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PANE: scalable and effective attributed network embedding

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Abstract

Given a graph G where each node is associated with a set of attributes, attributed network embedding (ANE) maps each node \(v \in G\) to a compact vector \(X_v\), which can be used in downstream machine learning tasks. Ideally, \(X_v\) should capture node v’s affinity to each attribute, which considers not only v’s own attribute associations, but also those of its connected nodes along edges in G. It is challenging to obtain high-utility embeddings that enable accurate predictions; scaling effective ANE computation to massive graphs with millions of nodes pushes the difficulty of the problem to a whole new level. Existing solutions largely fail on such graphs, leading to prohibitive costs, low-quality embeddings, or both. This paper proposes \(\texttt {PANE}\), an effective and scalable approach to ANE computation for massive graphs that achieves state-of-the-art result quality on multiple benchmark datasets, measured by the accuracy of three common prediction tasks: attribute inference, link prediction, and node classification. \(\texttt {PANE}\) obtains high scalability and effectiveness through three main algorithmic designs. First, it formulates the learning objective based on a novel random walk model for attributed networks. The resulting optimization task is still challenging on large graphs. Second, \(\texttt {PANE}\) includes a highly efficient solver for the above optimization problem, whose key module is a carefully designed initialization of the embeddings, which drastically reduces the number of iterations required to converge. Finally, \(\texttt {PANE}\) utilizes multi-core CPUs through non-trivial parallelization of the above solver, which achieves scalability while retaining the high quality of the resulting embeddings. The performance of \(\texttt {PANE}\) depends upon the number of attributes in the input network. To handle large networks with numerous attributes, we further extend \(\texttt {PANE}\) to \(\texttt{PANE}^{++}\), which employs an effective attribute clustering technique. Extensive experiments, comparing 10 existing approaches on 8 real datasets, demonstrate that \(\texttt {PANE}\) and \(\texttt{PANE}^{++}\) consistently outperform all existing methods in terms of result quality, while being orders of magnitude faster.

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Notes

  1. The work reported here is an extended version of [92, 93].

  2. In the degenerate case that \(v_l\) is not associated with any attribute, e.g., \(v_1\) in Fig. 1, we simply restart the random walk from the source node \(v_i\) and repeat the process.

  3. The PMI quantifies how much more or less likely we are to see the two events co-occur, given their individual probabilities, and relative to the case where they are completely independent.

  4. http://linqs.soe.ucsc.edu/data

  5. https://github.com/mengzaiqiao/CAN

  6. http://snap.stanford.edu/data

  7. https://www.kaggle.com/c/kddcup2012-track1

  8. http://ma-graph.org/rdf-dumps/

  9. https://figshare.com/articles/dataset/mag_scholar/12696653

References

  1. Arora, S., Ge, R., Kannan, R., Moitra, A.: Computing a nonnegative matrix factorization-provably. STOC, pp. 145–161 (2012)

  2. Bandyopadhyay, S., Vivek, S.V., Murty, M.: Outlier resistant unsupervised deep architectures for attributed network embedding. WSDM, pp. 25–33 (2020). https://doi.org/10.1145/3336191.3371788

  3. Bielak, P., Tagowski, K., Falkiewicz, M., Kajdanowicz, T., Chawla, N..V.: FILDNE: A framework for incremental learning of dynamic networks embeddings. Knowl. Based Syst 236, 107–453 (2022). https://doi.org/10.1016/j.knosys.2021.107453

  4. Bojchevski, A., Klicpera, J., Perozzi, B., Kapoor, A., Blais, M., Rózemberczki, B., Lukasik, M., Günnemann, S.: Scaling graph neural networks with approximate pagerank. In: KDD, pp. 2464–2473 (2020). https://doi.org/10.1145/3394486.3403296

  5. Bottou, L.: Large-scale machine learning with stochastic gradient descent. COMPSTAT pp. 177–186 (2010). https://doi.org/10.1007/978-3-7908-2604-3_16

