@inproceedings{DGCL,
	title = {Disentangled {Contrastive} {Learning} on {Graphs}},
	language = {en},
	urldate = {2022-03-17},
	author = {Li, Haoyang and Wang, Xin and Zhang, Ziwei and Yuan, Zehuan and Li, Hang and Zhu, Wenwu},
	year = {2021},
	booktitle = {NeurIPS},
	keywords = {Disentangled Representation Learning},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/48U89M4J/Li et al. - 2021 - Disentangled Contrastive Learning on Graphs.pdf:application/pdf;Snapshot:/Users/spectator2ts/Zotero/storage/VDE76K88/forum.html:text/html},
}

@inproceedings{GCN,
	title = {Semi-{Supervised} {Classification} with {Graph} {Convolutional} {Networks}},
	abstract = {We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.},
	urldate = {2022-03-26},
	booktitle = {ICLR},
	author = {Kipf, Thomas N. and Welling, Max},
	year = {2017},
	file = {arXiv Fulltext PDF:/Users/spectator2ts/Zotero/storage/FF47EN78/Kipf and Welling - 2017 - Semi-Supervised Classification with Graph Convolut.pdf:application/pdf;arXiv.org Snapshot:/Users/spectator2ts/Zotero/storage/V28I52LH/1609.html:text/html},
}

@article{GatedGNN,
	title = {Gated {Graph} {Sequence} {Neural} {Networks}},
	abstract = {Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases. In this work, we study feature learning techniques for graph-structured inputs. Our starting point is previous work on Graph Neural Networks (Scarselli et al., 2009), which we modify to use gated recurrent units and modern optimization techniques and then extend to output sequences. The result is a flexible and broadly useful class of neural network models that has favorable inductive biases relative to purely sequence-based models (e.g., LSTMs) when the problem is graph-structured. We demonstrate the capabilities on some simple AI (bAbI) and graph algorithm learning tasks. We then show it achieves state-of-the-art performance on a problem from program verification, in which subgraphs need to be matched to abstract data structures.},
	urldate = {2022-05-05},
	journal = {ICLR},
	author = {Li, Yujia and Tarlow, Daniel and Brockschmidt, Marc and Zemel, Richard},
	year = {2016},
	file = {arXiv Fulltext PDF:/Users/spectator2ts/Zotero/storage/9KLBR3BM/Li et al. - 2017 - Gated Graph Sequence Neural Networks.pdf:application/pdf;arXiv.org Snapshot:/Users/spectator2ts/Zotero/storage/ICAQLVIR/1511.html:text/html},
}

@inproceedings{InfoGraph,
	title = {{InfoGraph}: {Unsupervised} and {Semi}-supervised {Graph}-{Level} {Representation} {Learning} via {Mutual} {Information} {Maximization}},
	shorttitle = {{InfoGraph}},
	booktitle = {ICLR},
	author = {Sun, Fan-Yun and Hoffmann, Jordan and Verma, Vikas and Tang, Jian},
	year = {2019},
	keywords = {Contrastive Learning},
	file = {arXiv Fulltext PDF:/Users/spectator2ts/Zotero/storage/YVD5N7J8/Sun et al. - 2020 - InfoGraph Unsupervised and Semi-supervised Graph-.pdf:application/pdf;arXiv.org Snapshot:/Users/spectator2ts/Zotero/storage/73HZAMZH/1908.html:text/html},
}

@inproceedings{xu_self-supervised_2021,
	title = {Self-supervised {Graph}-level {Representation} {Learning} with {Local} and {Global} {Structure}},
	url = {https://proceedings.mlr.press/v139/xu21g.html},
	abstract = {This paper studies unsupervised/self-supervised whole-graph representation learning, which is critical in many tasks such as molecule properties prediction in drug and material discovery. Existing methods mainly focus on preserving the local similarity structure between different graph instances but fail to discover the global semantic structure of the entire data set. In this paper, we propose a unified framework called Local-instance and Global-semantic Learning (GraphLoG) for self-supervised whole-graph representation learning. Specifically, besides preserving the local similarities, GraphLoG introduces the hierarchical prototypes to capture the global semantic clusters. An efficient online expectation-maximization (EM) algorithm is further developed for learning the model. We evaluate GraphLoG by pre-training it on massive unlabeled graphs followed by fine-tuning on downstream tasks. Extensive experiments on both chemical and biological benchmark data sets demonstrate the effectiveness of the proposed approach.},
	language = {en},
	urldate = {2022-05-05},
	booktitle = {ICML},
	author = {Xu, Minghao and Wang, Hang and Ni, Bingbing and Guo, Hongyu and Tang, Jian},
	month = jul,
	year = {2021},
	note = {ISSN: 2640-3498},
	pages = {11548--11558},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/KTJTG4FU/Xu et al. - 2021 - Self-supervised Graph-level Representation Learnin.pdf:application/pdf},
}

