@article{chanussot2021open,
  title={Open catalyst 2020 (OC20) dataset and community challenges},
  author={Chanussot, Lowik and Das, Abhishek and Goyal, Siddharth and Lavril, Thibaut and Shuaibi, Muhammed and Riviere, Morgane and Tran, Kevin and Heras-Domingo, Javier and Ho, Caleb and Hu, Weihua and others},
  journal={ACS Catalysis},
  volume={11},
  number={10},
  pages={6059--6072},
  year={2021},
  publisher={ACS Publications}
}

@inproceedings{liu2021exact,
  title={EXACT: Scalable graph neural networks training via extreme activation compression},
  author={Liu, Zirui and Zhou, Kaixiong and Yang, Fan and Li, Li and Chen, Rui and Hu, Xia},
  booktitle={International Conference on Learning Representations},
  year={2021}
}


@article{zhou2019auto,
  title={Auto-gnn: Neural architecture search of graph neural networks},
  author={Zhou, Kaixiong and Song, Qingquan and Huang, Xiao and Hu, Xia},
  journal={arXiv preprint arXiv:1909.03184},
  year={2019}
}


@inproceedings{
duan2022a,
title={A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking},
author={Keyu Duan and Zirui Liu and Peihao Wang and Wenqing Zheng and Kaixiong Zhou and Tianlong Chen and Xia Hu and Zhangyang Wang},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=2QrFr_U782Z}
}


@article{chen2022bag,
  title={Bag of tricks for training deeper graph neural networks: A comprehensive benchmark study},
  author={Chen, Tianlong and Zhou, Kaixiong and Duan, Keyu and Zheng, Wenqing and Wang, Peihao and Hu, Xia and Wang, Zhangyang},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2022},
  publisher={IEEE}
}


@inproceedings{chen2022tinykg,
  title={TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems},
  author={Chen, Huiyuan and Li, Xiaoting and Zhou, Kaixiong and Hu, Xia and Yeh, Chin-Chia Michael and Zheng, Yan and Yang, Hao},
  booktitle={Proceedings of the 16th ACM Conference on Recommender Systems},
  pages={257--267},
  year={2022}
}


@inproceedings{derrow2021eta,
  title={Eta prediction with graph neural networks in google maps},
  author={Derrow-Pinion, Austin and She, Jennifer and Wong, David and Lange, Oliver and Hester, Todd and Perez, Luis and Nunkesser, Marc and Lee, Seongjae and Guo, Xueying and Wiltshire, Brett and others},
  booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
  pages={3767--3776},
  year={2021}
}

@inproceedings{you2021graph,
  title={Graph contrastive learning automated},
  author={You, Yuning and Chen, Tianlong and Shen, Yang and Wang, Zhangyang},
  booktitle={International Conference on Machine Learning},
  pages={12121--12132},
  year={2021},
  organization={PMLR}
}

@article{jiang2021could,
  title={Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models},
  author={Jiang, Dejun and Wu, Zhenxing and Hsieh, Chang-Yu and Chen, Guangyong and Liao, Ben and Wang, Zhe and Shen, Chao and Cao, Dongsheng and Wu, Jian and Hou, Tingjun},
  journal={Journal of cheminformatics},
  volume={13},
  number={1},
  pages={1--23},
  year={2021},
  publisher={BioMed Central}
}

@article{you2020graph,
  title={Graph contrastive learning with augmentations},
  author={You, Yuning and Chen, Tianlong and Sui, Yongduo and Chen, Ting and Wang, Zhangyang and Shen, Yang},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={5812--5823},
  year={2020}
}


@article{wieder2020compact,
  title={A compact review of molecular property prediction with graph neural networks},
  author={Wieder, Oliver and Kohlbacher, Stefan and Kuenemann, M{\'e}laine and Garon, Arthur and Ducrot, Pierre and Seidel, Thomas and Langer, Thierry},
  journal={Drug Discovery Today: Technologies},
  volume={37},
  pages={1--12},
  year={2020},
  publisher={Elsevier}
}


