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Graph Neural Networks (GNNs) have made rapid developments in the recent years.
Learning how to propagate messages in graph neural networks
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Improving fairness in graph neural networks via mitigating sensitive attribute leakage
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Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
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The political blogosphere and the 2004 us election: divided they blog
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Calibrating noise to sensitivity in private data analysis
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Measuring user influence in twitter: The million follower fallacy
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2012
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Learning to discover social circles in ego networks
Leskovec, J., and Mcauley, J · 2012
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Image labeling on a network: using social-network metadata for image classification
McAuley, J., and Leskovec, J · 2012
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Learning to discover social circles in ego networks
McAuley, J. J., and Leskovec, J · 2012
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Too much, too little, or just right? ways explanations impact end users’ mental models
Kulesza, T., Stumpf, S., Burnett, M., Yang, S., Kwan, I., and Wong, W.-K · 2013
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The algorithmic foundations of differential privacy
Dwork, C., Roth, A., et al · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Differential privacy and machine learning: a survey and review
Ji, Z., Lipton, Z. C., and Elkan, C · 2014
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Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W · 2015
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Censoring representations with an adversary
Edwards, H., and Storkey, A · 2015
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The movielens datasets: History and context
Harper, F. M., and Konstan, J. A · 2015
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The effect of race/ethnicity on sentencing: Examining sentence type, jail length, and prison length
Jordan, K. L., and Freiburger, T. L · 2015
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Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
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On the efficiency of the information networks in social media
Babaei, M., Grabowicz, P., Valera, I., Gummadi, K. P., and Gomez-Rodriguez, M · 2016
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Dong, Y., Lizardo, O., and Chawla, N. V · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N., and Welling, M · 2016
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Variational graph auto-encoders
Kipf, T. N., and Welling, M · 2016
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Federated learning: Strategies for improving communication efficiency
Konečnỳ, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D · 2016
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Tri-party deep network representation
Pan, S., Wu, J., Zhu, X., Zhang, C., and Wang, Y · 2016
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Semi-supervised knowledge transfer for deep learning from private training data
Papernot, N., Abadi, M., Erlingsson, U., Goodfellow, I., and Talwar, K · 2016
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" why should i trust you?" explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Data decisions and theoretical implications when adversarially learning fair representations
Beutel, A., Chen, J., Zhao, Z., and Chi, E. H · 2017
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Supervised community detection with line graph neural networks
Chen, Z., Li, X., and Bruna, J · 2017
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Inductive representation learning on large graphs
Hamilton, W. L., Ying, R., and Leskovec, J · 2017
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Brainnetcnn: Convolutional neural networks for brain networks; towards predicting neurodevelopment
Kawahara, J., Brown, C. J., Miller, S. P., Booth, B. G., Chau, V., Grunau, R. E., Zwicker, J. G., and Hamarneh, G · 2017
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Kusner, M. J., Loftus, J. R., Russell, C., and Silva, R · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
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Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
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Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
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Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
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Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B · 2018
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On the robustness of interpretability methods
Alvarez-Melis, D., and Jaakkola, T. S · 2018
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Fastgcn: fast learning with graph convolutional networks via importance sampling
Chen, J., Ma, T., and Xiao, C · 2018
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Link prediction adversarial attack
Chen, J., Shi, Z., Wu, Y., Xu, X., and Zheng, H · 2018
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Fast gradient attack on network embedding
Chen, J., Wu, Y., Xu, X., Chen, Y., Zheng, H., and Xuan, Q · 2018
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Adversarial attack on graph structured data
Dai, H., Li, H., Tian, T., Huang, X., Wang, L., Zhu, J., and Song, L · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function
Garcia, J. O., Ashourvan, A., Muldoon, S., Vettel, J. M., and Bassett, D. S · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
Klicpera, J., Bojchevski, A., and Günnemann, S · 2018
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Visualizing the loss landscape of neural nets
Li, H., Xu, Z., Taylor, G., Studer, C., and Goldstein, T · 2018
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Learning adversarially fair and transferable representations