@inproceedings{MomentumContrast,
	title = {Momentum {Contrast} for {Unsupervised} {Visual} {Representation} {Learning}},
	url = {https://openaccess.thecvf.com/content_CVPR_2020/html/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.html},
	urldate = {2022-05-05},
	author = {He, Kaiming and Fan, Haoqi and Wu, Yuxin and Xie, Saining and Girshick, Ross},
	year = {2020},
	keywords = {Contrastive Learning},
	booktitle = {CVPR},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/CCYH3RBN/He et al. - 2020 - Momentum Contrast for Unsupervised Visual Represen.pdf:application/pdf;Snapshot:/Users/spectator2ts/Zotero/storage/JMA2AUYB/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.html:text/html},
}

@inproceedings{SimCLR,
	title = {A {Simple} {Framework} for {Contrastive} {Learning} of {Visual} {Representations}},
	language = {en},
	urldate = {2022-05-05},
	booktitle = {ICML},
	author = {Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
	year = {2020},
	keywords = {Contrastive Learning},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/35APYUEA/Chen et al. - 2020 - A Simple Framework for Contrastive Learning of Vis.pdf:application/pdf;Supplementary PDF:/Users/spectator2ts/Zotero/storage/7HRDIWM4/Chen et al. - 2020 - A Simple Framework for Contrastive Learning of Vis.pdf:application/pdf},
}

@inproceedings{NCE,
	title = {Unsupervised {Feature} {Learning} via {Non}-{Parametric} {Instance} {Discrimination}},
	booktitle = {CVPR},
	author = {Wu, Zhirong and Xiong, Yuanjun and Yu, Stella X. and Lin, Dahua},
	year = {2018},
	keywords = {Contrastive Learning},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/CKYB9DWF/Wu et al. - 2018 - Unsupervised Feature Learning via Non-Parametric I.pdf:application/pdf;Snapshot:/Users/spectator2ts/Zotero/storage/USS6QP59/Wu_Unsupervised_Feature_Learning_CVPR_2018_paper.html:text/html},
}

@article{InfoNCE,
	title = {Representation {Learning} with {Contrastive} {Predictive} {Coding}},
	journal = {arXiv e-prints},
	author = {van den Oord, Aaron and Li, Yazhe and Vinyals, Oriol},
	year = {2018},
	keywords = {Computer Science - Machine Learning, Contrastive Learning, Statistics - Machine Learning},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/L2KIT7XU/van den Oord et al. - 2018 - Representation Learning with Contrastive Predictiv.pdf:application/pdf},
}

@inproceedings{GCC,
	title = {{GCC}: {Graph} {Contrastive} {Coding} for {Graph} {Neural} {Network} {Pre}-{Training}},
	booktitle = {KDD},
	author = {Qiu, Jiezhong and Chen, Qibin and Dong, Yuxiao and Zhang, Jing and Yang, Hongxia and Ding, Ming and Wang, Kuansan and Tang, Jie},
	year = {2020}
}


@inproceedings{GraphCL,
	title = {Graph {Contrastive} {Learning} with {Augmentations}},
	booktitle = {NeurIPS},
	author = {You, Yuning and Chen, Tianlong and Sui, Yongduo and Chen, Ting and Wang, Zhangyang and Shen, Yang},
	year = {2020},
	keywords = {Contrastive Learning},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/GSTV7CZE/You et al. - 2020 - Graph Contrastive Learning with Augmentations.pdf:application/pdf;You et al. - Graph Contrastive Learning with Augmentations.pdf:/Users/spectator2ts/Zotero/storage/C2U86DJ8/You et al. - Graph Contrastive Learning with Augmentations.pdf:application/pdf},
}