@article{xiong2019pushing,
  title={Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism},
  author={Xiong, Zhaoping and Wang, Dingyan and Liu, Xiaohong and Zhong, Feisheng and Wan, Xiaozhe and Li, Xutong and Li, Zhaojun and Luo, Xiaomin and Chen, Kaixian and Jiang, Hualiang and others},
  journal={Journal of medicinal chemistry},
  volume={63},
  number={16},
  pages={8749--8760},
  year={2019},
  publisher={ACS Publications}
}

@inproceedings{gasteiger_diffusion_2019,
  title = {Diffusion Improves Graph Learning},
  author = {Gasteiger, Johannes and Wei{\ss}enberger, Stefan and G{\"u}nnemann, Stephan},
  booktitle={Conference on Neural Information Processing Systems (NeurIPS)},
  year = {2019}
}

@inproceedings{xu2018representation,
  title={Representation learning on graphs with jumping knowledge networks},
  author={Xu, Keyulu and Li, Chengtao and Tian, Yonglong and Sonobe, Tomohiro and Kawarabayashi, Ken-ichi and Jegelka, Stefanie},
  booktitle={International conference on machine learning},
  pages={5453--5462},
  year={2018},
  organization={PMLR}
}

@inproceedings{
gasteiger2018combining,
title={Combining Neural Networks with Personalized PageRank for Classification on Graphs},
author={Johannes Gasteiger and Aleksandar Bojchevski and Stephan Günnemann},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=H1gL-2A9Ym},
}

@inproceedings{welling2016semi,
  title={Semi-supervised classification with graph convolutional networks},
  author={Welling, Max and Kipf, Thomas N},
  booktitle={J. International Conference on Learning Representations (ICLR 2017)},
  year={2016}
}

@article{
heat_chung,
author = {Fan Chung },
title = {The heat kernel as the pagerank of a graph},
journal = {Proceedings of the National Academy of Sciences},
volume = {104},
number = {50},
pages = {19735-19740},
year = {2007},
doi = {10.1073/pnas.0708838104},
URL = {https://www.pnas.org/doi/abs/10.1073/pnas.0708838104},
eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.0708838104}
}

@article{hamilton2017inductive,
  title={Inductive representation learning on large graphs},
  author={Hamilton, Will and Ying, Zhitao and Leskovec, Jure},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}


@article{wang2019deep,
  title={Deep graph library: A graph-centric, highly-performant package for graph neural networks},
  author={Wang, Minjie and Zheng, Da and Ye, Zihao and Gan, Quan and Li, Mufei and Song, Xiang and Zhou, Jinjing and Ma, Chao and Yu, Lingfan and Gai, Yu and others},
  journal={arXiv preprint arXiv:1909.01315},
  year={2019}
}

@article{wu2020graph,
  title={Graph neural networks in recommender systems: a survey},
  author={Wu, Shiwen and Sun, Fei and Zhang, Wentao and Xie, Xu and Cui, Bin},
  journal={ACM Computing Surveys (CSUR)},
  year={2020},
  publisher={ACM New York, NY}
}

@inproceedings{fan2019graph,
  title={Graph neural networks for social recommendation},
  author={Fan, Wenqi and Ma, Yao and Li, Qing and He, Yuan and Zhao, Eric and Tang, Jiliang and Yin, Dawei},
  booktitle={The world wide web conference},
  pages={417--426},
  year={2019}
}

@inproceedings{chaudhary2019anomaly,
  title={Anomaly detection using graph neural networks},
  author={Chaudhary, Anshika and Mittal, Himangi and Arora, Anuja},
  booktitle={2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon)},
  pages={346--350},
  year={2019},
  organization={IEEE}
}

@inproceedings{wu2019session,
  title={Session-based recommendation with graph neural networks},
  author={Wu, Shu and Tang, Yuyuan and Zhu, Yanqiao and Wang, Liang and Xie, Xing and Tan, Tieniu},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={33},
  number={01},
  pages={346--353},
  year={2019}
}