Madras, D., Creager, E., Pitassi, T., and Zemel, R · 2018
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Membership inference attack against differentially private deep learning model
Rahman, M. A., Rahman, T., Laganière, R., Mohammed, N., and Wang, Y · 2018
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Perturbation-based explanations of prediction models
Robnik-Šikonja, M., and Bohanec, M · 2018
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Modeling relational data with graph convolutional networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., Berg, R. v. d., Titov, I., and Welling, M · 2018
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Pitfalls of graph neural network evaluation
Shchur, O., Mumme, M., Bojchevski, A., and Günnemann, S · 2018
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Algorithmic glass ceiling in social networks: The effects of social recommendations on network diversity
Stoica, A.-A., Riederer, C., and Chaintreau, A · 2018
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Adversarial attack and defense on graph data: A survey
Sun, L., Wang, J., Yu, P. S., and Li, B · 2018
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Dkn: Deep knowledge-aware network for news recommendation
Wang, H., Zhang, F., Xie, X., and Guo, M · 2018
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Moleculenet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., and Pande, V · 2018
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Looking deeper into deep learning model: Attribution-based explanations of textcnn
Xiong, W., Ni’mah, I., Huesca, J. M., van Ipenburg, W., Veldsink, J., and Pechenizkiy, M · 2018
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Dpne: Differentially private network embedding
Xu, D., Yuan, S., Wu, X., and Phan, H · 2018
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Graph convolutional neural networks for web-scale recommender systems
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J · 2018
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Visual interpretability for deep learning: a survey
Zhang, Q.-s., and Zhu, S.-C · 2018
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Adversarial attacks on neural networks for graph data
Zügner, D., Akbarnejad, A., and Günnemann, S · 2018
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Privacy-preserving machine learning: Threats and solutions
Al-Rubaie, M., and Chang, J. M · 2019
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Local differential privacy for deep learning
Arachchige, P. C. M., Bertok, P., Khalil, I., Liu, D., Camtepe, S., and Atiquzzaman, M · 2019
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Explainability techniques for graph convolutional networks
Baldassarre, F., and Azizpour, H · 2019
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Adversarial attacks on node embeddings via graph poisoning
Bojchevski, A., and Günnemann, S · 2019
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Certifiable robustness to graph perturbations
Bojchevski, A., and Günnemann, S · 2019
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Compositional fairness constraints for graph embeddings
Bose, A., and Hamilton, W · 2019
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Can adversarial network attack be defended?
Chen, J., Wu, Y., Lin, X., and Xuan, Q · 2019
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Risk assessment for networked-guarantee loans using high-order graph attention representation
Cheng, D., Tu, Y., Ma, Z.-W., Niu, Z., and Zhang, L · 2019
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Adversarial training methods for network embedding
Dai, Q., Shen, X., Zhang, L., Li, Q., and Wang, D · 2019
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Batch virtual adversarial training for graph convolutional networks
Deng, Z., Dong, Y., and Zhu, J · 2019
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Graph neural networks for social recommendation
Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., and Yin, D · 2019
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Graph adversarial training: Dynamically regularizing based on graph structure
Feng, F., He, X., Tang, J., and Chua, T.-S · 2019
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Certified data removal from machine learning models
Guo, C., Goldstein, T., Hannun, A., and Van Der Maaten, L · 2019
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Strategies for pre-training graph neural networks
Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J · 2019
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Power up! robust graph convolutional network against evasion attacks based on graph powering
Jin, M., Chang, H., Zhu, W., and Sojoudi, S · 2019
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Advances and open problems in federated learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al · 2019
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Learning generative adversarial representations (gap) under fairness and censoring constraints
Liao, J., Huang, C., Kairouz, P., and Sankar, L · 2019
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On the fairness of disentangled representations
Locatello, F., Abbati, G., Rainforth, T., Bauer, S., Schölkopf, B., and Bachem, O · 2019
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Auto-encoder based graph convolutional networks for online financial anti-fraud
Lv, L., Cheng, J., Peng, N., Fan, M., Zhao, D., and Zhang, J · 2019
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Explanation in artificial intelligence: Insights from the social sciences
Miller, T · 2019
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Explaining explanations in ai
Mittelstadt, B., Russell, C., and Wachter, S · 2019
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Definitions, methods, and applications in interpretable machine learning
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., and Yu, B · 2019
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Social data: Biases, methodological pitfalls, and ethical boundaries
Olteanu, A., Castillo, C., Diaz, F., and Kıcıman, E · 2019