@inproceedings{hsieh_learning_2018,
	title = {Learning to {Decompose} and {Disentangle} {Representations} for {Video} {Prediction}},
	booktitle = {NeurIPS},
	author = {Hsieh, Jun-Ting and Liu, Bingbin and Huang, De-An and Fei-Fei, Li F and Niebles, Juan Carlos},
	year = {2018},
	keywords = {Disentangled Representation Learning},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/J5RAXVBC/Hsieh et al. - 2018 - Learning to Decompose and Disentangle Representati.pdf:application/pdf},
}

@inproceedings{InfoGAN,
	title = {{InfoGAN}: {Interpretable} {Representation} {Learning} by {Information} {Maximizing} {Generative} {Adversarial} {Nets}},
	shorttitle = {{InfoGAN}},
	url = {https://proceedings.neurips.cc/paper/2016/hash/7c9d0b1f96aebd7b5eca8c3edaa19ebb-Abstract.html},
	abstract = {This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. InfoGAN is a generative adversarial network that also maximizes the mutual information between a small subset of the latent variables and the observation. We derive a lower bound to the mutual information objective that can be optimized efficiently, and show that our training procedure can be interpreted as a variation of the Wake-Sleep algorithm. Specifically, InfoGAN successfully disentangles writing styles from digit shapes on the MNIST dataset, pose from lighting of 3D rendered images, and background digits from the central digit on the SVHN dataset. It also discovers visual concepts that include hair styles, presence/absence of eyeglasses, and emotions on the CelebA face dataset. Experiments show that InfoGAN learns interpretable representations that are competitive with representations learned by existing fully supervised methods.},
	urldate = {2022-05-05},
	booktitle = {NeurIPS},
	author = {Chen, Xi and Duan, Yan and Houthooft, Rein and Schulman, John and Sutskever, Ilya and Abbeel, Pieter},
	year = {2016},
	keywords = {Disentangled Representation Learning},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/6AEBWAQU/Chen et al. - 2016 - InfoGAN Interpretable Representation Learning by .pdf:application/pdf},
}

@inproceedings{ma_disentangled_2018,
	title = {Disentangled {Person} {Image} {Generation}},
	url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Ma_Disentangled_Person_Image_CVPR_2018_paper.html},
	urldate = {2022-05-05},
	author = {Ma, Liqian and Sun, Qianru and Georgoulis, Stamatios and Van Gool, Luc and Schiele, Bernt and Fritz, Mario},
	year = {2018},
	keywords = {Disentangled Representation Learning},
	booktile = {CVPR},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/VZFYC99B/Ma et al. - 2018 - Disentangled Person Image Generation.pdf:application/pdf;Snapshot:/Users/spectator2ts/Zotero/storage/GAPQS4S3/Ma_Disentangled_Person_Image_CVPR_2018_paper.html:text/html},
}

@inproceedings{xinyi_capsule_2018,
	title = {Capsule {Graph} {Neural} {Network}},
	url = {https://openreview.net/forum?id=Byl8BnRcYm},
	abstract = {Inspired by CapsNet, we propose a novel architecture for graph embeddings on the basis of node features extracted from GNN.},
	language = {en},
	urldate = {2022-05-05},
	author = {Xinyi, Zhang and Chen, Lihui},
	year = {2018},
	keywords = {Disentangled Representation Learning},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/UDQE497H/Xinyi and Chen - 2018 - Capsule Graph Neural Network.pdf:application/pdf;Snapshot:/Users/spectator2ts/Zotero/storage/F49P35V6/forum.html:text/html},
}

@inproceedings{DisenGCN,
	title = {Disentangled {Graph} {Convolutional} {Networks}},
	language = {en},
	urldate = {2022-05-05},
	booktitle = {ICML},
	author = {Ma, Jianxin and Cui, Peng and Kuang, Kun and Wang, Xin and Zhu, Wenwu},
	year = {2019},
	keywords = {Disentangled Representation Learning},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/3T63LBAW/Ma et al. - 2019 - Disentangled Graph Convolutional Networks.pdf:application/pdf},
}

