|
Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (6): 1217-1229.doi: 10.1007/s11390-019-1971-3
Special Issue: Data Management and Data Mining
• Data Management and Data Mining • Previous Articles Next Articles
Chun-Yang Ruan1,2, Ye Wang3, Jiangang Ma4, Yanchun Zhang1,2,5, Xin-Tian Chen1,2
[1] Chen Y X, Wang C G. HINE:Heterogeneous information network embedding. In Proc. the 22nd International Conference on Database Systems for Advanced Applications, March 2017, pp.180-195. [2] Meng C P, Cheng R, Maniu S, Senellart P, Zhang W D. Discovering meta-paths in large heterogeneous information networks. In Proc. the 24th International Conference on World Wide Web, May 2015, pp.754-764. [3] Shi C, Hu B B, Zhao X, Yu P. Heterogeneous information network embedding for recommendation. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(2):357-370. [4] Chen H X, Yin H Z, Wang W Q, Wang H, Nguyen Q V H, Li X. PME:Projected metric embedding on heterogeneous networks for link prediction. In Proc. the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, August 2018, pp.1177-1186. [5] Fu T Y, Lee W C, Lei Z. HIN2Vec:Explore meta-paths in heterogeneous information networks for representation learning. In Proc. the 2017 ACM on Conference on Information and Knowledge Management, November 2017, pp.1797-1806. [6] Wang H W, Zhang F Z, Hou M, Xie X, Guo M Y, Liu Q. SHINE:Signed heterogeneous information network embedding for sentiment link prediction. In Proc. the 11th ACM International Conference on Web Search and Data Mining, February 2018, pp.592-600. [7] Shi Y, Gui H, Zhu Q, Kaplan L M, Han J W. AspEm:Embedding learning by aspects in heterogeneous information networks. In Proc. the 2018 SIAM International Conference on Data Mining, May 2018, pp.144-152. [8] Dai Q Y, Li Q, Tang J, Wang D. Adversarial network embedding. In Proc. the 32nd AAAI Conference on Artificial Intelligence, February 2018, pp.2167-2174. [9] Huang Z P, Mamoulis N. Heterogeneous information network embedding for meta path based proximity. arXiv:1701.05291, 2017. https://arxiv.org/pdf/17-01.05291.pdf, August 2019. [10] Domhan T. How much attention do you need? A granular analysis of neural machine translation architectures. In Proc. the 56th Annual Meeting of the Association for Computational Linguistics, July 2018, pp.1799-1808. [11] Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, WardeFarley D, Ozair S, Courville A C, Bengio Y. Generative adversarial nets. In Proc. the 2014 Annual Conference on Neural Information Processing Systems, December 2014, pp.2672-2680. [12] Sun Y Z, Norick B, Han J W, Yan X F, Yu P S, Yu X. PathSelClus:Integrating meta-path selection with userguided object clustering in heterogeneous information networks. ACM Transactions on Knowledge Discovery from Data, 2013, 7(3):Article No. 11. [13] Li J H, Wang C D, Huang L, Huang D, Lai J H, Chen P. Attributed network embedding with micro-meso structure. In Proc. the 23rd International Conference on Database Systems for Advanced Applications, May 2018, pp.20-36. [14] Wang H W, Wang J, Wang J L, Zhao M, Zhang W N, Zhang F Z, Xie X, Guo M Y. GraphGAN:Graph representation learning with generative adversarial nets. In Proc. the 32nd AAAI Conference on Artificial Intelligence, February 2018, pp.2508-2515. [15] Li C Z, Li Z J, Wang S Z, Yang Y, Zhang X M, Zhou J S. Semi-supervised network embedding. In Proc. the 22nd International Conference on Database Systems for Advanced Application, March 2017, pp.131-147. [16] Tang J, Qu M, Wang M Z, Zhang M, Yan J, Mei Q Z. LINE:Large-scale information network embedding. In Proc. the 24th International Conference on World Wide Web, May 2015, pp.1067-1077. [17] Grover A, Leskovec J. node2vec:Scalable feature learning for networks. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, pp.855-864. [18] Cheng D W, Tu Y, Ma Z W, Niu Z B, Zhang L Q. BHONEM:Binary high-order network embedding methods for networked-guarantee loans. J. Comput. Sci. Technol., 2019, 34(3):657-669. [19] Chang S Y, Han W, Tang J L, Qi G J, Aggarwal C C, Huang T S. Heterogeneous network embedding via deep architectures. In Proc. the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2015, pp.119-128. [20] Guo L, Wen Y F, Wang X H. Exploiting pre-trained network embeddings for recommendations in social networks. J. Comput. Sci. Technol., 2018, 33(4):682-696. [21] Zhou D F, Fan J X, Lin C K, Cheng B L, Zhou J Y, Liu Z. Optimal path embedding in the exchanged crossed cube. J. Comput. Sci. Technol., 2017, 32(3):618-629. [22] Dong Y X, Chawla N V, Swami A. Metapath2vec:Scalable representation learning for heterogeneous networks. In Proc. the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2017, pp.135-144. [23] Ji H Y, Shi C, Wang B. Attention based meta path fusion for heterogeneous information network embedding. In Proc. the 15th Pacific Rim International Conference on Artificial Intelligence, August 2018, pp.348-360. [24] Wang X, Ji H Y, Shi C, Wang B, Ye Y F, Cui P, Yu P S. Heterogeneous graph attention network. In Proc. the 2019 World Wide Web