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Graph embedding is a transformation of nodes of a graph into a set of vectors.
P. J. Rousseeuw: Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis, Computational and Applied Mathematics. 1987, 20: 53–65
1987
Earlier work this paper cites.
P.D. Hoff, A.E. Raftery and M.S. Handcock, Latent space approaches to social network analysis, J. Amer. Stat. Assoc. 2002; 97(460) 1090-1098
2002
Earlier work this paper cites.
M. Girvan, M.E. Newman. Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99, 7821-7826 (2002)
2002
Earlier work this paper cites.
M.T. Gastner and M.E.J. Newman, The spatial structure of networks. European Physical Journal B. 2006; 49(2):247-252
2006
Earlier work this paper cites.
A. Lancichinetti, S. Fortunato, and F. Radicchi. Benchmark graphs for testing community detection algorithms. Phys. Rev. E, 78(4), 2008
2008
Earlier work this paper cites.
M. Newman, Networks: An Introduction. Oxford University Press; 2010
2010
Earlier work this paper cites.
J. Janssen, Spatial Models for Virtual Networks. CiE 2010, LNCS 6158, pp. 201-210, 2010
2010
Earlier work this paper cites.
P. Expert, T.S. Evans, V.D. Blondel and R. Lambiotte, Uncovering space-independent communities in spatial networks. Proceedings of the National Academy of Sciences. (2011); 108(19):7663-7668
2011
Earlier work this paper cites.
T. Mikolov, Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 2013
2013
Earlier work this paper cites.
B. Perozzi, R. Al-Rfou, and S. Skiena. DeepWalk: Online learning of social representations. In KDD, 2014
2014
Earlier work this paper cites.
G. Bianconi, Interdisciplinary and physics challenges of network theory. EPL. 2015; 111(5):56001
2015
Earlier work this paper cites.
K. Zuev, M. Boguna, G. Bianconi and D. Krioukov, Emergence of Soft Communities from Geometric Preferential Attachment. Scientific Reports. 2015; 5,9421
2015
Cited alongside, same era.
J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, Q. Mei, Line: Large-scale information network embedding, in: Proceedings 24th International Conference on World Wide Web, 2015, pp. 1067–1077
2015
Cited alongside, same era.
A.L. Barabasi, Network Science, Cambridge U Press, 2016
2016
Cited alongside, same era.
D. Krioukov, Clustering means geometry in networks. Phys Rev Lett. 2016; 208302(May):1-5
2016
Cited alongside, same era.
A. Grover, J. Leskovec. node2vec: Scalable Feature Learning for Networks. KDD 2016: 855–864
2016
Cited alongside, same era.
V. Poulin and F. Théberge, Ensemble Clustering for Graphs. In: Aiello L., Cherifi C., Cherifi H., Lambiotte R., Lió P., Rocha L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 812. Springer, Cham
2018
Later among the works it cites.
Z. Lu, J. Wahlström, A. Nehorai, Community Detection in Complex Networks via Clique Conductance. Nature Scientific Reports (2018) 8:5982
2018
Later among the works it cites.
2018
Later among the works it cites.
N. Lavrač, B. Škrlj, M. Robnik-Šikonja: Propositionalization and embeddings: two sides of the same coin. Mach Learn. 2020;109(7):1465-1507. doi:10.1007/s10994-020-05890-8
2020
Later among the works it cites.
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D. Wang, P. Cui, and W. Zhu, Structural deep network embedding. In Proc. ACM SIGKDD, 2016, pp. 1225–1234
2016
Cited alongside, same era.
M. Ou, P. Cui, J. Pei, Z. Zhang, W. Zhu, Asymmetric Transitivity Preserving Graph Embedding. In KDD 2016
2016
Cited alongside, same era.
W. L. Hamilton, R. Ying, J. Leskovec: Representation Learning on Graphs: Methods and Applications. IEEE Data Eng. Bull. 40(3): 52-74 (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
P. Goyal, E. Ferrara, Graph Embedding Techniques, Applications, and Performance: A Survey. Knowledge Based Systems 151(1) (2018) 78–94
2018
Cited alongside, same era.
A. Tsitsulin, D. Mottin, P. Karras, and E. Müller. 2018. VERSE: Versatile Graph Embeddings from Similarity Measures. In Proceedings of the 2018 World Wide Web Conference (WWW’18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 539-548
2018
Cited alongside, same era.
B. Kamiński, P. Prałat, and F. Théberge, An Unsupervised Framework for Comparing Graph Embeddings, Journal of Complex Networks 8(5) (2020), cnz043
2020
Later among the works it cites.
B. Kamiński, P. Prałat, and F. Théberge, A Scalable Unsupervised Framework for Comparing Graph Embeddings, Proceedings of the 17th Workshop on Algorithms and Models for the Web Graph (WAW 2020), Lecture Notes in Computer Science 12091, Springer, 2020, 52–67
2020
Later among the works it cites.
B. Kamiński, P. Prałat, and F. Théberge, Mining Complex Networks, CRC Press, 2021
2021
Closest in time.
I. Makarov, D. Kiselev, N. Nikitinsky and L. Subelj, Survey on graph embeddings and their applications to machine learning problems on graphs. PeerJ Computer Science 7:e357 (2021), https://doi.org/10.7717/peerj-cs.357
2021
Closest in time.
B. Kamiński, P. Prałat, and F. Théberge, Artificial Benchmark for Community Detection (ABCD) — Fast Random Graph Model with Community Structure. Network Science. 9(2) (2021), 153-178
2021
Closest in time.
2022
Closest in time.