@inproceedings{yang_factorizable_2020,
	title = {Factorizable {Graph} {Convolutional} {Networks}},
	url = {https://proceedings.neurips.cc/paper/2020/hash/ea3502c3594588f0e9d5142f99c66627-Abstract.html},
	abstract = {Graphs have been widely adopted to denote structural connections between entities. The relations are in many cases heterogeneous, but entangled together and denoted merely as a single edge between a pair of nodes. For example, in a social network graph, users in different latent relationships like friends and colleagues, are usually connected via a bare edge that conceals such intrinsic connections. In this paper, we introduce a novel graph convolutional network (GCN), termed as factorizable graph convolutional network (FactorGCN), that explicitly disentangles such intertwined relations encoded in a graph. FactorGCN takes a simple graph as input, and disentangles it into several factorized graphs, each of which represents a latent and disentangled relation among nodes. The features of the nodes are then aggregated separately in each factorized latent space to produce disentangled features, which further leads to better performances for downstream tasks. We evaluate the proposed FactorGCN both qualitatively and quantitatively on the synthetic and real-world datasets, and demonstrate that it yields truly encouraging results in terms of both disentangling and feature aggregation. Code is publicly available at https://github.com/ihollywhy/FactorGCN.PyTorch.},
	urldate = {2022-05-05},
	booktitle = {NeurIPS},
	publisher = {Curran Associates, Inc.},
	author = {Yang, Yiding and Feng, Zunlei and Song, Mingli and Wang, Xinchao},
	year = {2020},
	keywords = {Disentangled Representation Learning},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/6KQF7HV3/Yang et al. - 2020 - Factorizable Graph Convolutional Networks.pdf:application/pdf},
}

@inproceedings{VGAE,
	title = {Variational {Graph} {Auto}-{Encoders}},
	abstract = {We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model using a graph convolutional network (GCN) encoder and a simple inner product decoder. Our model achieves competitive results on a link prediction task in citation networks. In contrast to most existing models for unsupervised learning on graph-structured data and link prediction, our model can naturally incorporate node features, which significantly improves predictive performance on a number of benchmark datasets.},
	urldate = {2022-05-05},
	booktitle = {NeurIPS},
	author = {Kipf, Thomas N. and Welling, Max},
	year = {2016},
	keywords = {Computer Science - Machine Learning, Genarative Method, Statistics - Machine Learning},
	file = {arXiv Fulltext PDF:/Users/spectator2ts/Zotero/storage/ZN3F3P65/Kipf and Welling - 2016 - Variational Graph Auto-Encoders.pdf:application/pdf;arXiv.org Snapshot:/Users/spectator2ts/Zotero/storage/4GZEHTSN/1611.html:text/html},
}

@article{burgess_understanding_2018,
	title = {Understanding disentangling in \${\textbackslash}beta\$-{VAE}},
	url = {http://arxiv.org/abs/1804.03599},
	abstract = {We present new intuitions and theoretical assessments of the emergence of disentangled representation in variational autoencoders. Taking a rate-distortion theory perspective, we show the circumstances under which representations aligned with the underlying generative factors of variation of data emerge when optimising the modified ELBO bound in \${\textbackslash}beta\$-VAE, as training progresses. From these insights, we propose a modification to the training regime of \${\textbackslash}beta\$-VAE, that progressively increases the information capacity of the latent code during training. This modification facilitates the robust learning of disentangled representations in \${\textbackslash}beta\$-VAE, without the previous trade-off in reconstruction accuracy.},
	urldate = {2022-05-05},
	journal = {arXiv:1804.03599 [cs, stat]},
	author = {Burgess, Christopher P. and Higgins, Irina and Pal, Arka and Matthey, Loic and Watters, Nick and Desjardins, Guillaume and Lerchner, Alexander},
	year = {2018},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Genarative Method, Computer Science - Artificial Intelligence},
	file = {arXiv Fulltext PDF:/Users/spectator2ts/Zotero/storage/PS4F4NRA/Burgess et al. - 2018 - Understanding disentangling in \$beta\$-VAE.pdf:application/pdf;arXiv.org Snapshot:/Users/spectator2ts/Zotero/storage/R5GDINAV/1804.html:text/html},
}


@article{blackbox,
	title = {Opening the {Black} {Box} of {Deep} {Neural} {Networks} via {Information}},
	journal = {arXiv:1703.00810 [cs]},
	author = {Shwartz-Ziv, Ravid and Tishby, Naftali},
	month = apr,
	year = {2017}
}

@inproceedings{GIB,
	title = {Graph {Information} {Bottleneck}},
	booktitle = {NeurIPS},
	author = {Wu, Tailin and Ren, Hongyu and Li, Pan and Leskovec, Jure},
	year = {2020},
	keywords = {Information Theory},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/7X3XTNXX/Wu et al. - 2020 - Graph Information Bottleneck.pdf:application/pdf},
}

