","venue":"IEDM","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kalofolias et al., 2014] Vassilis Kalofolias, Xavier Bresson, Michael M. Bronstein, and Pierre Vandergheynst. Matrix completion on graphs. CoRR, abs/1408.1717, 2014.","title":"Matrix completion on graphs","authors":"Vassilis Kalofolias, Xavier Bresson, Michael M Bronstein, Pierre Vandergheynst","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Kim et al., 2020] Seijoon Kim, Seongsik Park, Byunggook Na, and Sungroh Yoon. Spiking-yolo: spiking neural network for energy-efficient object detection. In AAAI, pages 11270-11277, 2020.","title":"Spiking-yolo: spiking neural network for energy-efficient object detection","authors":"Kim","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kipf and Welling, 2016] Thomas N Kipf and Max Welling. Semi- supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.","title":"Semisupervised classification with graph convolutional networks","authors":"N Thomas, Max Kipf, Welling","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[LeCun et al., 1998] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to docu- ment recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.","title":"Gradient-based learning applied to document recognition","authors":"Lecun","year":"1998","venue":"Proceedings of the IEEE","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lee et al., 2016] Junhaeng Lee, Tobi Delbrück, and Michael Pfeif- fer. Training deep spiking neural networks using backpropaga- tion. CoRR, abs/1608.08782, 2016.","title":"Training deep spiking neural networks using backpropagation","authors":"Lee","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Liu et al., 2020] Meng Liu, Hongyang Gao, and Shuiwang Ji. To- wards deeper graph neural networks. In SIGKDD, pages 338- 348, 2020.","title":"Towards deeper graph neural networks","authors":"Meng Liu, Hongyang Gao, Shuiwang Ji","year":"2020","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Maass, 1997] Wolfgang Maass. Networks of spiking neurons: the third generation of neural network models. Neural networks, 10(9):1659-1671, 1997.","title":"Networks of spiking neurons: the third generation of neural network models","authors":"Wolfgang Maass, Maass","year":"1997","venue":"Neural networks","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Merolla et al., 2014] Paul A Merolla, John V Arthur, Rodrigo Alvarez-Icaza, et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 345(6197):668-673, 2014.","title":"A million spiking-neuron integrated circuit with a scalable communication network and interface","authors":"John V Paul A Merolla, Rodrigo Arthur, Alvarez-Icaza","year":"2014","venue":"Science","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Monti et al., 2017a] Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, and Michael M. Bron- stein. Geometric deep learning on graphs and manifolds using mixture model cnns. In CVPR, pages 5425-5434. IEEE Com- puter Society, 2017.","title":"Geometric deep learning on graphs and manifolds using mixture model cnns","authors":"Monti","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Monti et al., 2017b] Federico Monti, Michael M. Bronstein, and Xavier Bresson. Geometric matrix completion with recurrent multi-graph neural networks. In NIPS, pages 3697-3707, 2017.","title":"Geometric matrix completion with recurrent multi-graph neural networks","authors":"Monti","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Rao et al., 2015] Nikhil Rao, Hsiang-Fu Yu, Pradeep Ravikumar, and Inderjit S. Dhillon. Collaborative filtering with graph in- formation: Consistency and scalable methods. In NIPS, pages 2107-2115, 2015.","title":"Collaborative filtering with graph information: Consistency and scalable methods","authors":"Nikhil Rao, Hsiang-Fu Yu, Pradeep Ravikumar, Inderjit S Dhillon","year":"2015","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Thiele et al., 2019] Johannes Christian Thiele, Olivier Bichler, and Antoine Dupret. Spikegrad: An ann-equivalent computation model for implementing backpropagation with spikes. arXiv preprint arXiv:1906.00851, 2019.","title":"Spikegrad: An ann-equivalent computation model for implementing backpropagation with spikes","authors":"Johannes Christian Thiele, Olivier Bichler, Antoine Dupret","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[van den Berg et al., 2017] Rianne van den Berg, Thomas N. Kipf, and Max Welling. Graph convolutional matrix completion. CoRR, abs/1706.02263, 2017.","title":"Graph convolutional matrix completion","authors":"Van Den Berg","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Veličković et al., 2017] Petar Veličković, Guillem Cucu- rull, Arantxa Casanova, et al. Graph attention networks. arXiv:1710.10903, 2017.","title":"Graph attention networks","authors":"Petar Veličković, Guillem Cucurull, Arantxa Casanova","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2019] Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu. Heterogeneous graph attention network. In WWW, pages 2022-2032, 2019.","title":"Heterogeneous graph attention network","authors":"Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, Philip S Yu","year":"2019","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wang et al., 2020] Xiao Wang, Meiqi Zhu, Deyu Bo, Peng Cui, Chuan Shi, and Jian Pei. AM-GCN: adaptive multi-channel graph convolutional networks. In KDD, pages 1243-1253. ACM, 2020.","title":"AM-GCN: adaptive multi-channel graph convolutional networks","authors":"Xiao Wang, Meiqi Zhu, Deyu Bo, Peng Cui, Chuan Shi, Jian Pei","year":"2020","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wu et al., 2019] Felix Wu, Amauri H Souza Jr, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q Weinberger. Simplifying graph convolutional networks. In ICML, 2019.","title":"Simplifying graph convolutional networks","authors":"Felix Wu, H Amauri, Tianyi Souza, Christopher Zhang, Tao Fifty, Kilian Q Yu, Weinberger","year":"2019","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Yang et al., 2016] Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov. Revisiting semi-supervised learning with graph embeddings. In ICML, volume 48 of JMLR Workshop and Con- ference Proceedings, pages 40-48. JMLR.org, 2016.","title":"Revisiting semi-supervised learning with graph embeddings","authors":"Zhilin Yang, William W Cohen, Ruslan Salakhutdinov","year":"2016","venue":"JMLR Workshop and Conference Proceedings","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Zhou et al., 2020] Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. Graph neural networks: A review of methods and applications. AI Open, 1:57-81, 2020.","title":"Graph neural networks: A review of methods and applications","authors":"Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, Maosong Sun","year":"2020","venue":"AI Open","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}