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Network embedding maps a network into a low-dimensional Euclidean space, and thus facilitate many network analysis tasks, such as node classification, link prediction and community detection etc, by utilizing machine learning methods.
A. Bavelas, “A mathematical model for group structures.” Human Organization , vol. 7, no. 3, pp. 16–30, 1948
1948
Earlier work this paper cites.
A. Shimbel, “Structural parameters of communication networks,” Bulletin of Mathematical Biophysics , vol. 15, no. 4, pp. 501–507, 1953
1953
Earlier work this paper cites.
J. B. Tenenbaum, V. D. Silva, and J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction,” Science , vol. 290, no. 5500, p. 2319, 2000
2000
Earlier work this paper cites.
S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” science , vol. 290, no. 5500, pp. 2323–2326, 2000
2000
Earlier work this paper cites.
A. K. Mccallum, K. Nigam, J. Rennie, and K. Seymore, “Automating the construction of internet portals with machine learning,” Information Retrieval , vol. 3, no. 2, pp. 127–163, 2000
2000
Earlier work this paper cites.
H. Jeong, S. P. Mason, A.-L. Barabási, and Z. N. Oltvai, “Lethality and centrality in protein networks,” Nature , vol. 411, no. 6833, p. 41, 2001
2001
Earlier work this paper cites.
M. Belkin and P. Niyogi, “Laplacian eigenmaps and spectral techniques for embedding and clustering,” in Advances in neural information processing systems , 2002, pp. 585–591
2002
Earlier work this paper cites.
D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, “The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations,” Behavioral Ecology and Sociobiology , vol. 54, no. 4, pp. 396–405, 2003
2003
Earlier work this paper cites.
L. A. Adamic and N. Glance, “The political blogosphere and the 2004 us election: divided they blog,” in Proceedings of the 3rd international workshop on Link discovery . ACM, 2005, pp. 36–43
2005
Earlier work this paper cites.
P. Crucitti, V. Latora, and S. Porta, “Centrality in networks of urban streets,” Chaos: an interdisciplinary journal of nonlinear science , vol. 16, no. 1, p. 015113, 2006
2006
Earlier work this paper cites.
M. E. Newman, “Modularity and community structure in networks,” Proceedings of the national academy of sciences , vol. 103, no. 23, pp. 8577–8582, 2006
2006
Earlier work this paper cites.
E. Zheleva and L. Getoor, “Preserving the privacy of sensitive relationships in graph data,” in Privacy, security, and trust in KDD . Springer, 2008, pp. 153–171
2008
Earlier work this paper cites.
2008
Earlier work this paper cites.
S. Nagaraja, “The impact of unlinkability on adversarial community detection: effects and countermeasures,” in International Symposium on Privacy Enhancing Technologies Symposium . Springer, 2010, pp. 253–272
2010
Earlier work this paper cites.
A. M. Fard, K. Wang, and P. S. Yu, “Limiting link disclosure in social network analysis through subgraph-wise perturbation,” in Proceedings of the 15th International Conference on Extending Database Technology . ACM, 2012, pp. 109–119
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: online learning of social representations,” in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2014, pp. 701–710
2014
Earlier work this paper cites.
F. Tian, B. Gao, Q. Cui, E. Chen, and T. Y. Liu, “Learning deep representations for graph clustering,” in Twenty-Eighth AAAI Conference on Artificial Intelligence , 2014, pp. 1293–1299
2014
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Advances in Neural Information Processing Systems , vol. 3, pp. 2672–2680, 2014
2014
Earlier work this paper cites.
H. Sak, A. Senior, and F. Beaufays, “Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition,” Computer Science , pp. 338–342, 2014
2014
Cited alongside, same era.
2014
Cited alongside, same era.
B. Biggio, G. Fumera, and F. Roli, “Security evaluation of pattern classifiers under attack,” IEEE transactions on knowledge and data engineering , vol. 26, no. 4, pp. 984–996, 2014
2014
Cited alongside, same era.
J. Tang, M. Qu, and Q. Mei, “Pte: Predictive text embedding through large-scale heterogeneous text networks,” pp. 1165–1174, 2015
2015
Cited alongside, same era.
E. Choi, M. T. Bahadori, L. Song, W. F. Stewart, and J. Sun, “Gram: Graph-based attention model for healthcare representation learning,” in The ACM SIGKDD International Conference , 2017, pp. 787–795
2017
Later among the works it cites.