  6. Cen, Y., Zou, X., Zhang, J., Yang, H., Zhou, J., Tang, J.: Representation learning for attributed multiplex heterogeneous network. KDD pp. 1358–1368 (2019). https://doi.org/10.1145/3292500.3330964

  7. Chang, S., Han, W., Tang, J., Qi, G.J., Aggarwal, C.C., Huang, T.S.: Heterogeneous network embedding via deep architectures. KDD pp. 119–128 (2015). https://doi.org/10.1145/2783258.2783296

  8. Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. Comput. Linguist., pp. 22–29 (1990)

  9. Comon, P., Luciani, X., De Almeida, A.L.: Tensor decompositions, alternating least squares and other tales. J. Chemom., pp. 393–405 (2009)

  10. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    Article  MATH  Google Scholar 

  11. Davison, M.L.: Introduction to Multidimensional Scaling (1983)

  12. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the em algorithm. J. R. Stat. Soc. 39(1), 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  13. Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: scalable representation learning for heterogeneous networks. In: KDD, pp. 135–144. ACM (2017). https://doi.org/10.1145/3097983.3098036

  14. Dong, Y., Hu, Z., Wang, K., Sun, Y., Tang, J.: Heterogeneous network representation learning. In: C. Bessiere (ed.) IJCAI, pp. 4861–4867. ijcai.org (2020). https://doi.org/10.24963/ijcai.2020/677

  15. Du, L., Wang, Y., Song, G., Lu, Z., Wang, J.: Dynamic network embedding : An extended approach for skip-gram based network embedding. In: J. Lang (ed.) IJCAI, pp. 2086–2092. ijcai.org (2018). https://doi.org/10.24963/ijcai.2018/288

  16. Duan, Z., Sun, X., Zhao, S., Chen, J., Zhang, Y., Tang, J.: Hierarchical community structure preserving approach for network embedding. Inf. Sci. 546, 1084–1096 (2021). https://doi.org/10.1016/j.ins.2020.09.053

  17. Fu, T., Lee, W., Lei, Z.: Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning. In: E. Lim, M. Winslett, M. Sanderson, A.W. Fu, J. Sun, J.S. Culpepper, E. Lo, J.C. Ho, D. Donato, R. Agrawal, Y. Zheng, C. Castillo, A. Sun, V.S. Tseng, C. Li (eds.) CIKM, pp. 1797–1806. ACM (2017). https://doi.org/10.1145/3132847.3132953

  18. Gao, H., Huang, H.: Deep attributed network embedding. IJCAI pp. 3364–3370 (2018). https://doi.org/10.24963/ijcai.2018/467

  19. Gao, H., Pei, J., Huang, H.: Progan: Network embedding via proximity generative adversarial network. KDD pp. 1308–1316 (2019). https://doi.org/10.1145/3292500.3330866

  20. Golub, G.H., Reinsch, C.: Singular value decomposition and least squares solutions. Linear Algebra, pp. 134–151 (1971)

  21. Golub, G.H., Van Loan, C.F.: Matrix Computations, 1996. Johns Hopkins University, Press, Baltimore, MD, USA (1996)

  22. Gong, M., Chen, C., Xie, Y., Wang, S.: Community preserving network embedding based on memetic algorithm. TETCI 4(2), 108–118 (2020). https://doi.org/10.1109/TETCI.2018.2866239

  23. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT press (2016)

  24. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. NeurIPS pp. 2672–2680 (2014)

  25. Goyal, P., Kamra, N., He, X., Liu, Y.: Dyngem: Deep embedding method for dynamic graphs. CoRR abs/1805.11273 (2018). http://arxiv.org/abs/1805.11273

  26. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

  27. Guo, X., Zhou, B., Skiena, S.: Subset node representation learning over large dynamic graphs. In: F. Zhu, B.C. Ooi, C. Miao (eds.) KDD, pp. 516–526. ACM (2021). https://doi.org/10.1145/3447548.3467393