@article{xiong2021graph,
  title={Graph neural networks for automated de novo drug design},
  author={Xiong, Jiacheng and Xiong, Zhaoping and Chen, Kaixian and Jiang, Hualiang and Zheng, Mingyue},
  journal={Drug Discovery Today},
  volume={26},
  number={6},
  pages={1382--1393},
  year={2021},
  publisher={Elsevier}
}

@article{guo2020deep,
  title={A deep graph neural network-based mechanism for social recommendations},
  author={Guo, Zhiwei and Wang, Heng},
  journal={IEEE Transactions on Industrial Informatics},
  volume={17},
  number={4},
  pages={2776--2783},
  year={2020},
  publisher={IEEE}
}

@article{STGSN,
title = {STGSN — A Spatial–Temporal Graph Neural Network framework for time-evolving social networks},
journal = {Knowledge-Based Systems},
volume = {214},
pages = {106746},
year = {2021},
issn = {0950-7051},
doi = {https://doi.org/10.1016/j.knosys.2021.106746},
url = {https://www.sciencedirect.com/science/article/pii/S0950705121000095},
author = {Shengjie Min and Zhan Gao and Jing Peng and Liang Wang and Ke Qin and Bo Fang},
keywords = {Criminal Network Analysis, Social Network Analysis, Graph Neural Network, Spatial–Temporal Graph Neural Network, Attention network}
}

@inproceedings{chang2021sequential,
  title={Sequential recommendation with graph neural networks},
  author={Chang, Jianxin and Gao, Chen and Zheng, Yu and Hui, Yiqun and Niu, Yanan and Song, Yang and Jin, Depeng and Li, Yong},
  booktitle={Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={378--387},
  year={2021}
}


@article{chen2018rise,
  title={The rise of deep learning in drug discovery},
  author={Chen, Hongming and Engkvist, Ola and Wang, Yinhai and Olivecrona, Marcus and Blaschke, Thomas},
  journal={Drug discovery today},
  volume={23},
  number={6},
  pages={1241--1250},
  year={2018},
  publisher={Elsevier}
}

@techreport{page1999pagerank,
  title={The PageRank citation ranking: Bringing order to the web.},
  author={Page, Lawrence and Brin, Sergey and Motwani, Rajeev and Winograd, Terry},
  year={1999},
  institution={Stanford InfoLab}
}

@article{heat_physics,
  title={Heat kernel expansion: user's manual},
  author={Vassilevich, Dmitri V},
  journal={Physics reports},
  volume={388},
  number={5-6},
  pages={279--360},
  year={2003},
  publisher={Elsevier}
}

@article{velickovic2019deep,
  title={Deep Graph Infomax.},
  author={Velickovic, Petar and Fedus, William and Hamilton, William L and Li{\`o}, Pietro and Bengio, Yoshua and Hjelm, R Devon},
  journal={ICLR (Poster)},
  volume={2},
  number={3},
  pages={4},
  year={2019}
}

@inproceedings{grace,
  author = {Zhu, Yanqiao and Xu, Yichen and Yu, Feng and Liu, Qiang and Wu, Shu and Wang, Liang},
  title = {{Deep Graph Contrastive Representation Learning}},
  booktitle = {ICML Workshop on Graph Representation Learning and Beyond},
  year = {2020},
  url = {http://arxiv.org/abs/2006.04131}
}

@inproceedings{sun2021mocl,
  title={MoCL: data-driven molecular fingerprint via knowledge-aware contrastive learning from molecular graph},
  author={Sun, Mengying and Xing, Jing and Wang, Huijun and Chen, Bin and Zhou, Jiayu},
  booktitle={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
  pages={3585--3594},
  year={2021}
}

@article{kang2019robust,
  title={Robust graph learning from noisy data},
  author={Kang, Zhao and Pan, Haiqi and Hoi, Steven CH and Xu, Zenglin},
  journal={IEEE transactions on cybernetics},
  volume={50},
  number={5},
  pages={1833--1843},
  year={2019},
  publisher={IEEE}
}