S. Wang, J. Tang, C. Aggarwal, Y. Chang, and H. Liu, “Signed network embedding in social media,” in SDM , 2017
2017
Later among the works it cites.
K. Allab, L. Labiod, and M. Nadif, “A semi-nmf-pca unified framework for data clustering,” IEEE Transactions on Knowledge and Data Engineering , vol. 29, no. 1, pp. 2–16, 2017
2017
Later among the works it cites.
H. Wang, J. Wang, J. Wang, M. Zhao, W. Zhang, F. Zhang, X. Xie, and M. Guo, “Graphgan: Graph representation learning with generative adversarial nets,” 2017
2017
Later among the works it cites.
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J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, “Line: Large-scale information network embedding,” in Proceedings of the 24th International Conference on World Wide Web . International World Wide Web Conferences Steering Committee, 2015, pp. 1067–1077
2015
Cited alongside, same era.
S. Cao, W. Lu, and Q. Xu, “Grarep: Learning graph representations with global structural information,” in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management . ACM, 2015, pp. 891–900
2015
Cited alongside, same era.
S. Mei and X. Zhu, “Using machine teaching to identify optimal training-set attacks on machine learners.” in AAAI , 2015, pp. 2871–2877
2015
Cited alongside, same era.
A. M. Fard and K. Wang, “Neighborhood randomization for link privacy in social network analysis,” World Wide Web , vol. 18, no. 1, pp. 9–32, 2015
2015
Cited alongside, same era.
Y. Yustiawan, W. Maharani, and A. A. Gozali, “Degree centrality for social network with opsahl method,” Procedia Computer Science , vol. 59, pp. 419–426, 2015
2015
Cited alongside, same era.
S. Wang, J. Tang, C. Aggarwal, and H. Liu, “Linked document embedding for classification,” pp. 115–124, 2016
2016
Cited alongside, same era.
L. Liu, W. K. Cheung, X. Li, and L. Liao, “Aligning users across social networks using network embedding.” in IJCAI , 2016, pp. 1774–1780
2016
Cited alongside, same era.
A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in SIGKDD , 2016, pp. 855–864
2016
Cited alongside, same era.
2017
Later among the works it cites.
T. Pham, T. Tran, D. Q. Phung, and S. Venkatesh, “Column networks for collective classification.” in AAAI , 2017, pp. 2485–2491
2017
Later among the works it cites.
Q. Xuan, B. Fang, Y. Liu, J. Wang, J. Zhang, Y. Zheng, and G. Bao, “Automatic pearl classification machine based on multi-stream convolutional neural network,” IEEE Transactions on Industrial Electronics , vol. PP, no. 99, pp. 1–1, 2017
2017
Later among the works it cites.
N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in Security and Privacy (SP), 2017 IEEE Symposium on . IEEE, 2017, pp. 39–57
2017
Later among the works it cites.
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against machine learning,” in Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security . ACM, 2017, pp. 506–519
2017
Later among the works it cites.
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard, “Universal adversarial perturbations,” arXiv preprint , 2017
2017
Later among the works it cites.
S. Cavallari, V. W. Zheng, H. Cai, K. C.-C. Chang, and E. Cambria, “Learning community embedding with community detection and node embedding on graphs,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . ACM, 2017, pp. 377–386
2017
Later among the works it cites.
X. Wang, P. Cui, J. Wang, J. Pei, W. Zhu, and S. Yang, “Community preserving network embedding.” in AAAI , 2017, pp. 203–209
2017
Later among the works it cites.
H. Cai, V. W. Zheng, and K. Chang, “A comprehensive survey of graph embedding: problems, techniques and applications,” IEEE Transactions on Knowledge and Data Engineering , 2018
2018
Closest in time.
2018
Closest in time.
2018
Closest in time.
M. Waniek, T. P. Michalak, M. J. Wooldridge, and T. Rahwan, “Hiding individuals and communities in a social network,” Nature Human Behaviour , vol. 2, no. 2, p. 139, 2018
2018
Closest in time.
V. Fionda and G. Pirro, “Community deception or: How to stop fearing community detection algorithms,” IEEE Transactions on Knowledge and Data Engineering , vol. 30, no. 4, pp. 660–673, 2018
2018
Closest in time.
D. Zügner, A. Akbarnejad, and S. Günnemann, “Adversarial attacks on neural networks for graph data,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018 , 2018, pp. 2847–2856
2018
Closest in time.