@inproceedings{zhao_learning_2021,
	title = {Learning {View}-{Disentangled} {Human} {Pose} {Representation} by {Contrastive} {Cross}-{View} {Mutual} {Information} {Maximization}},
	booktitle={CVPR},
	author = {Zhao, Long and Wang, Yuxiao and Zhao, Jiaping and Yuan, Liangzhe and Sun, Jennifer J. and Schroff, Florian and Adam, Hartwig and Peng, Xi and Metaxas, Dimitris and Liu, Ting},
	year = {2021},
	keywords = {Disentangled Representation Learning},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/D3K28RLJ/Zhao et al. - 2021 - Learning View-Disentangled Human Pose Representati.pdf:application/pdf;Snapshot:/Users/spectator2ts/Zotero/storage/G6VNEWS3/Zhao_Learning_View-Disentangled_Human_Pose_Representation_by_Contrastive_Cross-View_Mutual_Info.html:text/html},
}

@inproceedings{InfoGCL,
	title = {{InfoGCL}: {Information}-{Aware} {Graph} {Contrastive} {Learning}},
	booktitle = {NeurIPS},
	author = {Xu, Dongkuan and Cheng, Wei and Luo, Dongsheng and Chen, Haifeng and Zhang, Xiang},
	year = {2021},
	keywords = {Information Theory},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/IG9UBBFR/Xu et al. - 2021 - InfoGCL Information-Aware Graph Contrastive Learn.pdf:application/pdf},
}

@inproceedings{yanardag_deep_2015,
	title = {Deep {Graph} {Kernels}},
	url = {https://dl.acm.org/doi/10.1145/2783258.2783417},
	abstract = {In this paper, we present Deep Graph Kernels, a uniﬁed framework to learn latent representations of sub-structures for graphs, inspired by latest advancements in language modeling and deep learning. Our framework leverages the dependency information between sub-structures by learning their latent representations. We demonstrate instances of our framework on three popular graph kernels, namely Graphlet kernels, Weisfeiler-Lehman subtree kernels, and Shortest-Path graph kernels. Our experiments on several benchmark datasets show that Deep Graph Kernels achieve significant improvements in classiﬁcation accuracy over state-of-the-art graph kernels.},
	language = {en},
	urldate = {2022-05-11},
	booktitle = {SIGKDD},
	publisher = {ACM},
	author = {Yanardag, Pinar and Vishwanathan, S.V.N.},
	year = {2015},
	keywords = {bioinformatics, collaboration networks, deep learning, graph kernels, r-convolution kernels, social networks, string kernels, structured data},
	file = {Yanardag and Vishwanathan - 2015 - Deep Graph Kernels.pdf:/Users/spectator2ts/Zotero/storage/ENJBH68L/Yanardag and Vishwanathan - 2015 - Deep Graph Kernels.pdf:application/pdf},
}

@inproceedings{sub2vec,
	title = {{Sub2Vec}: {Feature} {Learning} for {Subgraphs}},
	shorttitle = {{Sub2Vec}},
	doi = {10.1007/978-3-319-93037-4_14},
	abstract = {Network embeddings have become very popular in learning effective feature representations of networks. Motivated by the recent successes of embeddings in natural language processing, researchers have tried to find network embeddings in order to exploit machine learning algorithms for mining tasks like node classification and edge prediction. However, most of the work focuses on distributed representations of nodes that are inherently ill-suited to tasks such as community detection which are intuitively dependent on subgraphs. Here, we formulate subgraph embedding problem based on two intuitive properties of subgraphs and propose Sub2Vec, an unsupervised algorithm to learn feature representations of arbitrary subgraphs. We also highlight the usability of Sub2Vec by leveraging it for network mining tasks, like community detection and graph classification. We show that Sub2Vec gets significant gains over state-of-the-art methods. In particular, Sub2Vec offers an approach to generate a richer vocabulary of meaningful features of subgraphs for representation and reasoning.},
	language = {en},
	booktitle = {KDD},
	author = {Adhikari, Bijaya and Zhang, Yao and Ramakrishnan, Naren and Prakash, B. Aditya},
	year = {2018},
	keywords = {Community Detection, Graph Classification, Learn Feature Representations, Network Embedding, Subgraph Embedding},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/4UH2GJS3/Adhikari et al. - 2018 - Sub2Vec Feature Learning for Subgraphs.pdf:application/pdf},
}