@article{velivckovic2017graph,
  title={Graph attention networks},
  author={Veli{\v{c}}kovi{\'c}, Petar and Cucurull, Guillem and Casanova, Arantxa and Romero, Adriana and Lio, Pietro and Bengio, Yoshua},
  journal={arXiv preprint arXiv:1710.10903},
  year={2017}
}


@article{thakoor2021large,
  title={Large-scale representation learning on graphs via bootstrapping},
  author={Thakoor, Shantanu and Tallec, Corentin and Azar, Mohammad Gheshlaghi and Azabou, Mehdi and Dyer, Eva L and Munos, Remi and Veli{\v{c}}kovi{\'c}, Petar and Valko, Michal},
  journal={arXiv preprint arXiv:2102.06514},
  year={2021}
}

@article{sen2008collective,
  title={Collective classification in network data},
  author={Sen, Prithviraj and Namata, Galileo and Bilgic, Mustafa and Getoor, Lise and Galligher, Brian and Eliassi-Rad, Tina},
  journal={AI magazine},
  volume={29},
  number={3},
  pages={93--93},
  year={2008}
}

@article{hu2020open,
  title={Open graph benchmark: Datasets for machine learning on graphs},
  author={Hu, Weihua and Fey, Matthias and Zitnik, Marinka and Dong, Yuxiao and Ren, Hongyu and Liu, Bowen and Catasta, Michele and Leskovec, Jure},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={22118--22133},
  year={2020}
}
@inproceedings{clustergcn,
  title={Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks},
  author={Chiang, Wei-Lin and Liu, Xuanqing and Si, Si and Li, Yang and Bengio, Samy and Hsieh, Cho-Jui},
  booktitle={Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery \& data mining},
  pages={257--266},
  year={2019}
}

@article{metis,
  title={METIS: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices},
  author={Karypis, George and Kumar, Vipin},
  year={1997}
}

@misc{
lagraph,
title={Self-Supervised Representation Learning via Latent Graph Prediction},
author={Yaochen Xie and Zhao Xu and Shuiwang Ji},
year={2022},
url={https://openreview.net/forum?id=Da3ZcbjRWy}
}

@inproceedings{
ccassg_long,
title={From Canonical Correlation Analysis to Self-supervised Graph Neural Networks},
author={Hengrui Zhang and Qitian Wu and Junchi Yan and David Wipf and Philip S. Yu},
booktitle={Advances in Neural Information Processing Systems},
editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year={2021},
url={https://openreview.net/forum?id=X3TdREzbZN}
}

@misc{ccassg,
  doi = {10.48550/ARXIV.2106.12484},
  
  url = {https://arxiv.org/abs/2106.12484},
  
  author = {Zhang, Hengrui and Wu, Qitian and Yan, Junchi and Wipf, David and Yu, Philip S.},
  
  keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {From Canonical Correlation Analysis to Self-supervised Graph Neural Networks},
  
  publisher = {arXiv},
  
  year = {2021},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}


@article{oord2018representation,
  title={Representation learning with contrastive predictive coding},
  author={Oord, Aaron van den and Li, Yazhe and Vinyals, Oriol},
  journal={arXiv preprint arXiv:1807.03748},
  year={2018}
}

@article{hjelm2018learning,
  title={Learning deep representations by mutual information estimation and maximization},
  author={Hjelm, R Devon and Fedorov, Alex and Lavoie-Marchildon, Samuel and Grewal, Karan and Bachman, Phil and Trischler, Adam and Bengio, Yoshua},
  journal={arXiv preprint arXiv:1808.06670},
  year={2018}
}


