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ORCIDJohn E. Hopcroft
Person information
- award (1986): Turing Award
- affiliation: Cornell University, Ithaca, NY, USA
- award (2010): IEEE John von Neumann Medal
- award (2005): Harry H. Goode Memorial Award
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2020 – today
- 2024
[i39]Jinsong Chen, Hanpeng Liu, John E. Hopcroft, Kun He:
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers. CoRR abs/2406.19258 (2024)- 2023
- [j70]Meng Wang
, Boyu Li, Kun He, John E. Hopcroft:
Uncovering the Local Hidden Community Structure in Social Networks. ACM Trans. Knowl. Discov. Data 17(5): 67:1-67:25 (2023) - [c79]Yu-Zhe Shi, Manjie Xu, John E. Hopcroft, Kun He, Joshua B. Tenenbaum, Song-Chun Zhu, Ying Nian Wu, Wenjuan Han, Yixin Zhu:
On the Complexity of Bayesian Generalization. ICML 2023: 31389-31407 - [i38]Kun He, Xin Liu, Yichen Yang, Zhou Qin, Weigao Wen, Hui Xue, John E. Hopcroft:
PIAT: Parameter Interpolation based Adversarial Training for Image Classification. CoRR abs/2303.13955 (2023) - [i37]Jinsong Chen, Gaichao Li, John E. Hopcroft, Kun He:
SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning. CoRR abs/2310.11025 (2023) - [i36]Gaichao Li, Jinsong Chen, John E. Hopcroft, Kun He:
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning. CoRR abs/2310.20250 (2023) - 2022
- [j69]Boyu Li, Meng Wang, John E. Hopcroft, Kun He:
HoSIM: Higher-order Structural Importance based method for multiple local community detection. Knowl. Based Syst. 256: 109853 (2022) - [c78]Yifeng Xiong, Jiadong Lin, Min Zhang, John E. Hopcroft, Kun He:
Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability. CVPR 2022: 14963-14972 - [c77]Binghui Li, Jikai Jin, Han Zhong, John E. Hopcroft, Liwei Wang:
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power. NeurIPS 2022 - [i35]Boyu Li, Meng Wang, John E. Hopcroft, Kun He:
HoSIM: Higher-order Structural Importance based Method for Multiple Local Community Detection. CoRR abs/2205.11812 (2022) - [i34]Binghui Li, Jikai Jin, Han Zhong, John E. Hopcroft, Liwei Wang:
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power. CoRR abs/2205.13863 (2022) - [i33]Kun He, Chang Liu, Stephen Lin, John E. Hopcroft:
Local Magnification for Data and Feature Augmentation. CoRR abs/2211.07859 (2022) - [i32]Yu-Zhe Shi, Manjie Xu, John E. Hopcroft, Kun He, Joshua B. Tenenbaum, Song-Chun Zhu, Ying Nian Wu, Wenjuan Han, Yixin Zhu:
On the Complexity of Bayesian Generalization. CoRR abs/2211.11033 (2022) - [i31]Shuoxi Zhang, Hanpeng Liu, John E. Hopcroft, Kun He:
Class-aware Information for Logit-based Knowledge Distillation. CoRR abs/2211.14773 (2022) - 2021
- [c76]Xuecheng Liu, Luoyi Fu, Xinbing Wang, John E. Hopcroft:
ProHiCo: A Probabilistic Framework to Hide Communities in Large Networks. INFOCOM 2021: 1-10 - [i30]Kun He, Chao Li, Yixiao Yang, Gao Huang, John E. Hopcroft:
Integrating Circle Kernels into Convolutional Neural Networks. CoRR abs/2107.02451 (2021) - [i29]Xiaodong Xin, Kun He, Jialu Bao, Bart Selman, John E. Hopcroft:
Structure Amplification on Multi-layer Stochastic Block Models. CoRR abs/2108.00127 (2021) - [i28]Yifeng Xiong, Jiadong Lin, Min Zhang, John E. Hopcroft, Kun He:
Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability. CoRR abs/2111.10752 (2021) - [i27]Meng Wang, Boyu Li, Kun He, John E. Hopcroft:
Uncovering the Local Hidden Community Structure in Social Networks. CoRR abs/2112.04100 (2021) - 2020
- [c75]Chao Li, Yixiao Yang, Kun He, Stephen Lin, John E. Hopcroft:
Single Image Reflection Removal Through Cascaded Refinement. CVPR 2020: 3562-3571 - [c74]Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, John E. Hopcroft:
Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks. ICLR 2020 - [c73]Chuanbiao Song, Kun He, Jiadong Lin, Liwei Wang, John E. Hopcroft:
Robust Local Features for Improving the Generalization of Adversarial Training. ICLR 2020 - [c72]Jialu Bao, Kun He, Xiaodong Xin, Bart Selman, John E. Hopcroft:
Hidden Community Detection on Two-Layer Stochastic Models: A Theoretical Perspective. TAMC 2020: 365-376 - [i26]Jialu Bao, Kun He, Xiaodong Xin, Bart Selman, John E. Hopcroft:
Hidden Community Detection on Two-layer Stochastic Models: a Theoretical Prospective. CoRR abs/2001.05919 (2020)
2010 – 2019
- 2019
- [j68]Carla P. Gomes, Thomas G. Dietterich, Christopher Barrett, Jon Conrad, Bistra Dilkina, Stefano Ermon, Fei Fang, Andrew Farnsworth, Alan Fern, Xiaoli Z. Fern, Daniel Fink, Douglas H. Fisher, Alexander Flecker, Daniel Freund, Angela Fuller, John M. Gregoire, John E. Hopcroft, Steve Kelling, J. Zico Kolter, Warren B. Powell, Nicole D. Sintov, John S. Selker, Bart Selman, Daniel Sheldon, David B. Shmoys, Milind Tambe, Weng-Keen Wong, Christopher Wood, Xiaojian Wu, Yexiang Xue, Amulya Yadav, Abdul-Aziz Yakubu, Mary Lou Zeeman:
Computational sustainability: computing for a better world and a sustainable future. Commun. ACM 62(9): 56-65 (2019) - [j67]Pan Shi, Kun He, David Bindel, John E. Hopcroft:
Locally-biased spectral approximation for community detection. Knowl. Based Syst. 164: 459-472 (2019) - [j66]Kun He, Pan Shi, David Bindel, John E. Hopcroft:
Krylov Subspace Approximation for Local Community Detection in Large Networks. ACM Trans. Knowl. Discov. Data 13(5): 52:1-52:30 (2019) - [j65]Kun He, Wu Wang, Xiaosen Wang, John E. Hopcroft:
A new anchor word selection method for the separable topic discovery. WIREs Data Mining Knowl. Discov. 9(5) (2019) - [c71]Zengjian Chen, Jiayi Liu, Yihe Deng, Kun He, John E. Hopcroft:
Adaptive Wavelet Clustering for Highly Noisy Data. ICDE 2019: 328-337 - [c70]Chuanbiao Song, Kun He, Liwei Wang, John E. Hopcroft:
Improving the Generalization of Adversarial Training with Domain Adaptation. ICLR (Poster) 2019 - [i25]Xiaosen Wang, Kun He, John E. Hopcroft:
AT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets. CoRR abs/1904.07793 (2019) - [i24]Kun He, Wu Wang, Xiaosen Wang, John E. Hopcroft:
A New Anchor Word Selection Method for the Separable Topic Discovery. CoRR abs/1905.06109 (2019) - [i23]Runtian Zhai, Tianle Cai, Di He, Chen Dan, Kun He, John E. Hopcroft, Liwei Wang:
Adversarially Robust Generalization Just Requires More Unlabeled Data. CoRR abs/1906.00555 (2019) - [i22]Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, John E. Hopcroft:
Nesterov Accelerated Gradient and Scale Invariance for Improving Transferability of Adversarial Examples. CoRR abs/1908.06281 (2019) - [i21]Chuanbiao Song, Kun He, Jiadong Lin, Liwei Wang, John E. Hopcroft:
Robust Local Features for Improving the Generalization of Adversarial Training. CoRR abs/1909.10147 (2019) - [i20]Chao Li, Yixiao Yang, Kun He, Stephen Lin, John E. Hopcroft:
Single Image Reflection Removal through Cascaded Refinement. CoRR abs/1911.06634 (2019) - 2018