@techreport{TUDataset,
	title = {{TUDataset}: {A} collection of benchmark datasets for learning with graphs},
	shorttitle = {{TUDataset}},
	institution = {arXiv},
	author = {Morris, Christopher and Kriege, Nils M. and Bause, Franka and Kersting, Kristian and Mutzel, Petra and Neumann, Marion},
	year = {2020},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Neural and Evolutionary Computing},
	file = {arXiv Fulltext PDF:/Users/spectator2ts/Zotero/storage/5YCDDGPE/Morris et al. - 2020 - TUDataset A collection of benchmark datasets for .pdf:application/pdf;arXiv.org Snapshot:/Users/spectator2ts/Zotero/storage/R2KQ3PJ2/2007.html:text/html},
}

@article{RLReview,
	title = {Representation {Learning}: {A} {Review} and {New} {Perspectives}},
	shorttitle = {Representation {Learning}},
	abstract = {The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.},
	journal = {TPAMI},
	author = {Bengio, Yoshua and Courville, Aaron and Vincent, Pascal},
	year = {2013},
	keywords = {Abstracts, autoencoder, Boltzmann machine, Deep learning, Feature extraction, feature learning, Learning systems, Machine learning, Manifolds, neural nets, Neural networks, representation learning, Speech recognition, unsupervised learning},
	file = {IEEE Xplore Abstract Record:/Users/spectator2ts/Zotero/storage/QRKXP6ZL/6472238.html:text/html;IEEE Xplore Full Text PDF:/Users/spectator2ts/Zotero/storage/IKDDERRV/Bengio et al. - 2013 - Representation Learning A Review and New Perspect.pdf:application/pdf},
}

@inproceedings{node2vec,
	title = {node2vec: {Scalable} {Feature} {Learning} for {Networks}},
	booktitle = {KDD},
	author = {Grover, Aditya and Leskovec, Jure},
	year = {2016},
	keywords = {feature learning, graph representations, information networks, node embeddings},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/9QZVBGH7/Grover and Leskovec - 2016 - node2vec Scalable Feature Learning for Networks.pdf:application/pdf},
}

@inproceedings{OGB,
	title = {Open {Graph} {Benchmark}: {Datasets} for {Machine} {Learning} on {Graphs}},
	booktitle = {NeurIPS},
	author = {Hu, Weihua and Fey, Matthias and Zitnik, Marinka and Dong, Yuxiao and Ren, Hongyu and Liu, Bowen and Catasta, Michele and Leskovec, Jure},
	year = {2020},
	file = {Full Text PDF:/Users/spectator2ts/Zotero/storage/3FVI93YZ/Hu et al. - 2020 - Open Graph Benchmark Datasets for Machine Learnin.pdf:application/pdf},
}

@article{VIB,
	title = {Deep {Variational} {Information} {Bottleneck}},
	journal = {ICLR},
	author = {Alemi, Alexander A. and Fischer, Ian and Dillon, Joshua V. and Murphy, Kevin},
	year = {2017}
}

@article{graph2vec,
	title = {graph2vec: {Learning} {Distributed} {Representations} of {Graphs}},
	shorttitle = {graph2vec},
	abstract = {This work proposes a neural embedding framework named graph2vec to learn data-driven distributed representations of arbitrary sized graphs that achieves significant improvements in classification and clustering accuracies over substructure representation learning approaches and are competitive with state-of-the-art graph kernels. Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs. However, many graph analytics tasks such as graph classification and clustering require representing entire graphs as fixed length feature vectors. While the aforementioned approaches are naturally unequipped to learn such representations, graph kernels remain as the most effective way of obtaining them. However, these graph kernels use handcrafted features (e.g., shortest paths, graphlets, etc.) and hence are hampered by problems such as poor generalization. To address this limitation, in this work, we propose a neural embedding framework named graph2vec to learn data-driven distributed representations of arbitrary sized graphs. graph2vec's embeddings are learnt in an unsupervised manner and are task agnostic. Hence, they could be used for any downstream task such as graph classification, clustering and even seeding supervised representation learning approaches. Our experiments on several benchmark and large real-world datasets show that graph2vec achieves significant improvements in classification and clustering accuracies over substructure representation learning approaches and are competitive with state-of-the-art graph kernels.},
	journal = {ArXiv},
	author = {Narayanan, A. and Chandramohan, Mahinthan and Venkatesan, R. and Chen, Lihui and Liu, Yang and Jaiswal, Shantanu},
	year = {2017},
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