Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) are widely adopted to analyse non-Euclidean data, such as chemical networks, brain networks, and social networks, modelling complex relationships and interdependency between objects.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
P. D. Dobson and A. J. Doig, “Distinguishing enzyme structures from non-enzymes without alignments,” Journal of molecular biology , 2003
2003
Earlier work this paper cites.
A. Krizhevsky, “Learning multiple layers of features from tiny images,” University of Toronto , 05 2012
2012
Earlier work this paper cites.
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC superpixels compared to state-of-the-art superpixel methods,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 34, no. 11, pp. 2274–2282, 2012
2012
Earlier work this paper cites.
J. Huang, Y. Xie, F. Yu, Q. Ke, M. Abadi, E. Gillum, and Z. M. Mao, “Socialwatch: detection of online service abuse via large-scale social graphs,” in Proc. ACM AsiaCCS , 2013
2013
Earlier work this paper cites.
D. Kong and G. Yan, “Discriminant malware distance learning on structural information for automated malware classification,” in Proc. ACM KDD , 2013
2013
Earlier work this paper cites.
N. Z. Gong, M. Frank, and P. Mittal, “Sybilbelief: A semi-supervised learning approach for structure-based sybil detection,” IEEE Trans. Inf. Forensics Secur. , vol. 9, no. 6, pp. 976–987, 2014
2014
Earlier work this paper cites.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in Proc. IEEE S&P , 2017
2017
Earlier work this paper cites.
S. D. Nikolopoulos and I. Polenakis, “A graph-based model for malware detection and classification using system-call groups,” J. Comput. Virol. Hacking Tech. , vol. 13, no. 1, pp. 29–46, 2017
2017
Earlier work this paper cites.
M. Hassen and P. K. Chan, “Scalable function call graph-based malware classification,” in Proc. ACM CODASPY , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proc. ICLR . OpenReview.net, 2017
2017
Earlier work this paper cites.
X. Bresson and T. Laurent, “Residual gated graph convnets,” CoRR , vol. abs/1711.07553, 2017
2017
Earlier work this paper cites.
W. L. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Proc. NIPS , 2017
2017
Earlier work this paper cites.
N. Z. Gong and B. Liu, “Attribute inference attacks in online social networks,” ACM Trans. Priv. Secur. , vol. 21, no. 1, pp. 3:1–3:30, 2018
2018
Cited alongside, same era.
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha, “Privacy risk in machine learning: Analyzing the connection to overfitting,” in Proc. IEEE CSF , 2018
2018
Cited alongside, same era.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in Proc. ICLR , 2018
2018
Cited alongside, same era.
W. Wang, H. Yin, X. Du, W. Hua, Y. Li, and Q. V. H. Nguyen, “Online user representation learning across heterogeneous social networks,” in Proc. ACM SIGIR , 2019
2019
Cited alongside, same era.
S. Truex, L. Liu, M. Gursoy, L. Yu, and W. Wei, “Demystifying membership inference attacks in machine learning as a service,” IEEE transactions on services computing , pp. 1–1, 2019
H. Xu, C. Huang, Y. Xu, L. Xia, H. Xing, and D. Yin, “Global context enhanced social recommendation with hierarchical graph neural networks,” in Proc. IEEE ICDM , 2020
2020
Later among the works it cites.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” IEEE transactions on neural networks and learning systems , vol. 32, no. 1, pp. 4–24, 2020
2020
Later among the works it cites.
D. Chen, N. Yu, Y. Zhang, and M. Fritz, “Gan-leaks: A taxonomy of membership inference attacks against generative models,” in Proc. ACM CCS , 2020
2020
Later among the works it cites.
Y. Zhu, X. Luo, Y. Li, B. Bu, K. Zhou, W. Zhang, and M. Lu, “Heterogeneous mini-graph neural network and its application to fraud invitation detection,” in Proc. IEEE ICDM , 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
A. Salem, Y. Zhang, M. Humbert, P. Berrang, M. Fritz, and M. Backes, “Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models,” in Proc. NDSS , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
B. Wang, J. Jia, and N. Z. Gong, “Graph-based security and privacy analytics via collective classification with joint weight learning and propagation,” in Proc. NDSS . The Internet Society, 2019
2019
Cited alongside, same era.
J. Yan, G. Yan, and D. Jin, “Classifying malware represented as control flow graphs using deep graph convolutional neural network,” in Proc. IEEE DSN , 2019
2019
Cited alongside, same era.
J. Jia, A. Salem, M. Backes, Y. Zhang, and N. Z. Gong, “Memguard: Defending against black-box membership inference attacks via adversarial examples,” in Proc. ACM CCS , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
D. Szklarczyk, A. L. Gable, D. Lyon, A. Junge, S. Wyder, J. Huerta-Cepas, M. Simonovic, N. T. Doncheva, J. H. Morris, P. Bork, L. J. Jensen, and C. von Mering, “STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets,” Nucleic Acids Res. , vol. 47, no. Database-Issue, pp. D607–D613, 2019
2019
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
K. Leino and M. Fredrikson, “Stolen memories: Leveraging model memorization for calibrated white-box membership inference,” in Proc. USENIX Security , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2021
Closest in time.
X. He, J. Jia, M. Backes, N. Z. Gong, and Y. Zhang, “Stealing links from graph neural networks,” in Proc. USENIX Security , 2021
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
Z. Zhang, M. Chen, M. Backes, Y. Shen, and Y. Zhang, “Inference attacks against graph neural networks,” in Proc. USENIX Security , 2022
2022
Closest in time.