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Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang, “Graph wavenet for deep spatial-temporal graph modeling,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence . AAAI Press, 2019, pp. 1907–1913
1913
Earlier work this paper cites.
G. H. Golub and C. Reinsch, “Singular value decomposition and least squares solutions,” Numerische Mathematik , vol. 14, no. 5, pp. 403–420, 1970
1970
Earlier work this paper cites.
W. N. Anderson Jr and T. D. Morley, “Eigenvalues of the laplacian of a graph,” Linear and Multilinear Algebra , vol. 18, no. 2, pp. 141–145, 1985
1985
Earlier work this paper cites.
L. Vandenberghe and S. Boyd, “Semidefinite programming,” SIAM Review , vol. 38, no. 1, pp. 49–95, 1996
1996
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.
J. Atwood and D. Towsley, “Diffusion-convolutional neural networks,” in Advances in Neural Information Processing Systems , 2016, pp. 1993–2001
2001
Earlier work this paper cites.
M. Balasubramanian and E. L. Schwartz, “The isomap algorithm and topological stability,” Science , vol. 295, no. 5552, pp. 7–7, 2002
2002
Earlier work this paper cites.
I. Borg and P. Groenen, “Modern multidimensional scaling: Theory and applications,” Journal of Educational Measurement , vol. 40, no. 3, pp. 277–280, 2003
2003
Earlier work this paper cites.
X. He and P. Niyogi, “Locality preserving projections,” in Advances in Neural Information Processing Systems , 2004, pp. 153–160
2004
Earlier work this paper cites.
X. He, W.-Y. Ma, and H.-J. Zhang, “Learning an image manifold for retrieval,” in Proceedings of the 12th annual ACM international conference on Multimedia . ACM, 2004, pp. 17–23
2004
Earlier work this paper cites.
M. Gori, G. Monfardini, and F. Scarselli, “A new model for learning in graph domains,” in IEEE International Joint Conference on Neural Networks , vol. 2. IEEE, 2005, pp. 729–734
2005
Earlier work this paper cites.
J. Leskovec and C. Faloutsos, “Sampling from large graphs,” in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2006, pp. 631–636
2006
Earlier work this paper cites.
U. Von Luxburg, “A tutorial on spectral clustering,” Statistics and Computing , vol. 17, no. 4, pp. 395–416, 2007
2007
Earlier work this paper cites.
S. Yan, D. Xu, B. Zhang, H.-J. Zhang, Q. Yang, and S. Lin, “Graph embedding and extensions: A general framework for dimensionality reduction,” IEEE Transactions on Pattern Analysis & Machine Intelligence , no. 1, pp. 40–51, 2007
2007
Earlier work this paper cites.
D. Cai, X. He, and J. Han, “Spectral regression: A unified subspace learning framework for content-based image retrieval,” in Proceedings of the 15th ACM international conference on Multimedia . ACM, 2007, pp. 403–412
2007
Earlier work this paper cites.
M. Puschel and J. M. Moura, “Algebraic signal processing theory: Foundation and 1-d time,” IEEE Transactions on Signal Processing , vol. 56, no. 8, pp. 3572–3585, 2008
2008
Earlier work this paper cites.
S. Xiang, F. Nie, C. Zhang, and C. Zhang, “Nonlinear dimensionality reduction with local spline embedding,” IEEE Transactions on Knowledge and Data Engineering , vol. 21, no. 9, pp. 1285–1298, 2008
2008
Earlier work this paper cites.
Y. Yang, F. Nie, S. Xiang, Y. Zhuang, and W. Wang, “Local and global regressive mapping for manifold learning with out-of-sample extrapolation,” in Twenty-Fourth AAAI Conference on Artificial Intelligence , 2010, pp. 649–654
2010
Earlier work this paper cites.
N. Lao and W. W. Cohen, “Relational retrieval using a combination of path-constrained random walks,” Machine learning , vol. 81, no. 1, pp. 53–67, 2010
2010
Earlier work this paper cites.