  28. Hagen, L., Kahng, A..B.: New spectral methods for ratio cut partitioning and clustering. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst 11(9), 1074–1085 (1992). https://doi.org/10.1109/43.159993

  29. Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. NeurIPS, pp. 1025–1035 (2017)

  30. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput., pp. 1735–1780 (1997)

  31. Holland, P.W., Laskey, K.B., Leinhardt, S.: Stochastic blockmodels: first steps. Soc. Netw 5(2), 109–137 (1983)

    Article  MathSciNet  Google Scholar 

  32. Hou, Y., Chen, H., Li, C., Cheng, J., Yang, M.C.: A representation learning framework for property graphs. KDD pp. 65–73 (2019). https://doi.org/10.1145/3292500.3330948

  33. Huang, W., Li, Y., Fang, Y., Fan, J., Yang, H.: Biane: Bipartite attributed network embedding. In: SIGIR, pp. 149–158 (2020). https://doi.org/10.1145/3397271.3401068

  34. Huang, X., Li, J., Hu, X.: Accelerated attributed network embedding. SDM, pp. 633–641 (2017). https://doi.org/10.1137/1.9781611974973.71

  35. Hussein, R., Yang, D., Cudré-Mauroux, P.: Are meta-paths necessary?: Revisiting heterogeneous graph embeddings. In: A. Cuzzocrea, J. Allan, N.W. Paton, D. Srivastava, R. Agrawal, A.Z. Broder, M.J. Zaki, K.S. Candan, A. Labrinidis, A. Schuster, H. Wang (eds.) CIKM, pp. 437–446. ACM (2018). https://doi.org/10.1145/3269206.3271777

  36. Jeh, G., Widom, J.: Scaling personalized web search. TheWebConf, pp. 271–279 (2003). https://doi.org/10.1145/775152.775191

  37. Jin, D., Li, B., Jiao, P., He, D., Zhang, W.: Network-specific variational auto-encoder for embedding in attribute networks. IJCAI, pp. 2663–2669 (2019). https://doi.org/10.24963/ijcai.2019/370

  38. Kaggle: Kdd cup (2012). https://www.kaggle.com/c/kddcup2012-track1

  39. Kanatsoulis, C.I., Sidiropoulos, N.D.: Gage: Geometry preserving attributed graph embeddings. In: WSDM, pp. 439–448 (2022). https://doi.org/10.1145/3488560.3498467

  40. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ICLR (2016)

  41. Lerer, A., Wu, L., Shen, J., Lacroix, T., Wehrstedt, L., Bose, A., Peysakhovich, A.: PyTorch-BigGraph: a large-scale graph embedding system. SysML, pp. 120–131 (2019)

  42. Leskovec, J., Mcauley, J.J.: Learning to discover social circles in ego networks. NeurIPS, pp. 539–547 (2012)

  43. Li, J., Huang, L., Wang, C., Huang, D., Lai, J., Chen, P.: Attributed network embedding with micro-meso structure. TKDD 15(4), 72:1-72:26 (2021). https://doi.org/10.1145/3441486

    Article  Google Scholar 

  44. Li, Z., Zheng, W., Lin, X., Zhao, Z., Wang, Z., Wang, Y., Jian, X., Chen, L., Yan, Q., Mao, T.: Transn: Heterogeneous network representation learning by translating node embeddings. In: ICDE, pp. 589–600. IEEE (2020). https://doi.org/10.1109/ICDE48307.2020.00057

  45. Liang, X., Li, D., Madden, A.: Attributed network embedding based on mutual information estimation. In: M. d’Aquin, S. Dietze, C. Hauff, E. Curry, P. Cudré-Mauroux (eds.) CIKM, pp. 835–844. ACM (2020). https://doi.org/10.1145/3340531.3412008

  46. Liao, L., He, X., Zhang, H., Chua, T..S.: Attributed social network embedding. TKDE 30(12), 2257–2270 (2018). https://doi.org/10.1109/TKDE.2018.2819980

