| [1] SHEIKH N T, KEFATO Z T, MONTRESOR A. Semi-supervised Heterogeneous Information Network Embedding for Node Classification Using 1D-CNN[C/OL]. [2019-02-25]. http://disi.unitn.it/~montreso/pubs/papers/snams18b.pdf.
[2] XU G L, WANG X K, WANG Y, et al. Edge-Nodes Representation Neural Machine for Link Prediction. Algorithms, 2019, 12(1). DOI: 10.3390/a12010012.
[3] BHATIA V, RANI R. A Distributed Overlapping Community Detection Model for Large Graphs Using Autoencoder. Future Generation Computer Systems, 2019, 94: 16-26.
[4] 涂存超,杨 成,刘知远,等.网络表示学习综述.中国科学(信息科学), 2017, 47(8): 980-996.
(TU C C, YANG C, LIU Z Y, et al. Network Representation Learning: An Overview. Scientia Sinica Informationis, 2017, 47(8): 980-996.)
[5] MEYER-BRÖTZ F, SCHIEBEL E, BRECHT L. Experimental Eva-luation of Parameter Settings in Calculation of Hybrid Similarities: Effects of First- and Second-Order Similarity, Edge Cutting, and Weighting Factors. Scientometrics, 2017, 111(3): 1307-1325.
[6] ZHANG H G, KONIUSZ P. Power Normalizing Second-Order Similarity Network for Few-Shot Learning[C/OL]. [2019-02-25]. https://arxiv.org/pdf/1811.04167.pdf.
[7] SONG G F. The Role of Structure and Content in Perception of Vi-sual Similarity between Web Pages. International Journal of Human-Computer Interaction, 2011, 27(8): 793-816.
[8] ZHANG J P, DING X Y, YANG J. Revealing the Role of Node Similarity and Community Merging in Community Detection. Know-ledge Based Systems, 2019, 165: 407-419.
[9] HAJIZADEH R, AGHAGOLZADEH A, EZOJI M. Mutual Neighbors and Diagonal Loading-Based Sparse Locally Linear Embedding. Applied Artificial Intelligence, 2018, 32(5): 496-514.
[10] MA M H, DENG T Q, WANG N, et al. Semi-supervised Rough Fuzzy Laplacian Eigenmaps for Dimensionality Reduction. International Journal of Machine Learning and Cybernetics, 2019, 10(2): 397-411.
[11] PEROZZI B, AL-RFOU R, SKIENA S. Deepwalk: Online Lear-ning of Social Representations // Proc of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2014: 701-710.
[12] BRA?INSKAWS A, HAVRYLOV S, TITOV I. Embedding Words as Distributions with a Bayesian Skip-Gram Model // Proc of the 27th International Conference on Computational Linguistics. Stroudsburg, USA: ACL, 2018: 1775-1789.
[13] TANG J, QU M, WANG M Z, et al. LINE: Large-Scale Information Network Embedding // Proc of the 24th International Confe-rence on World Wide Web. New York, USA: ACM, 2015: 1067-1077.
[14] CAO S S, LU W, XU Q K. GraRep: Learning Graph Representations with Global Structural Information // Proc of the 24th ACM Internatio-nal Conference on Information and Knowledge Management. New York, USA: ACM, 2015: 891-900.
[15] GROVER A, LESKOVEC J. Node2vec: Scalable Feature Learning for Networks // Proc of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2016: 855-864.
[16] CAO S S, LU W, XU Q K. Deep Neural Networks for Learning Graph Representations // Proc of the 30th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI, 2016: 1145-1152.
[17] FAN X N, ZHANG S W, ZHANG S Y, et al. Prediction of lncRNA-Disease Associations by Integrating Diverse Heterogeneous Information Sources with RWR Algorithm and Positive Pointwise Mutual Information. BMC Bioinformatics, 2019, 20(1): 871-883.
[18] VINCENT P, LAROCHELLE H, LAJOIE I, et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. Journal of Machine Learning Research, 2010, 11: 3371-3408.
[19] KIPF T N, WELLING M. Semi-supervised Classification with Graph Convolutional Networks[C/OL]. [2019-02-25]. https://arxiv.org/pdf/1609.02907.pdf.
[20] HAMILTON W L, YING R, LESKOVEC J. Inductive Representation Learning on Large Graphs[C/OL]. [2019-02-25]. https://arxiv.org/pdf/1706.02216.pdf.
[21] WANG H W, WANG J, WANG J L, et al. GraphGAN: Graph Representation Learning with Generative Adversarial Nets[C/OL]. [2019-02-25]. https://arxiv.org/pdf/1711.08267.pdf.
[22] 赵 姝,赵 晖,陈 洁,等.基于社团结构的多粒度结构洞占据者发现及分析.智能系统学报, 2016, 11(3): 343-351.
(ZHAO S, ZHAO H, CHEN J, et al. Recognition and Analysis of Structural Hole Spanner in Multi-granularity Based on Community Structure. CAAI Transactions on Intelligent Systems, 2016, 11(3): 343-351.)
[23] 张燕平,张 铃,吴 涛.不同粒度世界的描述法——商空间法.计算机学报, 2004, 27(3): 328-333.
(ZHANG Y P, ZHANG L, WU T. The Representation of Different Granular Worlds: A Quotient Space. Chinese Journal of Compu-ters, 2004, 27(3): 328-333.)
[24] 赵 姝,柯 望,陈 洁,等.基于聚类粒化的社团发现算法.计算机应用, 2014, 34(10): 2812-2815.
(ZHAO S, KE W, CHEN J, et al. Community Detection Algorithm Based on Clustering Granulation. Journal of Computer Applications, 2014, 34(10): 2812-2815.)
[25] SARKAR P, CHAKRABARTI D, BICKEL P. The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels // Proc of the 28th International Conference on Neural Information Processing Systems. Cambridge, USA: The MIT Press, 2015, II: 3016-3024.
[26] BAG S, KUMAR S K, TIWARI M K. An Efficient Recommendation Generation Using Relevant Jaccard Similarity. Information Sciences, 2019, 483: 53-64.
[27] HAMERS L, HEMERYCK Y, HERWEYERS G, et al. Similarity Measures in Scientometric Research: The Jaccard Index Versus Salton′s Cosine Formula. Information Processing and Management, 1989, 25(3): 315-318.
[28] ADAMIC L A, LENTO T M, ADAR E, et al. Information Evolution in Social Networks // Proc of the 9th ACM International Conference on Web Search and Data Mining. New York, USA: ACM, 2016: 473-482.
[29] MCCALLUM A K, NIGAM K, RENNIE J, et al. Automating the Construction of Internet Portals with Machine Learning. Information Retrieval, 2000, 3(2): 127-163.
[30] GAO H C, HUANG H. Self-paced Network Embedding // Proc of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2018: 1406-1415.
[31] WU W, LI B, CHEN L, et al. Efficient Attributed Network Embedding via Recursive Randomized Hashing // Proc of the 27th International Joint Conference on Artificial Intelligence. Hackensack, USA: World Scientific, 2018: 2861-2867.
[32] TANG L, LIU H. Relational Learning via Latent Social Dimensions // Proc of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2009: 817-826.
[33] FAWCETT T. An Introduction to ROC Analysis. Pattern Recognition Letters, 2006, 27(8): 861-874. |
2019, Vol. 32 