N. Lao, T. Mitchell, and W. W. Cohen, “Random walk inference and learning in a large scale knowledge base,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, 2011, pp. 529–539
2011
Earlier work this paper cites.
X. Zhu and M. Rabbat, “Graph spectral compressed sensing for sensor networks,” in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2012, pp. 2865–2868
2012
Earlier work this paper cites.
R. Jenatton, N. L. Roux, A. Bordes, and G. R. Obozinski, “A latent factor model for highly multi-relational data,” in Advances in Neural Information Processing Systems , 2012, pp. 3167–3175
2012
Earlier work this paper cites.
Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 35, no. 8, pp. 1798–1828, 2013
2013
Earlier work this paper cites.
A. Sandryhaila and J. M. Moura, “Discrete signal processing on graphs,” IEEE Transactions on Signal Processing , vol. 61, no. 7, pp. 1644–1656, 2013
2013
Earlier work this paper cites.
D. Shuman, S. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, “The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains,” IEEE Signal Processing Magazine , vol. 3, no. 30, pp. 83–98, 2013
2013
Earlier work this paper cites.
A. Sandryhaila and J. M. Moura, “Discrete signal processing on graphs: Graph filters,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing . IEEE, 2013, pp. 6163–6166
2013
Earlier work this paper cites.
S. K. Narang, A. Gadde, and A. Ortega, “Signal processing techniques for interpolation in graph structured data,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing . IEEE, 2013, pp. 5445–5449
2013
Earlier work this paper cites.
F. Xia, N. Y. Asabere, A. M. Ahmed, J. Li, and X. Kong, “Mobile multimedia recommendation in smart communities: A survey,” IEEE Access , vol. 1, no. 1, pp. 606–624, 2013
2013
Earlier work this paper cites.
A. Ahmed, N. Shervashidze, S. Narayanamurthy, V. Josifovski, and A. J. Smola, “Distributed large-scale natural graph factorization,” in Proceedings of the 22nd International Conference on World Wide Web . ACM, 2013, pp. 37–48
2013
Earlier work this paper cites.
M. Gardner, P. P. Talukdar, B. Kisiel, and T. Mitchell, “Improving learning and inference in a large knowledge-base using latent syntactic cues,” in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing , 2013, pp. 833–838
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” in Advances in Neural Information Processing Systems , 2013, pp. 2787–2795
2013
Earlier work this paper cites.
F. Xia, A. M. Ahmed, L. T. Yang, J. Ma, and J. J. Rodrigues, “Exploiting social relationship to enable efficient replica allocation in ad-hoc social networks,” IEEE Transactions on Parallel and Distributed Systems , vol. 25, no. 12, pp. 3167–3176, 2014
2014
Earlier work this paper cites.
F. Xia, A. M. Ahmed, L. T. Yang, and Z. Luo, “Community-based event dissemination with optimal load balancing,” IEEE Transactions on Computers , vol. 64, no. 7, pp. 1857–1869, 2014
2014
Earlier work this paper cites.
A. Anis, A. Gadde, and A. Ortega, “Towards a sampling theorem for signals on arbitrary graphs,” in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2014, pp. 3864–3868
2014
Earlier work this paper cites.
H. Shomorony and A. S. Avestimehr, “Sampling large data on graphs,” in 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP) . IEEE, 2014, pp. 933–936
2014
Earlier work this paper cites.
M. Chen, I. W. Tsang, M. Tan, and T. J. Cham, “A unified feature selection framework for graph embedding on high dimensional data,” IEEE Transactions on Knowledge and Data Engineering , vol. 27, no. 6, pp. 1465–1477, 2014
2014
Earlier work this paper cites.
F. Xia, Z. Chen, W. Wang, J. Li, and L. T. Yang, “Mvcwalker: Random walk-based most valuable collaborators recommendation exploiting academic factors,” IEEE Transactions on Emerging Topics in Computing , vol. 2, no. 3, pp. 364–375, 2014
2014
Earlier work this paper cites.
B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2014, pp. 701–710
2014
Earlier work this paper cites.