@article{grill2020bootstrap,
  title={Bootstrap your own latent-a new approach to self-supervised learning},
  author={Grill, Jean-Bastien and Strub, Florian and Altch{\'e}, Florent and Tallec, Corentin and Richemond, Pierre and Buchatskaya, Elena and Doersch, Carl and Avila Pires, Bernardo and Guo, Zhaohan and Gheshlaghi Azar, Mohammad and others},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={21271--21284},
  year={2020}
}


@article{zhu2020deep,
  title={Deep graph contrastive representation learning},
  author={Zhu, Yanqiao and Xu, Yichen and Yu, Feng and Liu, Qiang and Wu, Shu and Wang, Liang},
  journal={arXiv preprint arXiv:2006.04131},
  year={2020}
}



@inproceedings{chen2020simple,
  title={A simple framework for contrastive learning of visual representations},
  author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  booktitle={International conference on machine learning},
  pages={1597--1607},
  year={2020},
  organization={PMLR}
}





@article{zhang2021canonical,
  title={From canonical correlation analysis to self-supervised graph neural networks},
  author={Zhang, Hengrui and Wu, Qitian and Yan, Junchi and Wipf, David and Yu, Philip S},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={76--89},
  year={2021}
}



@inproceedings{randomerasing,
  title={Random erasing data augmentation},
  author={Zhong, Zhun and Zheng, Liang and Kang, Guoliang and Li, Shaozi and Yang, Yi},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={34},
  number={07},
  pages={13001--13008},
  year={2020}
}

@article{imageaugment,
  title={A survey on image data augmentation for deep learning},
  author={Shorten, Connor and Khoshgoftaar, Taghi M},
  journal={Journal of big data},
  volume={6},
  number={1},
  pages={1--48},
  year={2019},
  publisher={SpringerOpen}
}

@article{
  gat,
  title="{Graph Attention Networks}",
  author={Veli{\v{c}}kovi{\'{c}}, Petar and Cucurull, Guillem and Casanova, Arantxa and Romero, Adriana and Li{\`{o}}, Pietro and Bengio, Yoshua},
  journal={International Conference on Learning Representations},
  year={2018},
  url={https://openreview.net/forum?id=rJXMpikCZ},
}

@article{gate,
  title={Graph attention auto-encoders},
  author={Salehi, Amin and Davulcu, Hasan},
  journal={arXiv preprint arXiv:1905.10715},
  year={2019}
}

@inproceedings{graphmae_old,
author = {Hou, Zhenyu and Liu, Xiao and Cen, Yukuo and Dong, Yuxiao and Yang, Hongxia and Wang, Chunjie and Tang, Jie},
title = {GraphMAE: Self-Supervised Masked Graph Autoencoders},
year = {2022},
isbn = {9781450393850},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3534678.3539321},
doi = {10.1145/3534678.3539321},
booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {594–604},
numpages = {11},
keywords = {graph representation learning, graph neural networks, self-supervised learning},
location = {Washington DC, USA},
series = {KDD '22}
}

@misc{graphmae,
  doi = {10.48550/ARXIV.2205.10803},
  
  url = {https://arxiv.org/abs/2205.10803},
  
  author = {Hou, Zhenyu and Liu, Xiao and Cen, Yukuo and Dong, Yuxiao and Yang, Hongxia and Wang, Chunjie and Tang, Jie},
  
  keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {GraphMAE: Self-Supervised Masked Graph Autoencoders},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}


@inproceedings{infogcl_long,
 author = {Xu, Dongkuan and Cheng, Wei and Luo, Dongsheng and Chen, Haifeng and Zhang, Xiang},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
 pages = {30414--30425},
 publisher = {Curran Associates, Inc.},
 title = {InfoGCL: Information-Aware Graph Contrastive Learning},
 url = {https://proceedings.neurips.cc/paper/2021/file/ff1e68e74c6b16a1a7b5d958b95e120c-Paper.pdf},
 volume = {34},
 year = {2021}
}