- [j64]Kun He, Yingru Li, Sucheta Soundarajan, John E. Hopcroft:
Hidden community detection in social networks. Inf. Sci. 425: 92-106 (2018) - [j63]Yuzhe Ma, Kun He, John E. Hopcroft, Pan Shi:
Neighbourhood-preserving dimension reduction via localised multidimensional scaling. Theor. Comput. Sci. 734: 58-71 (2018) - [j62]Yixuan Li, Kun He, Kyle Kloster, David Bindel, John E. Hopcroft:
Local Spectral Clustering for Overlapping Community Detection. ACM Trans. Knowl. Discov. Data 12(2): 17:1-17:27 (2018) - [c69]Tao Yu, Huan Long, John E. Hopcroft:
Curvature-based Comparison of Two Neural Networks. ICPR 2018: 441-447 - [c68]Liwei Wang, Lunjia Hu, Jiayuan Gu, Zhiqiang Hu, Yue Wu, Kun He, John E. Hopcroft:
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation. NeurIPS 2018: 9607-9616 - [i19]Tao Yu, Huan Long, John E. Hopcroft:
Curvature-based Comparison of Two Neural Networks. CoRR abs/1801.06801 (2018) - [i18]Chuanbiao Song, Kun He, Liwei Wang, John E. Hopcroft:
Improving the Generalization of Adversarial Training with Domain Adaptation. CoRR abs/1810.00740 (2018) - [i17]Liwei Wang, Lunjia Hu, Jiayuan Gu, Yue Wu, Zhiqiang Hu, Kun He, John E. Hopcroft:
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation. CoRR abs/1810.11750 (2018) - [i16]Zengjian Chen, Jiayi Liu, Yihe Deng, Kun He, John E. Hopcroft:
Adaptive Wavelet Clustering for Highly Noisy Data. CoRR abs/1811.10786 (2018) - 2017
- [j61]John E. Hopcroft, Kun He:
Computer Science in the Information Age. IEEE Intell. Informatics Bull. 18(2): 3-6 (2017) - [c67]Xun Huang, Yixuan Li, Omid Poursaeed, John E. Hopcroft, Serge J. Belongie:
Stacked Generative Adversarial Networks. CVPR 2017: 1866-1875 - [c66]Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger:
Snapshot Ensembles: Train 1, Get M for Free. ICLR (Poster) 2017 - [c65]Wu Wang, Houquan Zhou, Kun He, John E. Hopcroft:
Learning Latent Topics from the Word Co-occurrence Network. NCTCS 2017: 18-30 - [c64]Jian Hu, Kun He, John E. Hopcroft, Yaren Zhang:
Deep Compression on Convolutional Neural Network for Artistic Style Transfer. NCTCS 2017: 157-166 - [c63]Pan Shi, Kun He, David Bindel, John E. Hopcroft:
Local Lanczos Spectral Approximation for Community Detection. ECML/PKDD (1) 2017: 651-667 - [i15]Kun He, Yingru Li, Sucheta Soundarajan, John E. Hopcroft:
Hidden Community Detection in Social Networks. CoRR abs/1702.07462 (2017) - [i14]Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger:
Snapshot Ensembles: Train 1, get M for free. CoRR abs/1704.00109 (2017) - [i13]Yao Shu, Man Zhu, Kun He, John E. Hopcroft, Pan Zhou:
Understanding Deep Representations through Random Weights. CoRR abs/1704.00330 (2017) - [i12]Mengxiao Zhang, Wangquan Wu, Yanren Zhang, Kun He, Tao Yu, Huan Long, John E. Hopcroft:
The Local Dimension of Deep Manifold. CoRR abs/1711.01573 (2017) - [i11]Kun He, Pan Shi, David Bindel, John E. Hopcroft:
Krylov Subspace Approximation for Local Community Detection. CoRR abs/1712.04823 (2017) - 2016
- [j60]Jianer Chen, John E. Hopcroft:
Special Issue for FAW 2014. J. Comb. Optim. 32(1): 1-2 (2016) - [c62]Yuzhe Ma, Kun He, John E. Hopcroft, Pan Shi:
Nonlinear Dimension Reduction by Local Multidimensional Scaling. FAW 2016: 158-171 - [c61]Kun He, Yan Wang, John E. Hopcroft:
A Powerful Generative Model Using Random Weights for the Deep Image Representation. NIPS 2016: 631-639 - [c60]Yixuan Li, Oscar Martinez, Xing Chen, Yi Li, John E. Hopcroft:
In a World That Counts: Clustering and Detecting Fake Social Engagement at Scale. WWW 2016: 111-120 - [c59]Jiezhong Qiu, Yixuan Li, Jie Tang, Zheng Lu, Hao Ye, Bo Chen, Qiang Yang, John E. Hopcroft:
The Lifecycle and Cascade of WeChat Social Messaging Groups. WWW 2016: 311-320 - [c58]Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, John E. Hopcroft:
Convergent Learning: Do different neural networks learn the same representations? ICLR 2016 - [i10]Kun He, Yan Wang, John E. Hopcroft:
A Powerful Generative Model Using Random Weights for the Deep Image Representation. CoRR abs/1606.04801 (2016) - [i9]Xun Huang, Yixuan Li, Omid Poursaeed, John E. Hopcroft, Serge J. Belongie:
Stacked Generative Adversarial Networks. CoRR abs/1612.04357 (2016) - 2015
- [j59]Jianer Chen, John E. Hopcroft:
Frontiers of Algorithmics. Theor. Comput. Sci. 607: 125 (2015) - [j58]Sucheta Soundarajan, John E. Hopcroft:
Use of Local Group Information to Identify Communities in Networks. ACM Trans. Knowl. Discov. Data 9(3): 21:1-21:27 (2015) - [c57]Kun He, Yiwei Sun, David Bindel, John E. Hopcroft, Yixuan Li:
Detecting Overlapping Communities from Local Spectral Subspaces. ICDM 2015: 769-774 - [c56]Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, John E. Hopcroft:
Convergent Learning: Do different neural networks learn the same representations? FE@NIPS 2015: 196-212 - [c55]Yixuan Li, Kun He, David Bindel, John E. Hopcroft:
Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach. WWW 2015: 658-668 - [i8]Kun He, Sucheta Soundarajan, Xuezhi Cao, John E. Hopcroft, Menglong Huang:
Revealing Multiple Layers of Hidden Community Structure in Networks. CoRR abs/1501.05700 (2015) - [i7]Yixuan Li, Kun He, David Bindel, John E. Hopcroft:
Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach. CoRR abs/1509.07715 (2015) - [i6]Yixuan Li, Kun He, David Bindel, John E. Hopcroft:
Overlapping Community Detection via Local Spectral Clustering. CoRR abs/1509.07996 (2015) - [i5]Kun He, Yiwei Sun, David Bindel, John E. Hopcroft, Yixuan Li:
Detecting Overlapping Communities from Local Spectral Subspaces. CoRR abs/1509.08065 (2015) - [i4]Jacob R. Gardner, Matt J. Kusner, Yixuan Li, Paul Upchurch, Kilian Q. Weinberger, John E. Hopcroft:
Deep Manifold Traversal: Changing Labels with Convolutional Features. CoRR abs/1511.06421 (2015) - [i3]Yixuan Li, Oscar Martinez, Xing Chen, Yi Li, John E. Hopcroft:
In a World that Counts: Clustering and Detecting Fake Social Engagement at Scale. CoRR abs/1512.05457 (2015) - [i2]Jiezhong Qiu, Yixuan Li, Jie Tang, Zheng Lu, Hao Ye, Bo Chen, Qiang Yang, John E. Hopcroft:
The Lifecycle and Cascade of Social Messaging Groups. CoRR abs/1512.07831 (2015) - 2014
- [j57]Bruno D. Abrahao, Sucheta Soundarajan, John E. Hopcroft, Robert D. Kleinberg:
A separability framework for analyzing community structure. ACM Trans. Knowl. Discov. Data 8(1): 5:1-5:29 (2014) - [e3]Jianer Chen, John E. Hopcroft, Jianxin Wang:
Frontiers in Algorithmics - 8th International Workshop, FAW 2014, Zhangjiajie, China, June 28-30, 2014. Proceedings. Lecture Notes in Computer Science 8497, Springer 2014, ISBN 978-3-319-08015-4 [contents] - 2013
- [j56]Liaoruo Wang, John E. Hopcroft, Jing He, Hongyu Liang, Supasorn Suwajanakorn:
Extracting the Core Structure of Social Networks Using (α, β)-Communities. Internet Math. 9(1): 58-81 (2013) - [j55]Tiancheng Lou, Jie Tang, John E. Hopcroft, Zhanpeng Fang, Xiaowen Ding:
Learning to predict reciprocity and triadic closure in social networks. ACM Trans. Knowl. Discov. Data 7(2): 5:1-5:25 (2013) - [c54]Ping Li, Gennady Samorodnitsky, John E. Hopcroft:
Sign Cauchy Projections and Chi-Square Kernel. NIPS 2013: 2571-2579 - [i1]Ping Li, Gennady Samorodnitsky, John E. Hopcroft:
Sign Stable Projections, Sign Cauchy Projections and Chi-Square Kernels. CoRR abs/1308.1009 (2013) - 2012
- [c53]Vint Cerf, John E. Hopcroft, Robert E. Kahn, Ronald L. Rivest, Adi Shamir:
Information, Data, Security in a Networked Future. ACM-TURING 2012: 14:1 - [c52]Sucheta Soundarajan, John E. Hopcroft:
Use of Supervised Learning to Predict Directionality of Links in a Network. ADMA 2012: 395-406 - [c51]John E. Hopcroft:
Making the World a Better Place. Logic and Program Semantics 2012: 328 - [c50]John E. Hopcroft:
Future Directions in Computer Science Research. ISAAC 2012: 1 - [c49]Bruno D. Abrahao, Sucheta Soundarajan, John E. Hopcroft, Robert Kleinberg:
On the separability of structural classes of communities. KDD 2012: 624-632 - [c48]Liaoruo Wang, Stefano Ermon, John E. Hopcroft:
Feature-Enhanced Probabilistic Models for Diffusion Network Inference. ECML/PKDD (2) 2012: 499-514 - [c47]John E. Hopcroft:
On the Impact of Turing Machines. TAMC 2012: 1-2 - [c46]Sucheta Soundarajan, John E. Hopcroft:
Using community information to improve the precision of link prediction methods. WWW (Companion Volume) 2012: 607-608 - 2011
- [j54]John E. Hopcroft, Sucheta Soundarajan, Liaoruo Wang:
The Future of Computer Science. Int. J. Softw. Informatics 5(4): 549-565 (2011) - [c45]John E. Hopcroft, Tiancheng Lou, Jie Tang:
Who will follow you back?: reciprocal relationship prediction. CIKM 2011: 1137-1146 - [c44]Liaoruo Wang, Tiancheng Lou, Jie Tang, John E. Hopcroft:
Detecting Community Kernels in Large Social Networks. ICDM 2011: 784-793 - [c43]Jing He, John E. Hopcroft, Hongyu Liang, Supasorn Suwajanakorn, Liaoruo Wang:
Detecting the Structure of Social Networks Using (α, β)-Communities. WAW 2011: 26-37 - [c42]Yookyung Jo, John E. Hopcroft, Carl Lagoze:
The web of topics: discovering the topology of topic evolution in a corpus. WWW 2011: 257-266 - 2010
- [c41]John E. Hopcroft:
New Research Directions in the Information Age. TAMC 2010: 1 - [c40]Sucheta Soundarajan, John E. Hopcroft:
Recovering Social Networks from Contagion Information. TAMC 2010: 419-430 - [c39]Liaoruo Wang, John E. Hopcroft:
Community Structure in Large Complex Networks. TAMC 2010: 455-466
2000 – 2009
- 2009
- [e2]Xiaotie Deng, John E. Hopcroft, Jinyun Xue:
Frontiers in Algorithmics, Third International Workshop, FAW 2009, Hefei, China, June 20-23, 2009. Proceedings. Lecture Notes in Computer Science 5598, Springer 2009, ISBN 978-3-642-02269-2 [contents] - 2008
- [j53]Reid Andersen, Christian Borgs, Jennifer T. Chayes, John E. Hopcroft, Vahab S. Mirrokni, Shang-Hua Teng:
Local Computation of PageRank Contributions. Internet Math. 5(1): 23-45 (2008) - [j52]John E. Hopcroft, Daniel Sheldon:
Manipulation-Resistant Reputations Using Hitting Time. Internet Math. 5(1): 71-90 (2008) - [c38]Reid Andersen, Christian Borgs, Jennifer T. Chayes, John E. Hopcroft, Kamal Jain, Vahab S. Mirrokni, Shang-Hua Teng:
Robust PageRank and locally computable spam detection features. AIRWeb 2008: 69-76 - [c37]John E. Hopcroft:
Computer Science in the Information Age. FAW 2008: 2 - [c36]Reid Andersen, Christian Borgs, Jennifer T. Chayes, John E. Hopcroft, Vahab S. Mirrokni, Shang-Hua Teng:
On the Stability of Web Crawling and Web Search. ISAAC 2008: 680-691 - 2007
- [b12]John E. Hopcroft, Rajeev Motwani, Jeffrey D. Ullman:
Introduction to automata theory, languages, and computation, 3rd Edition. Pearson international edition, Addison-Wesley 2007, ISBN 978-0-321-47617-3, pp. I-XVII, 1-535 - [j51]André Allavena, Anirban Dasgupta, John E. Hopcroft, Ravi Kumar:
Finding (Short) Paths in Social Networks. Internet Math. 3(2): 129-146 (2007) - [c35]Anirban Dasgupta, John E. Hopcroft, Ravi Kannan, Pradipta Prometheus Mitra:
Spectral clustering with limited independence. SODA 2007: 1036-1045 - [c34]John E. Hopcroft, Daniel Sheldon:
Manipulation-Resistant Reputations Using Hitting Time. WAW 2007: 68-81 - [c33]Reid Andersen, Christian Borgs, Jennifer T. Chayes, John E. Hopcroft, Vahab S. Mirrokni, Shang-Hua Teng:
Local Computation of PageRank Contributions. WAW 2007: 150-165 - 2006
- [c32]Anirban Dasgupta, John E. Hopcroft, Ravi Kannan, Pradipta Prometheus Mitra:
Spectral Clustering by Recursive Partitioning. ESA 2006: 256-267 - 2005
- [c31]Anirban Dasgupta, John E. Hopcroft, Jon M. Kleinberg, Mark Sandler:
On Learning Mixtures of Heavy-Tailed Distributions. FOCS 2005: 491-500 - [c30]Thorsten Joachims, John E. Hopcroft:
Error bounds for correlation clustering. ICML 2005: 385-392 - [c29]André Allavena, Alan J. Demers, John E. Hopcroft:
Correctness of a gossip based membership protocol. PODC 2005: 292-301 - 2004
- [c28]Anirban Dasgupta, John E. Hopcroft, Frank McSherry:
Spectral Analysis of Random Graphs with Skewed Degree Distributions. FOCS 2004: 602-610 - 2003
- [b11]John E. Hopcroft, Rajeev Motwani, Jeffrey D. Ullman:
Introduction to automata theory, languages, and computation - international edition, 2nd Edition. Addison-Wesley 2003, ISBN 978-0-321-21029-6, pp. I-XIV, 1-521 - [c27]John E. Hopcroft, Omar Khan, Brian Kulis, Bart Selman:
Natural communities in large linked networks. KDD 2003: 541-546 - 2002
- [b10]John E. Hopcroft, Rajeev Motwani, Jeffrey D. Ullman:
Einführung in die Automatentheorie, formale Sprachen und Komplexitätstheorie (2. Aufl.). Pearson Studium 2002, ISBN 978-3-8273-7020-4, pp. 1-528 - 2001
- [b9]John E. Hopcroft, Rajeev Motwani, Jeffrey D. Ullman:
Introduction to automata theory, languages, and computation, 2nd Edition. Addison-Wesley series in computer science, Addison-Wesley-Longman 2001, ISBN 978-0-201-44124-6, pp. I-XIV, 1-521 - [j50]John E. Hopcroft, Rajeev Motwani, Jeffrey D. Ullman:
Introduction to automata theory, languages, and computation, 2nd edition. SIGACT News 32(1): 60-65 (2001) - 2000
- [b8]John E. Hopcroft, Jeffrey D. Ullman:
Introduction to Automata Theory, Languages and Computation, Second Edition. Addison-Wesley 2000 - [p1]John E. Hopcroft:
Automata Theory: Its Past and Future. A Half-Century of Automata Theory 2000: 37-47
1990 – 1999
- 1994
- [b7]John E. Hopcroft, Jeffrey D. Ullman:
Einführung in die Automatentheorie, formale Sprachen und Komplexitätstheorie (3. Aufl.). Internationale Computer-Bibliothek, Addison-Wesley 1994, ISBN 978-3-89319-744-6, pp. I-IX, 1-461 - 1992
- [j49]John E. Hopcroft, Peter J. Kahn:
A Paradigm for Robust Geometric Algorithms. Algorithmica 7(4): 339-380 (1992) - 1991
- [j48]