O. Levy and Y. Goldberg, “Neural word embedding as implicit matrix factorization,” in Advances in Neural Information Processing Systems , 2014, pp. 2177–2185
2014
Earlier work this paper cites.
X. Rong, “word2vec parameter learning explained,” arXiv preprint arXiv:1411.2738 , 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
M. Gardner, P. Talukdar, J. Krishnamurthy, and T. Mitchell, “Incorporating vector space similarity in random walk inference over knowledge bases,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2014, pp. 397–406
2014
Earlier work this paper cites.
Y. Qi, Y. Wang, X. Zheng, and Z. Wu, “Robust feature learning by stacked autoencoder with maximum correntropy criterion,” in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2014, pp. 6716–6720
2014
Earlier work this paper cites.
Z. Wang, J. Zhang, J. Feng, and Z. Chen, “Knowledge graph embedding by translating on hyperplanes,” in Twenty-Eighth AAAI Conference on Artificial Intelligence , 2014, pp. 1112–1119
2014
Earlier work this paper cites.
X. Wang, P. Liu, and Y. Gu, “Local-set-based graph signal reconstruction,” IEEE Transactions on Signal Processing , vol. 63, no. 9, pp. 2432–2444, 2015
2015
Earlier work this paper cites.
L. Akoglu, H. Tong, and D. Koutra, “Graph based anomaly detection and description: a survey,” Data Mining and Knowledge Discovery , vol. 29, no. 3, pp. 626–688, 2015
2015
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , vol. 521, no. 7553, p. 436, 2015
2015
Earlier work this paper cites.
S. Chen, R. Varma, A. Sandryhaila, and J. Kovačević, “Discrete signal processing on graphs: Sampling theory,” IEEE Transactions on Signal Processing , vol. 63, no. 24, pp. 6510–6523, 2015
2015
Earlier work this paper cites.
B. Pasdeloup, M. Rabbat, V. Gripon, D. Pastor, and G. Mercier, “Graph reconstruction from the observation of diffused signals,” in 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton) . IEEE, 2015, pp. 1386–1390
2015
Earlier work this paper cites.
A. Gadde and A. Ortega, “A probabilistic interpretation of sampling theory of graph signals,” in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2015, pp. 3257–3261
2015
Earlier work this paper cites.
X. Wang, M. Wang, and Y. Gu, “A distributed tracking algorithm for reconstruction of graph signals,” IEEE Journal of Selected Topics in Signal Processing , vol. 9, no. 4, pp. 728–740, 2015
2015
Earlier work this paper cites.
M. Nagahara, “Discrete signal reconstruction by sum of absolute values,” IEEE Signal Processing Letters , vol. 22, no. 10, pp. 1575–1579, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
C. Yang, Z. Liu, D. Zhao, M. Sun, and E. Y. Chang, “Network representation learning with rich text information,” in International Joint Conference on Artificial Intelligence , 2015, pp. 2111–2117
2015
Earlier work this paper cites.
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 , 2015, pp. 1067–1077
2015
Earlier work this paper cites.
W. Y. Wang and W. W. Cohen, “Joint information extraction and reasoning: A scalable statistical relational learning approach,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , 2015, pp. 355–364
2015
Earlier work this paper cites.
J. Tang, M. Qu, and Q. Mei, “Pte: Predictive text embedding through large-scale heterogeneous text networks,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2015, pp. 1165–1174
2015
Earlier work this paper cites.
M. Henaff, J. Bruna, and Y. LeCun, “Deep convolutional networks on graph-structured data,” Advances in Neural Information Processing Systems , pp. 1–9, 2015
2015
Earlier work this paper cites.
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, “Convolutional networks on graphs for learning molecular fingerprints,” in Advances in Neural Information Processing Systems , 2015, pp. 2224–2232
2015
Earlier work this paper cites.
Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu, “Learning entity and relation embeddings for knowledge graph completion,” in Twenty-ninth AAAI conference on artificial intelligence , 2015, pp. 2181–2187
2015
Earlier work this paper cites.