@misc{infogcl,
  doi = {10.48550/ARXIV.2110.15438},
  
  url = {https://arxiv.org/abs/2110.15438},
  
  author = {Xu, Dongkuan and Cheng, Wei and Luo, Dongsheng and Chen, Haifeng and Zhang, Xiang},
  
  keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {InfoGCL: Information-Aware Graph Contrastive Learning},
  
  publisher = {arXiv},
  
  year = {2021},
  
  copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International}
}


@incollection{mvgrl,
 author = {Hassani, Kaveh and Khasahmadi, Amir Hosein},
 booktitle = {Proceedings of International Conference on Machine Learning},
 pages = {3451--3461},
 title = {Contrastive Multi-View Representation Learning on Graphs},
 year = {2020}
}

@inproceedings{bojchevski2020pprgo,
  title={Scaling Graph Neural Networks with Approximate PageRank},
  author={Bojchevski, Aleksandar and Gasteiger, Johannes and Perozzi, Bryan and Kapoor, Amol and Blais, Martin and R{\'o}zemberczki, Benedek and Lukasik, Michal and G{\"u}nnemann, Stephan},
  booktitle = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  year={2020},
  publisher = {ACM},
  address = {New York, NY, USA},
}

@inproceedings{
ppnp,
title={Combining Neural Networks with Personalized PageRank for Classification on Graphs},
author={Johannes Gasteiger and Aleksandar Bojchevski and Stephan Günnemann},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=H1gL-2A9Ym},
}

@article{graddesc,
  author    = {Sebastian Ruder},
  title     = {An overview of gradient descent optimization algorithms},
  journal   = {CoRR},
  volume    = {abs/1609.04747},
  year      = {2016},
  url       = {http://arxiv.org/abs/1609.04747},
  eprinttype = {arXiv},
  eprint    = {1609.04747},
  timestamp = {Mon, 13 Aug 2018 16:48:10 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/Ruder16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@inproceedings{
adamw,
title={Decoupled Weight Decay Regularization},
author={Ilya Loshchilov and Frank Hutter},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=Bkg6RiCqY7},
}

@article{kld,
  title={On information and sufficiency},
  author={Kullback, Solomon and Leibler, Richard A},
  journal={The annals of mathematical statistics},
  volume={22},
  number={1},
  pages={79--86},
  year={1951},
  publisher={JSTOR}
}

@inbook{proximity,
author = {Jing Zhu and Xingyu Lu and Mark Heimann and Danai Koutra},
title = {Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding},
booktitle = {Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)},
chapter = {},
pages = {163-171},
doi = {10.1137/1.9781611976700.19},
URL = {https://epubs.siam.org/doi/abs/10.1137/1.9781611976700.19},
eprint = {https://epubs.siam.org/doi/pdf/10.1137/1.9781611976700.19},
    abstract = { Abstract While most network embedding techniques model the relative positions of nodes in a network, recently there has been significant interest in structural embeddings that model node role equivalences, irrespective of their distances to any specific nodes. We present PhUSION, a proximity-based unified framework for computing structural and positional node embeddings, which leverages well-established methods for calculating node proximity scores. Clarifying a point of contention in the literature, we show which step of PhUSION produces the different kinds of embeddings and what steps can be used by both. Moreover, by aggregating the PhUSION node embeddings, we obtain graph-level features that model information lost by previous graph feature learning and kernel methods. In a comprehensive empirical study with over 10 datasets, 4 tasks, and 35 methods, we systematically reveal successful design choices for node and graph-level machine learning with embeddings. }
}

@inproceedings{link_predict,
author = {Liben-Nowell, David and Kleinberg, Jon},
title = {The Link Prediction Problem for Social Networks},
year = {2003},
isbn = {1581137230},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/956863.956972},
doi = {10.1145/956863.956972},
abstract = {Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link prediction problem, and develop approaches to link prediction based on measures the "proximity" of nodes in a network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.},
booktitle = {Proceedings of the Twelfth International Conference on Information and Knowledge Management},
pages = {556–559},
numpages = {4},
keywords = {link analysis, social networks, link prediction},
location = {New Orleans, LA, USA},
series = {CIKM '03}
}