Conference, May 2019, pp.2022-2032. [25] Fang Y, Lin W Q, Zheng V W C, Wu M, Chang K C C, Li X L. Semantic proximity search on graphs with metagraph-based learning. In Proc. the 32nd IEEE International Conference on Data Engineering, May 2016, pp.277-288. [26] Zhang D K, Yin J, Zhu X G, Zhang C Q. Metagraph2vec:Complex semantic path augmented heterogeneous network embedding. In Proc. the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, June 2018, pp.196-208. [27] Sun L C, He L F, Huang Z P, Cao B K, Xia C Y, Wei X K, Yu P S. Joint embedding of meta-path and meta-graph for heterogeneous information networks. In Proc. the 2018 IEEE International Conference on Big Knowledge, November 2018, pp.131-138. [28] Donahue J, Krähenbühl P, Darrell T. Adversarial feature learning. In Proc. the 5th International Conference on Learning Representations, April 2017. [29] Bousmalis K, Silberman N, Dohan D, Erhan D, Krishnan D. Unsupervised pixel-level domain adaptation with generative adversarial networks. In Proc. the 2017 IEEE Conference on Computer Vision and Pattern Recognition, July 2017, pp.95-104. [30] Li H Y, Dong W M, Hu B G. Facial image attributes transformation via conditional recycle generative adversarial networks. J. Comput. Sci. Technol., 2018, 33(3):511-521. [31] Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. In Proc. the 4th International Conference on Learning Representations, May 2016. [32] Xiao Y, Xiao D, Hu B B, Shi C. ANE:Network embedding via adversarial autoencoders. In Proc. the 2018 IEEE International Conference on Big Data and Smart Computing, January 2018, pp.66-73. [33] Wang D X, Cui P, Zhu W W. Structural deep network embedding. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, pp.1225-1234. [34] Gulrajani I, Ahmed Fa, Arjovsky M, Dumoulin V, Courville A C. Improved training of Wasserstein GANs. In Proc. the 2017 Annual Conference on Neural Information Processing Systems, December 2017, pp.5769-5779. [35] Sun Y Z, Han J W, Yan X F, Yu P S, Wu T Y. PathSim:Meta path-based top-k similarity search in heterogeneous information networks. Proceedings of the VLDB Endowment, 2011, 4(11):992-1003. [36] Perozzi B, Al-Rfou' R, Skiena S. DeepWalk:Online learning of social representations. In Proc. the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2014, pp.701-710. [37] Tang J, Zhang J, Yao L M, Li J Z, Zhang L, Su Z. ArnetMiner:Extraction and mining of academic social networks. In Proc. the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2008, pp.990-998. |
[1] | Dan-Hao Zhu, Xin-Yu Dai, Jia-Jun Chen. Pre-Train and Learn: Preserving Global Information for Graph Neural Networks [J]. Journal of Computer Science and Technology, 2021, 36(6): 1420-1430. |
[2] | Jia-Ke Ge, Yan-Feng Chai, Yun-Peng Chai. WATuning: A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning [J]. Journal of Computer Science and Technology, 2021, 36(4): 741-761. |
[3] | Chen-Chen Sun, De-Rong Shen. Mixed Hierarchical Networks for Deep Entity Matching [J]. Journal of Computer Science and Technology, 2021, 36(4): 822-838. |
[4] | Yang Liu, Ruili He, Xiaoqian Lv, Wei Wang, Xin Sun, Shengping Zhang. Is It Easy to Recognize Baby's Age and Gender? [J]. Journal of Computer Science and Technology, 2021, 36(3): 508-519. |
[5] | Ying Li, Jia-Jie Xu, Peng-Peng Zhao, Jun-Hua Fang, Wei Chen, Lei Zhao. ATLRec: An Attentional Adversarial Transfer Learning Network for Cross-Domain Recommendation [J]. Journal of Computer Science and Technology, 2020, 35(4): 794-808. |
[6] | Yi-Ting Wang, Jie Shen, Zhi-Xu Li, Qiang Yang, An Liu, Peng-Peng Zhao, Jia-Jie Xu, Lei Zhao, Xun-Jie Yang. Enriching Context Information for Entity Linking with Web Data [J]. Journal of Computer Science and Technology, 2020, 35(4): 724-738. |
[7] | Chao Kong, Bao-Xiang Chen, Li-Ping Zhang. DEM: Deep Entity Matching Across Heterogeneous Information Networks [J]. Journal of Computer Science and Technology, 2020, 35(4): 739-750. |
[8] | Yue Kou, De-Rong Shen, Dong Li, Tie-Zheng Nie, Ge Yu. Finding Communities by Decomposing and Embedding Heterogeneous Information Network [J]. Journal of Computer Science and Technology, 2020, 35(2): 320-337. |
[9] | Da-Wei Cheng, Yi Tu, Zhen-Wei Ma, Zhi-Bin Niu, Li-Qing Zhang. BHONEM: Binary High-Order Network Embedding Methods for Networked-Guarantee Loans [J]. Journal of Computer Science and Technology, 2019, 34(3): 657-669. |
[10] | Yifan Wu, Fan Yang, Yong Xu, Haibin Ling. Privacy-Protective-GAN for Privacy Preserving Face De-Identification [J]. Journal of Computer Science and Technology, 2019, 34(1): 47-60. |
[11] | Lei Guo, Yu-Fei Wen, Xin-Hua Wang. Exploiting Pre-Trained Network Embeddings for Recommendations in Social Networks [J]. , 2018, 33(4): 682-696. |
[12] | Huai-Yu Li, Wei-Ming Dong, Bao-Gang Hu. Facial Image Attributes Transformation via Conditional Recycle Generative Adversarial Networks [J]. , 2018, 33(3): 511-521. |
[13] | Sheng Zhang, Zhu-Zhong Qian, Jie Wu, Sang-Lu Lu. Service-Oriented Resource Allocation in Clouds: Pursuing Flexibility and Efficiency [J]. , 2015, 30(2): 421-436. |
|
|