G. Ji, S. He, L. Xu, K. Liu, and J. Zhao, “Knowledge graph embedding via dynamic mapping matrix,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , vol. 1, 2015, pp. 687–696
2015
Earlier work this paper cites.
B. Yang, W.-t. Yih, X. He, J. Gao, and L. Deng, “Embedding entities and relations for learning and inference in knowledge bases,” International Conference on Learning Representations , 2015
2015
Cited alongside, same era.
A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2016, pp. 855–864
2016
Cited alongside, same era.
M. Nickel, K. Murphy, V. Tresp, and E. Gabrilovich, “A review of relational machine learning for knowledge graphs,” Proceedings of the IEEE , vol. 104, no. 1, pp. 11–33, 2016
2016
Cited alongside, same era.
A. G. Marques, S. Segarra, G. Leus, and A. Ribeiro, “Sampling of graph signals with successive local aggregations.” IEEE Transactions Signal Processing , vol. 64, no. 7, pp. 1832–1843, 2016
2016
Cited alongside, same era.
B. Pasdeloup, V. Gripon, G. Mercier, D. Pastor, and M. G. Rabbat, “Characterization and inference of graph diffusion processes from observations of stationary signals,” IEEE Transactions on Signal and Information Processing over Networks , vol. 4, no. 3, pp. 481–496, 2018
2018
Later among the works it cites.
W. Huang, A. G. Marques, and A. R. Ribeiro, “Rating prediction via graph signal processing,” IEEE Transactions on Signal Processing , vol. 66, no. 19, pp. 5066–5081, 2018
2018
Later among the works it cites.
M. A. Al-Garadi, K. D. Varathan, S. D. Ravana, E. Ahmed, G. Mujtaba, M. U. S. Khan, and S. U. Khan, “Analysis of online social network connections for identification of influential users: Survey and open research issues,” ACM Computing Surveys (CSUR) , vol. 51, no. 1, pp. 1–37, 2018
2018
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,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018, pp. 2508–2515
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P. Di Lorenzo, S. Barbarossa, P. Banelli, and S. Sardellitti, “Adaptive least mean squares estimation of graph signals,” IEEE Transactions on Signal and Information Processing over Networks , vol. 2, no. 4, pp. 555–568, 2016
2016
Cited alongside, same era.
S. Segarra, A. G. Marques, G. Leus, and A. Ribeiro, “Reconstruction of graph signals through percolation from seeding nodes,” IEEE Transactions on Signal Processing , vol. 64, no. 16, pp. 4363–4378, 2016
2016
Cited alongside, same era.
X. Dong, D. Thanou, P. Frossard, and P. Vandergheynst, “Learning laplacian matrix in smooth graph signal representations,” IEEE Transactions on Signal Processing , vol. 64, no. 23, pp. 6160–6173, 2016
2016
Cited alongside, same era.
V. Kalofolias, “How to learn a graph from smooth signals,” in Artificial Intelligence and Statistics , 2016, pp. 920–929
2016
Cited alongside, same era.
E. Pavez and A. Ortega, “Generalized laplacian precision matrix estimation for graph signal processing,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2016, pp. 6350–6354
2016
Cited alongside, same era.
J. Mei and J. M. Moura, “Signal processing on graphs: Causal modeling ofunstructured data,” IEEE Transactions on Signal Processing , vol. 65, no. 8, pp. 2077–2092, 2016
2016
Cited alongside, same era.
F. Xia, H. Liu, I. Lee, and L. Cao, “Scientific article recommendation: Exploiting common author relations and historical preferences,” IEEE Transactions on Big Data , vol. 2, no. 2, pp. 101–112, 2016
2016
Cited alongside, same era.
R. Jiang, W. Fu, L. Wen, S. Hao, and R. Hong, “Dimensionality reduction on anchorgraph with an efficient locality preserving projection,” Neurocomputing , vol. 187, pp. 109–118, 2016
2016
Cited alongside, same era.
2018
Later among the works it cites.