@INPROCEEDINGS{link_predict2,
  author={Murata, Tsuyoshi and Moriyasu, Sakiko},
  booktitle={IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)}, 
  title={Link Prediction of Social Networks Based on Weighted Proximity Measures}, 
  year={2007},
  volume={},
  number={},
  pages={85-88},
  doi={10.1109/WI.2007.52}}

@inproceedings{cfec,
author = {Koren, Yehuda and North, Stephen C. and Volinsky, Chris},
title = {Measuring and Extracting Proximity in Networks},
year = {2006},
isbn = {1595933395},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1150402.1150432},
doi = {10.1145/1150402.1150432},
abstract = {Measuring distance or some other form of proximity between objects is a standard data mining tool. Connection subgraphs were recently proposed as a way to demonstrate proximity between nodes in networks. We propose a new way of measuring and extracting proximity in networks called "cycle free effective conductance"(CFEC). Our proximity measure can handle more than two endpoints, directed edges, is statistically well-behaved, and produces an effectiveness score for the computed subgraphs. We provide an efficien talgorithm. Also, we report experimental results and show examples for three large network data sets: a telecommunications calling graph, the IMDB actors graph, and an academic co-authorship network.},
booktitle = {Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {245–255},
numpages = {11},
keywords = {cycle-free escape probability, escape probability, connection subgraph, random walks, proximity subgraph, proximity},
location = {Philadelphia, PA, USA},
series = {KDD '06}
}

@article{katz,
  title={A new status index derived from sociometric analysis},
  author={Katz, Leo},
  journal={Psychometrika},
  volume={18},
  number={1},
  pages={39--43},
  year={1953},
  publisher={Springer}
}

@inproceedings{simrank,
  title={Simrank: a measure of structural-context similarity},
  author={Jeh, Glen and Widom, Jennifer},
  booktitle={Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining},
  pages={538--543},
  year={2002}
}

@article{prox_measures,
  doi = {10.48550/ARXIV.MATH/0602073},
  
  url = {https://arxiv.org/abs/math/0602073},
  
  author = {Chebotarev, Pavel and Shamis, Elena},
  
  keywords = {Combinatorics (math.CO), Data Structures and Algorithms (cs.DS), Networking and Internet Architecture (cs.NI), Metric Geometry (math.MG), FOS: Mathematics, FOS: Mathematics, FOS: Computer and information sciences, FOS: Computer and information sciences, 05C50; 05C05; 15A09; 15A51},
  
  title = {On Proximity Measures for Graph Vertices},
  
  publisher = {arXiv},
  
  year = {2006},
  
  copyright = {Assumed arXiv.org perpetual, non-exclusive license to distribute this article for submissions made before January 2004}
}

@article{tang2023autodifferentiation,
  title={Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning},
  author={Tang, Yuxin and Ding, Zhimin and Jankov, Dimitrije and Yuan, Binhang and Bourgeois, Daniel and Jermaine, Chris},
  journal={arXiv preprint arXiv:2306.00088},
  year={2023}
}

@article{homophily,
  title={Birds of a feather: Homophily in social networks},
  author={McPherson, Miller and Smith-Lovin, Lynn and Cook, James M},
  journal={Annual review of sociology},
  volume={27},
  number={1},
  pages={415--444},
  year={2001},
  publisher={Annual Reviews 4139 El Camino Way, PO Box 10139, Palo Alto, CA 94303-0139, USA}
}

@inproceedings{augmentationfree,
  title={Augmentation-free self-supervised learning on graphs},
  author={Lee, Namkyeong and Lee, Junseok and Park, Chanyoung},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={36},
  number={7},
  pages={7372--7380},
  year={2022}
}

@article{augmentationfreecont,
  title={Augmentation-free graph contrastive learning},
  author={Wang, Haonan and Zhang, Jieyu and Zhu, Qi and Huang, Wei},
  journal={arXiv preprint arXiv:2204.04874},
  year={2022}
}