    Article  Google Scholar 

  47. Liu, J., He, Z., Wei, L., Huang, Y.: Content to node: Self-translation network embedding. KDD, pp. 1794–1802 (2018). https://doi.org/10.1145/3219819.3219988

  48. Liu, X., Yang, B., Song, W., Musial, K., Zuo, W., Chen, H., Yin, H.: A block-based generative model for attributed network embedding. World Wide Web 24(5), 1439–1464 (2021). https://doi.org/10.1007/s11280-021-00918-y

    Article  Google Scholar 

  49. Liu, Z., Huang, C., Yu, Y., Dong, J.: Motif-preserving dynamic attributed network embedding. In: TheWebConf, pp. 1629–1638 (2021)

  50. Lutkepohl, H.: Handbook of matrices. Comput. Stat. Data Anal. 2(25), 243 (1997)

    Google Scholar 

  51. Ma, J., Cui, P., Wang, X., Zhu, W.: Hierarchical taxonomy aware network embedding. KDD, pp. 1920–1929 (2018). https://doi.org/10.1145/3219819.3220062

  52. Mahdavi, S., Khoshraftar, S., An, A.: dynnode2vec: Scalable dynamic network embedding. In: N. Abe, H. Liu, C. Pu, X. Hu, N.K. Ahmed, M. Qiao, Y. Song, D. Kossmann, B. Liu, K. Lee, J. Tang, J. He, J.S. Saltz (eds.) IEEE BigData, pp. 3762–3765. IEEE (2018). https://doi.org/10.1109/BigData.2018.8621910

  53. Meng, Z., Liang, S., Bao, H., Zhang, X.: Co-embedding attributed networks. WSDM, pp. 393–401 (2019). https://doi.org/10.1145/3289600.3291015

  54. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  55. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. NeurIPS, pp. 3111–3119 (2013)

  56. Musco, C., Musco, C.: Randomized block krylov methods for stronger and faster approximate singular value decomposition. NeurIPS, pp. 1396–1404 (2015)

  57. Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., Zhang, C.: Adversarially regularized graph autoencoder for graph embedding. IJCAI, pp. 2609–2615 (2018). https://doi.org/10.24963/ijcai.2018/362

  58. Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: Online learning of social representations. KDD, pp. 701–710 (2014). https://doi.org/10.1145/2623330.2623732

  59. Qiu, J., Dhulipala, L., Tang, J., Peng, R., Wang, C.: Lightne: a lightweight graph processing system for network embedding. In: SIGMOD, pp. 2281–2289 (2021). https://doi.org/10.1145/3448016.3457329

  60. Qiu, J., Dong, Y., Ma, H., Li, J., Wang, K., Tang, J.: Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. WSDM, pp. 459–467 (2018). https://doi.org/10.1145/3159652.3159706

  61. Rozemberczki, B., Allen, C., Sarkar, R.: Multi-scale attributed node embedding. J. Complex Netw. 9(1), 1–22 (2021). https://doi.org/10.1093/comnet/cnab014

    Article  MathSciNet  MATH  Google Scholar 

  62. Salton, G., McGill, M.J.: Introduction to modern information retrieval (1986)

  63. Sameh, A.H., Wisniewski, J.A.: A trace minimization algorithm for the generalized eigenvalue problem. J. Numer. Anal. 19(6), 1243–1259 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  64. Sheikh, N., Kefato, Z.T., Montresor, A.: A simple approach to attributed graph embedding via enhanced autoencoder. Complex Netw., pp. 797–809 (2019). https://doi.org/10.1007/978-3-030-36687-2_66

  65. Shi, Y., Zhu, Q., Guo, F., Zhang, C., Han, J.: Easing embedding learning by comprehensive transcription of heterogeneous information networks. In: Y. Guo, F. Farooq (eds.) KDD, pp. 2190–2199. ACM (2018). https://doi.org/10.1145/3219819.3220006

  66. Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., Hsu, B.J., Wang, K.: An overview of microsoft academic service (mas) and applications. TheWebConf, pp. 243–246 (2015). https://doi.org/10.1145/2740908.2742839