A. Bojchevski, O. Shchur, D. Zügner, and S. Günnemann, “Netgan: Generating graphs via random walks,” Proceedings of the 35th International Conference on Machine Learning (ICML 2018) , pp. 609–618, 2018
2018
Later among the works it cites.
R. Hussein, D. Yang, and P. Cudré-Mauroux, “Are meta-paths necessary?: Revisiting heterogeneous graph embeddings,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management . ACM, 2018, pp. 437–446
2018
Later among the works it cites.
G. H. Nguyen, J. B. Lee, R. A. Rossi, N. K. Ahmed, E. Koh, and S. Kim, “Continuous-time dynamic network embeddings,” in Companion Proceedings of the The Web Conference , 2018, pp. 969–976
2018
Later among the works it cites.
Y. Zuo, G. Liu, H. Lin, J. Guo, X. Hu, and J. Wu, “Embedding temporal network via neighborhood formation,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 2018, pp. 2857–2866
2018
Later among the works it cites.
S. Yan, Y. Xiong, and D. Lin, “Spatial temporal graph convolutional networks for skeleton-based action recognition,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018, pp. 3634–3640
2018
Later among the works it cites.
M. Allamanis, M. Brockschmidt, and M. Khademi, “Learning to represent programs with graphs,” International Conference on Learning Representations , 2018
2018
Later among the works it cites.
C. Zhuang and Q. Ma, “Dual graph convolutional networks for graph-based semi-supervised classification,” in Proceedings of the Web Conference , 2018, pp. 499–508
2018
Later among the works it cites.
H. Dai, Z. Kozareva, B. Dai, A. Smola, and L. Song, “Learning steady-states of iterative algorithms over graphs,” in International Conference on Machine Learning , 2018, pp. 1114–1122
2018
Later among the works it cites.
H. Gao, Z. Wang, and S. Ji, “Large-scale learnable graph convolutional networks,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2018, pp. 1416–1424
2018
Later among the works it cites.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” International Conference on Learning Representations , 2018
2018
Later among the works it cites.
J. Zhang, X. Shi, J. Xie, H. Ma, I. King, and D.-Y. Yeung, “GaAN: Gated attention networks for learning on large and spatiotemporal graphs,” Thirty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI) , 2018
2018
Later among the works it cites.
J. B. Lee, R. Rossi, and X. Kong, “Graph classification using structural attention,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2018, pp. 1666–1674
2018
Later among the works it cites.
S. Abu-El-Haija, B. Perozzi, R. Al-Rfou, and A. A. Alemi, “Watch your step: Learning node embeddings via graph attention,” in Advances in Neural Information Processing Systems , 2018, pp. 9180–9190
2018
Later among the works it cites.
K. Tu, P. Cui, X. Wang, P. S. Yu, and W. Zhu, “Deep recursive network embedding with regular equivalence,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2018, pp. 2357–2366
2018
Later among the works it cites.
M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling, “Modeling relational data with graph convolutional networks,” in European Semantic Web Conference . Springer, 2018, pp. 593–607
2018
Later among the works it cites.
2018
Later among the works it cites.
Y. Xian, C. H. Lampert, B. Schiele, and Z. Akata, “Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 41, no. 9, pp. 2251–2265, 2018
2018
Later among the works it cites.
Y. Zhang, Q. Liu, and L. Song, “Sentence-state LSTM for text representation,” The 56th Annual Meeting of the Association for Computational Linguistics , pp. 317–327, 2018
2018
Later among the works it cites.
D. Beck, G. Haffari, and T. Cohn, “Graph-to-sequence learning using gated graph neural networks,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2018, pp. 273–283
2018
Later among the works it cites.
H. Peng, J. Li, Y. He, Y. Liu, M. Bao, L. Wang, Y. Song, and Q. Yang, “Large-scale hierarchical text classification with recursively regularized deep graph-cnn,” in Proceedings of the Web Conference , 2018, pp. 1063–1072
2018
Later among the works it cites.