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Monet: Debiasing graph embeddings via the metadata-orthogonal training unit
Palowitch, J., and Perozzi, B · 2019
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Explainability methods for graph convolutional neural networks
Pope, P. E., Kolouri, S., Rostami, M., Martin, C. E., and Hoffmann, H · 2019
When comparing to ground truth is wrong: On evaluating gnn explanation methods
Faber, L., K. Moghaddam, A., and Wattenhofer, R · 2021
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Jointly attacking graph neural network and its explanations
Fan, W., Jin, W., Liu, X., Xu, H., Tang, X., Wang, S., Li, Q., Tang, J., Wang, J., and Aggarwal, C · 2021
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Hard masking for explaining graph neural networks, 2021
Funke, T., Khosla, M., and Anand, A · 2021
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Zorro: Valid, sparse, and stable explanations in graph neural networks
Funke, T., Khosla, M., and Anand, A · 2021
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Gnes: Learning to explain graph neural networks
Gao, Y., Sun, T., Bhatt, R., Yu, D., Hong, S., and Zhao, L · 2021
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Layerwise relevance visualization in convolutional text graph classifiers
Schwarzenberg, R., Hübner, M., Harbecke, D., Alt, C., and Hennig, L · 2019
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Overlapping community detection with graph neural networks
Shchur, O., and Günnemann, S · 2019
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Fairness in algorithmic decision-making: Applications in multi-winner voting, machine learning, and recommender systems
Shrestha, Y. R., and Yang, Y · 2019
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Ethics guidelines for trustworthy ai
Smuha, N · 2019
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Deep representation learning for social network analysis
Tan, Q., Liu, N., and Hu, X · 2019
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Learning robust representations with graph denoising policy network
Wang, L., Yu, W., Wang, W., Cheng, W., Zhang, W., Zha, H., He, X., and Chen, H · 2019
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Robustness of graph neural networks at scale
Geisler, S., Schmidt, T., Şirin, H., Zügner, D., Bojchevski, A., and Günnemann, S · 2021
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Spreadgnn: Serverless multi-task federated learning for graph neural networks
He, C., Ceyani, E., Balasubramanian, K., Annavaram, M., and Avestimehr, S · 2021
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Node-level membership inference attacks against graph neural networks
He, X., Wen, R., Wu, Y., Backes, M., Shen, Y., and Zhang, Y · 2021
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Could graph neural networks learn better molecular representation for drug discovery? a comparison study of descriptor-based and graph-based models
Jiang, D., Wu, Z., Hsieh, C.-Y., Chen, G., Liao, B., Wang, Z., Shen, C., Cao, D., Wu, J., and Hou, T · 2021
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Node similarity preserving graph convolutional networks
Jin, W., Derr, T., Wang, Y., Ma, Y., Liu, Z., and Tang, J · 2021
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Crosswalk: Fairness-enhanced node representation learning
Khajehnejad, A., Khajehnejad, M., Babaei, M., Gummadi, K. P., Weller, A., and Mirzasoleiman, B · 2021
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Fairness-aware node representation learning
Köse, Ö. D., and Shen, Y · 2021
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Adversarial attack on large scale graph
Li, J., Xie, T., Liang, C., Xie, F., He, X., and Zheng, Z · 2021
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Braingnn: Interpretable brain graph neural network for fmri analysis
Li, X., Zhou, Y., Dvornek, N., Zhang, M., Gao, S., Zhuang, J., Scheinost, D., Staib, L. H., Ventola, P., and Duncan, J. S · 2021
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Unified robust training for graph neural networks against label noise
Li, Y., Yin, J., and Chen, L · 2021
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Credit risk and limits forecasting in e-commerce consumer lending service via multi-view-aware mixture-of-experts nets
Liang, T., Zeng, G., Zhong, Q., Chi, J., Feng, J., Ao, X., and Tang, J · 2021
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Information obfuscation of graph neural networks
Liao, P., Zhao, H., Xu, K., Jaakkola, T., Gordon, G. J., Jegelka, S., and Salakhutdinov, R · 2021
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Generative causal explanations for graph neural networks
Lin, W., Lan, H., and Li, B · 2021
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Trustworthy ai: A computational perspective
Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., Liu, Y., Jain, A. K., and Tang, J · 2021
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Federated social recommendation with graph neural network
Liu, Z., Yang, L., Fan, Z., Peng, H., and Yu, P. S · 2021
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Learning to drop: Robust graph neural network via topological denoising
Luo, D., Cheng, W., Yu, W., Zong, B., Ni, J., Chen, H., and Zhang, X · 2021
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Graph adversarial attack via rewiring
Ma, Y., Wang, S., Derr, T., Wu, L., and Tang, J · 2021
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A survey on bias and fairness in machine learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A · 2021
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Releasing graph neural networks with differential privacy guarantees
Olatunji, I. E., Funke, T., and Khosla, M · 2021
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Membership inference attack on graph neural networks
Olatunji, I. E., Nejdl, W., and Khosla, M · 2021
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Decentralized federated graph neural networks
Pei, Y., Mao, R., Liu, Y., Chen, C., Xu, S., Qiang, F., and Tech, B. E · 2021
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Graph neural networks for friend ranking in large-scale social platforms