  67. Strang, G., Strang, G., Strang, G., Strang, G.: Introduction to Linear Algebra, vol. 3. Wellesley-Cambridge Press, Cambridge (1993)

  68. Tang, J., Qu, M., Mei, Q.: PTE: predictive text embedding through large-scale heterogeneous text networks. In: L. Cao, C. Zhang, T. Joachims, G.I. Webb, D.D. Margineantu, G. Williams (eds.) KDD, pp. 1165–1174. ACM (2015). https://doi.org/10.1145/2783258.2783307

  69. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Large-scale information network embedding. TheWebConf, pp. 1067–1077 (2015). https://doi.org/10.1145/2736277.2741093

  70. Tong, H., Faloutsos, C., Pan, J.Y.: Fast random walk with restart and its applications. ICDM, pp. 613–622 (2006). https://doi.org/10.1109/ICDM.2006.70

  71. Tsitsulin, A., Mottin, D., Karras, P., Müller, E.: Verse: Versatile graph embeddings from similarity measures. TheWebConf, pp. 539–548 (2018). https://doi.org/10.1145/3178876.3186120

  72. Tsitsulin, A., Munkhoeva, M., Mottin, D., Karras, P., Oseledets, I., Müller, E.: Frede: anytime graph embeddings. PVLDB 14(6), 1102–1110 (2021). https://doi.org/10.14778/3447689.3447713

    Article  Google Scholar 

  73. Veličković, P., Fedus, W., Hamilton, W.L., Liò, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. ICLR (2019)

  74. Von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)

    Article  MathSciNet  Google Scholar 

  75. Wang, H., Chen, E., Liu, Q., Xu, T., Du, D., Su, W., Zhang, X.: A united approach to learning sparse attributed network embedding. ICDM, pp. 557–566 (2018). https://doi.org/10.1109/ICDM.2018.00071

  76. Wang, J., Qu, X., Bai, J., Li, Z., Zhang, J., Gao, J.: Sages: Scalable attributed graph embedding with sampling for unsupervised learning. TKDE, (01), 1–1 (2022). https://doi.org/10.1109/TKDE.2022.3148272

  77. Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., Yang, S.: Community preserving network embedding. In: S. Singh, S. Markovitch (eds.) AAAI, pp. 203–209. AAAI Press (2017). http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14589

  78. Wang, Y., Duan, Z., Liao, B., Wu, F., Zhuang, Y.: Heterogeneous attributed network embedding with graph convolutional networks. In: AAAI, pp. 10,061–10,062 (2019)

  79. Wright, S.J.: Coordinate descent algorithms. Math. Program., pp. 3–34 (2015)

  80. Wu, J., He, J.: Scalable manifold-regularized attributed network embedding via maximum mean discrepancy. CIKM, pp. 2101–2104 (2019). https://doi.org/10.1145/3357384.3358091

  81. Wu, W., Li, B., Chen, L., Zhang, C.: Efficient attributed network embedding via recursive randomized hashing. IJCAI, pp. 2861–2867 (2018). https://doi.org/10.24963/ijcai.2018/397

  82. Xie, Y., Yu, B., Lv, S., Zhang, C., Wang, G., Gong, M.: A survey on heterogeneous network representation learning. Pattern Recognit. 116, 107–936 (2021). https://doi.org/10.1016/j.patcog.2021.107936

    Article  Google Scholar 

  83. Xue, G., Zhong, M., Li, J., Chen, J., Zhai, C.: Dynamic network embedding survey. Neurocomputing 472, 212–223 (2022). https://doi.org/10.1016/j.neucom.2021.03.138

    Article  Google Scholar 

  84. Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E.: Network representation learning with rich text information. IJCAI, pp. 2111–2117 (2015)

  85. Yang, C., Xiao, Y., Zhang, Y., Sun, Y., Han, J.: Heterogeneous network representation learning: a unified framework with survey and benchmark. TKDE (2020)

