1 |
FREEMAN L C. Visualizing social networks[J]. Journal of Social Structure, 2000, 1: No.1.
|
2 |
KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[EB/OL]. (2017-02-22) [2021-06-01]..
|
3 |
BELKIN M, NIYOGI P. Laplacian eigenmaps and spectral techniques for embedding and clustering[C]// Proceedings of the 14th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2001: 585-591. 10.7551/mitpress/1120.003.0080
|
4 |
HE X F, NIYOGI P. Locality preserving projections[C]// Proceedings of the 16th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2003: 153-160.
|
5 |
AHMED A, SHERVASHIDZE N, NARAYANAMURTHY S, et al. Distributed large-scale natural graph factorization[C]// Proceedings of the 22nd International Conference on World Wide Web. New York: ACM, 2013: 37-48. 10.1145/2488388.2488393
|
6 |
CAO S S, LU W, XU Q K. GraRep: learning graph representations with global structural information[C]// Proceedings of the 24th ACM International Conference on Information and Knowledge Management. New York: ACM, 2015: 891-900. 10.1145/2806416.2806512
|
7 |
OU M D, CUI P, PEI J, et al. Asymmetric transitivity preserving graph embedding[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2016: 1105-1114. 10.1145/2939672.2939751
|
8 |
PEROZZI B, Al-RFOU R, SKIENA S. DeepWalk: online learning of social representations[C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2014: 701-710. 10.1145/2623330.2623732
|
9 |
GROVER A, LESKOVEC J. node2vec: scalable feature learning for networks[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2016: 855-864. 10.1145/2939672.2939754
|
10 |
PEROZZI B, KULKARNI V, CHEN H C, et al. Don’t walk, skip! online learning of multi-scale network embeddings[EB/OL]. (2017-06-24) [2021-06-01].. 10.1145/3110025.3110086
|
11 |
CHAMBERLAIN B P, CLOUGH J R, DEISENROTH M P. Neural embeddings of graphs in hyperbolic space [EB/OL]. (2017-05-29) [2021-06-01]..
|
12 |
TIAN F, GAO B, CUI Q, et al. Learning deep representations for graph clustering[C]// Proceedings of the 28th AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2014:1293-1299.
|
13 |
WANG D X, CUI P, ZHU W W. Structural deep network embedding[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2016: 1225-1234. 10.1145/2939672.2939753
|
14 |
CAO S S, LU W, XU Q K. Deep neural networks for learning graph representations[C]// Proceedings of the 30th AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2016:1145-1152.
|
15 |
TANG J, QU M, WANG M Z, et al. LINE: large-scale information network embedding[C]// Proceedings of the 24th International Conference on World Wide Web. Republic and Canton of Geneva: International World Wide Web Conferences Steering Committee, 2015: 1067-1077. 10.1145/2736277.2741093
|
16 |
祁志卫,王笳辉,岳昆,等. 图嵌入方法与应用:研究综述[J]. 电子学报, 2020, 48(4):808-818. 10.3969/j.issn.0372-2112.2020.04.023
|
|
QI Z W, WANG J H, YUE K, et al. Methods and applications of graph embedding: a survey[J]. Acta Electronica Sinica, 2020, 48(4):808-818. 10.3969/j.issn.0372-2112.2020.04.023
|
17 |
HUANG X, LI J D, HU X. Label informed attributed network embedding[C]// Proceedings of the 10th ACM International Conference on Web Search and Data Mining. New York: ACM, 2017: 731-739. 10.1145/3018661.3018667
|
18 |
HAMILTON W L, YING R, LESKOVEC J. Inductive representation learning on large graphs[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2017: 1025-1035. 10.1145/3219819.3219890
|
19 |
VELIČKOVIĆ P, CUCURULL G, CASANOVA A, et al. Graph attention networks[EB/OL]. (2018-02-04) [2021-06-01]..
|
20 |
YANG C, LIU Z Y, ZHAO D L, et al. Network representation learning with rich text information[C]// Proceedings of the 24th International Joint Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2015: 2111-2117.
|
21 |
HUANG X, LI J D, HU X. Accelerated attributed network embedding[C]// Proceedings of the 2017 SIAM International Conference on Data Mining. Philadelphia, PA: SIAM, 2017: 633-641. 10.1137/1.9781611974973.71
|
22 |
KIPF T N, WELLING M. Variational graph auto-encoders[EB/OL]. (2016-11-21) [2021-06-01]..
|
23 |
PAN S R, HU R Q, LONG G D, et al. Adversarially regularized graph autoencoder for graph embedding[C]// Proceedings of the 27th International Joint Conference on Artificial Intelligence. California: ijcai.org, 2018: 2609-2615. 10.24963/ijcai.2018/362
|
24 |
WANG C, PAN S R, LONG G D, et al. MGAE: marginalized graph autoencoder for graph clustering[C]// Proceedings of the 2017 ACM Conference on Information and Knowledge Management. New York: ACM, 2017: 889-898. 10.1145/3132847.3132967
|
25 |
GAO H C, HUANG H. Deep attributed network embedding[C]// Proceedings of the 27th International Joint Conference on Artificial Intelligence. California: ijcai.org, 2018:3364-3370. 10.24963/ijcai.2018/467
|
26 |
MONTI F, BOSCAINI D, MASCI J, et al. Geometric deep learning on graphs and manifolds using mixture model CNNs[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 5425-5434. 10.1109/cvpr.2017.576
|
27 |
KINGMA D P, BA J L. Adam: a method for stochastic optimization[EB/OL]. (2017-01-30) [2021-06-01]..
|
28 |
MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[C]// Proceedings of the 26th International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2013: 3111-3119.
|
29 |
ZHU X J, GHAHRAMANI Z. Learning from labeled and unlabeled data with label propagation[R]. Pittsburgh, PA: Carnegie Mellon University, 2002:237-244.
|
30 |
VELIČKOVIĆ P, FEDUS W, HAMILTON W L, et al. Deep graph infomax[EB/OL]. [2021-06-01]..
|