Z. Wang, T. Chen, J. Ren, W. Yu, H. Cheng, and L. Lin, “Deep reasoning with knowledge graph for social relationship understanding,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence . AAAI Press, 2018, pp. 1021–1028
2018
Later among the works it cites.
C.-W. Lee, W. Fang, C.-K. Yeh, and Y.-C. Frank Wang, “Multi-label zero-shot learning with structured knowledge graphs,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 1576–1585
2018
Later among the works it cites.
K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, and A. Walsh, “Machine learning for molecular and materials science,” Nature , vol. 559, no. 7715, pp. 547–555, 2018
2018
Later among the works it cites.
S. Rhee, S. Seo, and S. Kim, “Hybrid approach of relation network and localized graph convolutional filtering for breast cancer subtype classification,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence . AAAI Press, 2018, pp. 3527–3534
2018
Later among the works it cites.
T. Hamaguchi, H. Oiwa, M. Shimbo, and Y. Matsumoto, “Knowledge base completion with out-of-knowledge-base entities: A graph neural network approach,” Transactions of the Japanese Society for Artificial Intelligence , vol. 33, pp. 1–10, 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
F. Xia, H. Wei, S. Yu, D. Zhang, and B. Xu, “A survey of measures for network motifs,” IEEE Access , vol. 7, no. 1, pp. 106 576–106 587, 2019
2019
Later among the works it cites.
L. Wan, Y. Yuan, F. Xia, and H. Liu, “To your surprise: Identifying serendipitous collaborators,” IEEE Transactions on Big Data , 2019
2019
Later among the works it cites.
F. Xia, J. Liu, H. Nie, Y. Fu, L. Wan, and X. Kong, “Random walks: A review of algorithms and applications,” IEEE Transactions on Emerging Topics in Computational Intelligence , vol. 4, no. 2, pp. 95–107, 2019
2019
Later among the works it cites.
W. Wang, J. Liu, Z. Yang, X. Kong, and F. Xia, “Sustainable collaborator recommendation based on conference closure,” IEEE Transactions on Computational Social Systems , vol. 6, no. 2, pp. 311–322, 2019
2019
Later among the works it cites.
C. Shi, B. Hu, W. X. Zhao, and S. Y. Philip, “Heterogeneous information network embedding for recommendation,” IEEE Transactions on Knowledge and Data Engineering , vol. 31, no. 2, pp. 357–370, 2019
2019
Later among the works it cites.
C. Zhang, A. Swami, and N. V. Chawla, “Shne: Representation learning for semantic-associated heterogeneous networks,” in Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining . ACM, 2019, pp. 690–698
2019
Later among the works it cites.
2019
Later among the works it cites.
S. Pan, R. Hu, S.-f. Fung, G. Long, J. Jiang, and C. Zhang, “Learning graph embedding with adversarial training methods,” IEEE Transactions on Cybernetics , 2019
2019
Later among the works it cites.
L. Yao, C. Mao, and Y. Luo, “Graph convolutional networks for text classification,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 7370–7377
2019
Later among the works it cites.
J. Liu, J. Ren, W. Zheng, L. Chi, I. Lee, and F. Xia, “Web of scholars: A scholar knowledge graph,” in the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) , 2020, pp. 2153–2156
2020
Later among the works it cites.
D. Zhang, J. Yin, X. Zhu, and C. Zhang, “Network representation learning: A survey,” IEEE Transactions on Big Data , vol. 6, no. 1, pp. 3–28, 2020
2020
Later among the works it cites.
K. Sun, J. Liu, S. Yu, B. Xu, and F. Xia, “Graph force learning,” in IEEE International Conference on Big Data (BigData) , 2020, pp. 2987–2994
2020
Later among the works it cites.
F. Xia, J. Wang, X. Kong, D. Zhang, and Z. Wang, “Ranking station importance with human mobility patterns using subway network datasets,” IEEE Transactions on Intelligent Transportation Systems , vol. 21, no. 7, pp. 2840–2852, 2020
2020
Later among the works it cites.