Sankar, A., Liu, Y., Yu, J., and Shah, N · 2021
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Model stealing attacks against inductive graph neural networks
Shen, Y., He, X., Han, Y., and Zhang, Y · 2021
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Biased edge dropout for enhancing fairness in graph representation learning
Spinelli, I., Scardapane, S., Hussain, A., and Uncini, A · 2021
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Single node injection attack against graph neural networks
Tao, S., Cao, Q., Shen, H., Huang, J., Wu, Y., and Cheng, X · 2021
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Poisoning attacks on fair machine learning
Van, M.-H., Du, W., Wu, X., and Lu, A · 2021
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Privacy-preserving representation learning on graphs: A mutual information perspective
Wang, B., Guo, J., Li, A., Chen, Y., and Li, H · 2021
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Certified robustness of graph neural networks against adversarial structural perturbation
Wang, B., Jia, J., Cao, X., and Gong, N. Z · 2021
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A review on graph neural network methods in financial applications
Wang, J., Zhang, S., Xiao, Y., and Song, R · 2021
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Unbiased graph embedding with biased graph observations
Wang, N., Lin, L., Li, J., and Wang, H · 2021
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Unsupervised learning for community detection in attributed networks based on graph convolutional network
Wang, X., Li, J., Yang, L., and Mi, H · 2021
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Towards multi-grained explainability for graph neural networks
Wang, X., Wu, Y., Zhang, A., He, X., and Chua, T.-S · 2021
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Adapting membership inference attacks to gnn for graph classification: Approaches and implications
Wu, B., Yang, X., Pan, S., and Yuan, X · 2021
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Fedgnn: Federated graph neural network for privacy-preserving recommendation
Wu, C., Wu, F., Cao, Y., Huang, Y., and Xie, X · 2021
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Graph backdoor
Xi, Z., Pang, R., Ji, S., and Wang, T · 2021
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Federated graph classification over non-iid graphs
Xie, H., Ma, J., Xiong, L., and Yang, C · 2021
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Towards consumer loan fraud detection: Graph neural networks with role-constrained conditional random field
Xu, B., Shen, H., Sun, B., An, R., Cao, Q., and Cheng, X · 2021
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On explainability of graph neural networks via subgraph explorations
Yuan, H., Yu, H., Wang, J., Li, K., and Ji, S · 2021
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Fair representation learning for heterogeneous information networks
Zeng, Z., Islam, R., Keya, K. N., Foulds, J., Song, Y., and Pan, S · 2021
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Graph embedding for recommendation against attribute inference attacks
Zhang, S., Yin, H., Chen, T., Huang, Z., Cui, L., and Zhang, X · 2021
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Relex: A model-agnostic relational model explainer
Zhang, Y., Defazio, D., and Ramesh, A · 2021
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Inference attacks against graph neural networks
Zhang, Z., Chen, M., Backes, M., Shen, Y., and Zhang, Y · 2021
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Backdoor attacks to graph neural networks
Zhang, Z., Jia, J., Wang, B., and Gong, N. Z · 2021
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Graphmi: Extracting private graph data from graph neural networks
Zhang, Z., Liu, Q., Huang, Z., Wang, H., Lu, C., Liu, C., and Chen, E · 2021
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Protgnn: Towards self-explaining graph neural networks, 2021
Zhang, Z., Liu, Q., Wang, H., Lu, C., and Lee, C · 2021
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Zhao, T., Dai, E., Shu, K., and Wang, S · 2021
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Graphsmote: Imbalanced node classification on graphs with graph neural networks
Zhao, T., Zhang, X., and Wang, S · 2021
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Watermarking graph neural networks by random graphs
Zhao, X., Wu, H., and Zhang, X · 2021
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Asfgnn: Automated separated-federated graph neural network
Zheng, L., Zhou, J., Chen, C., Wu, B., Wang, L., and Zhang, B · 2021
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Deep graph structure learning for robust representations: A survey
Zhu, Y., Xu, W., Zhang, J., Liu, Q., Wu, S., and Wang, L · 2021
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Tdgia: Effective injection attacks on graph neural networks
Zou, X., Zheng, Q., Dong, Y., Guan, X., Kharlamov, E., Lu, J., and Tang, J · 2021
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Graph unlearning
Chen, M., Zhang, Z., Wang, T., Backes, M., Humbert, M., and Zhang, Y · 2022
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Understanding and improving graph injection attack by promoting unnoticeability
Chen, Y., Yang, H., Zhang, Y., Ma, K., Liu, T., Han, B., and Cheng, J · 2022
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Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
Chen, Y., Zhang, Y., Bian, Y., Yang, H., Kaili, M., Xie, B., Liu, T., Han, B., and Cheng, J · 2022
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Efficient model updates for approximate unlearning of graph-structured data
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Towards robust graph neural networks for noisy graphs with sparse labels
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On structural explanation of bias in graph neural networks
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Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure
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Adversarial graph contrastive learning with information regularization
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