Fetching the paper…
Reading the bibliography…
Most approaches that tackle the problem of node classification consider nodes to be similar, if they have shared neighbors or are close to each other in the graph.
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural computation , vol. 1, no. 4, pp. 541–551, 1989
1989
Earlier work this paper cites.
L. Page, S. Brin, R. Motwani, and T. Winograd, “The pagerank citation ranking: bringing order to the web.” 1999
1999
Earlier work this paper cites.
M. McPherson, L. Smith-Lovin, and J. M. Cook, “Birds of a feather: Homophily in social networks,” Annual review of sociology , vol. 27, no. 1, pp. 415–444, 2001
2001
Earlier work this paper cites.
X. Zhu, Z. Ghahramani, and J. D. Lafferty, “Semi-supervised learning using gaussian fields and harmonic functions,” in Proc. of ICML , 2003, pp. 912–919
2003
Earlier work this paper cites.
G. Jeh and J. Widom, “Scaling personalized web search,” in Proc. of the 12th WWW . ACM, 2003, pp. 271–279
2003
Earlier work this paper cites.
D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf, “Learning with local and global consistency,” in Advances in neural information processing systems , 2004, pp. 321–328
2004
Earlier work this paper cites.
M. Belkin, P. Niyogi, and V. Sindhwani, “Manifold regularization: A geometric framework for learning from labeled and unlabeled examples,” Journal of machine learning research , vol. 7, no. Nov, pp. 2399–2434, 2006
2006
Earlier work this paper cites.
P. Berkhin, “Bookmark-coloring algorithm for personalized pagerank computing,” Internet Mathematics , vol. 3, no. 1, pp. 41–62, 2006
2006
Earlier work this paper cites.
R. Andersen, F. Chung, and K. Lang, “Local graph partitioning using pagerank vectors,” in Proc. of IEEE FOCS . IEEE, 2006, pp. 475–486
2006
Earlier work this paper cites.
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad, “Collective classification in network data,” AI magazine , vol. 29, no. 3, p. 93, 2008
2008
Earlier work this paper cites.
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE Transactions on Neural Networks , vol. 20, no. 1, pp. 61–80, 2009
2009
Earlier work this paper cites.
2009
Earlier work this paper cites.
J. Weston, F. Ratle, H. Mobahi, and R. Collobert, “Deep learning via semi-supervised embedding,” in Neural Networks: Tricks of the Trade . Springer, 2012, pp. 639–655
2012
Earlier work this paper cites.
G. Namata, B. London, L. Getoor, B. Huang, and U. EDU, “Query-driven active surveying for collective classification,” in 10th International Workshop on Mining and Learning with Graphs , 2012
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 Proc. of ACM SIGKDD , 2014, pp. 701–710
2014
Cited alongside, same era.
J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, “Line: Large-scale information network embedding,” in Proc. of WWW . ACM, 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 Proc. of CIKM . ACM, 2015, pp. 891–900
2015
Cited alongside, same era.
2015
Cited alongside, same era.
S. A. Al-Sayouri, P. Devineni, S. S. Lam, E. E. Papalexakis, and D. Koutra, “Gecs: Graph embedding using connection subgraphs,” 2016
2016
Later among the works it cites.
S. Nandanwar and M. N. Murty, “Structural neighborhood based classification of nodes in a network,” in Proc. of ACM SIGKDD , 2016, pp. 1085–1094
2016
Later among the works it cites.
2017
Later among the works it cites.
L. F. Ribeiro, P. H. Saverese, and D. R. Figueiredo, “struc2vec: Learning node representations from structural identity,” in Proc. of ACM SIGKDD . ACM, 2017, pp. 385–394
2017
Later among the works it cites.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in NIPS , 2017, pp. 1025–1035
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in Proc. of ACM SIGKDD , 2016, pp. 855–864
2016
Cited alongside, same era.
D. Wang, P. Cui, and W. Zhu, “Structural deep network embedding,” in Proc. of ACM SIGKDD . ACM, 2016, pp. 1225–1234
2016
Cited alongside, same era.
T. N. Kipf and M. Welling, “Variational graph auto-encoders,” arXiv preprint arXiv:1611.07308 , 2016
2016
Cited alongside, same era.
Z. Yang, W. Cohen, and R. Salakhudinov, “Revisiting semi-supervised learning with graph embeddings,” in Proc. of ICDM , 2016, pp. 40–48
2016
Cited alongside, same era.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in Advances in Neural Information Processing Systems 29 , D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, Eds., 2016, pp. 3844–3852
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
M. Simonovsky and N. Komodakis, “Dynamic edge-conditioned filters in convolutional neural networks on graphs,” in Proc. CVPR , 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
W. Ye, L. Zhou, D. Mautz, C. Plant, and C. Böhm, “Learning from labeled and unlabeled vertices in networks,” in Proc. of ACM SIGKDD . ACM, 2017, pp. 1265–1274
2017
Later among the works it cites.