K. Sun, L. Wang, B. Xu, W. Zhao, S. W. Teng, and F. Xia, “Network representation learning: From traditional feature learning to deep learning,” IEEE Access , vol. 8, no. 1, pp. 205 600–205 617, 2020
2020
Later among the works it cites.
S. Yu, F. Xia, J. Xu, Z. Chen, and I. Lee, “Offer: A motif dimensional framework for network representation learning,” in The 29th ACM International Conference on Information and Knowledge Management (CIKM) , 2020, pp. 3349–3352
2020
Later among the works it cites.
T. Guo, F. Xia, S. Zhen, X. Bai, D. Zhang, Z. Liu, and J. Tang, “Graduate employment prediction with bias,” in Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI) , 2020, pp. 670–677
2020
Later among the works it cites.
J. Zhang, W. Wang, F. Xia, Y.-R. Lin, and H. Tong, “Data-driven computational social science: A survey,” Big Data Research , vol. 21, p. 100145, 2020
2020
Later among the works it cites.
Z. Zhang, P. Cui, and W. Zhu, “Deep learning on graphs: A survey,” IEEE Transactions on Knowledge and Data Engineering , 2020
2020
Later among the works it cites.
J. Xu, S. Yu, K. Sun, J. Ren, I. Lee, S. Pan, and F. Xia, “Multivariate relations aggregation learning in social networks,” in ACM/IEEE Joint Conference on Digital Libraries (JCDL) , 2020, pp. 77–86
2020
Later among the works it cites.
H. D. Bedru, S. Yu, X. Xiao, D. Zhang, L. Wan, H. Guo, and F. Xia, “Big networks: A survey,” Computer Science Review , vol. 37, p. 100247, 2020
2020
Later among the works it cites.
F. Xia, J. Liu, J. Ren, W. Wang, and X. Kong, “Turing number: How far are you to a. m. turing award?” ACM SIGWEB Newsletter , vol. Autumn, 2020, article No.: 5
2020
Later among the works it cites.
G. Wan, B. Du, S. Pan, and G. Haffari, “Reinforcement learning based meta-path discovery in large-scale heterogeneous information networks,” in AAAI Conference on Artificial Intelligence . AAAI, apr 2020
2020
Later among the works it cites.
M. Hou, J. Ren, D. Zhang, X. Kong, D. Zhang, and F. Xia, “Network embedding: Taxonomies, frameworks and applications,” Computer Science Review , vol. 38, p. 100296, 2020
2020
Later among the works it cites.
S. Zhu, L. Zhou, S. Pan, C. Zhou, G. Yan, and B. Wang, “GSSNN: Graph smoothing splines neural networks,” in AAAI Conference on Artificial Intelligence . AAAI, apr 2020
2020
Later among the works it cites.
L. Jing and Y. Tian, “Self-supervised visual feature learning with deep neural networks: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020
2020
Later among the works it cites.
M. R. Vyas, H. Venkateswara, and S. Panchanathan, “Leveraging seen and unseen semantic relationships for generative zero-shot learning,” in European Conference on Computer Vision . Springer, 2020, pp. 70–86
2020
Later among the works it cites.
2020
Later among the works it cites.
J. Liu, F. Xia, L. Wang, B. Xu, X. Kong, H. Tong, and I. King, “Shifu2: A network representation learning based model for advisor-advisee relationship mining,” IEEE Transactions on Knowledge and Data Engineering , vol. 33, no. 4, pp. 1763–1777, 2021
2021
Closest in time.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, “A comprehensive survey on graph neural networks,” IEEE Transactions on Neural Networks and Learning Systems , vol. 32, no. 1, pp. 4–24, 2021
2021
Closest in time.
M. Hou, L. Wang, J. Liu, X. Kong, and F. Xia, “A3graph: Adversarial attributed autoencoder for graph representation,” in The 36th ACM Symposium on Applied Computing (SAC) , 2021, pp. 1697–1704
2021
Closest in time.
M. Niepert, M. Ahmed, and K. Kutzkov, “Learning convolutional neural networks for graphs,” in International Conference on Machine Learning , 2016, pp. 2014–2023
2023
Closest in time.