  86. Yang, C., Xiao, Y., Zhang, Y., Sun, Y., Han, J.: Heterogeneous network representation learning: a unified framework with survey and benchmark. TKDE 34(10), 4854–4873 (2022). https://doi.org/10.1109/TKDE.2020.3045924

    Article  Google Scholar 

  87. Yang, C., Zhong, L., Li, L.J., Jie, L.: Bi-directional joint inference for user links and attributes on large social graphs. TheWebConf, pp. 564–573 (2017). https://doi.org/10.1145/3041021.3054181

  88. Yang, H., Pan, S., Chen, L., Zhou, C., Zhang, P.: Low-bit quantization for attributed network representation learning. IJCAI, pp. 4047–4053 (2019). https://doi.org/10.24963/ijcai.2019/562

  89. Yang, H., Pan, S., Zhang, P., Chen, L., Lian, D., Zhang, C.: Binarized attributed network embedding. ICDM, pp. 1476–1481 (2018). https://doi.org/10.1109/ICDM.2018.8626170

  90. Yang, J., McAuley, J., Leskovec, J.: Community detection in networks with node attributes. ICDM, pp. 1151–1156 (2013). https://doi.org/10.1109/ICDM.2013.167

  91. Yang, R., Shi, J., Xiao, X., Yang, Y., Bhowmick, S..S.: Homogeneous network embedding for massive graphs via reweighted personalized pagerank. PVLDB 13(5), 670–683 (2020). https://doi.org/10.14778/3377369.3377376

    Article  Google Scholar 

  92. Yang, R., Shi, J., Xiao, X., Yang, Y., Bhowmick, S.S., Liu, J.: No pane, no gain: Scaling attributed network embedding in a single server. ACM SIGMOD Record 51(1), 42–49 (2022)

    Article  Google Scholar 

  93. Yang, R., Shi, J., Xiao, X., Yang, Y., Liu, J., Bhowmick, S..S.: Scaling attributed network embedding to massive graphs. Proc. VLDB Endow. 14(1), 37–49 (2020). https://doi.org/10.14778/3421424.3421430

    Article  Google Scholar 

  94. Yang, R., Shi, J., Yang, Y., Huang, K., Zhang, S., Xiao, X.: Effective and scalable clustering on massive attributed graphs. In: TheWebConf, pp. 3675–3687 (2021). https://doi.org/10.1145/3442381.3449875

  95. Ye, D., Jiang, H., Jiang, Y., Wang, Q., Hu, Y.: Community preserving mapping for network hyperbolic embedding. Knowl. Based Syst. 246, 108–699 (2022). https://doi.org/10.1016/j.knosys.2022.108699

    Article  Google Scholar 

  96. Yin, Y., Wei, Z.: Scalable graph embeddings via sparse transpose proximities. KDD, pp. 1429–1437 (2019). https://doi.org/10.1145/3292500.3330860

  97. Zhang, C., Swami, A., Chawla, N.V.: Shne: Representation learning for semantic-associated heterogeneous networks. In: WSDM, pp. 690–698 (2019). https://doi.org/10.1145/3289600.3291001

  98. Zhang, D., Yin, J., Zhu, X., Zhang, C.: Homophily, structure, and content augmented network representation learning. ICDM, pp. 609–618 (2016). https://doi.org/10.1109/ICDM.2016.0072

  99. Zhang, Z., Cui, P., Li, H., Wang, X., Zhu, W.: Billion-scale network embedding with iterative random projection. ICDM, pp. 787–796 (2018). https://doi.org/10.1109/ICDM.2018.00094

  100. Zhang, Z., Cui, P., Wang, X., Pei, J., Yao, X., Zhu, W.: Arbitrary-order proximity preserved network embedding. KDD, pp. 2778–2786 (2018). https://doi.org/10.1145/3219819.3219969

  101. Zhang, Z., Yang, H., Bu, J., Zhou, S., Yu, P., Zhang, J., Ester, M., Wang, C.: Anrl: Attributed network representation learning via deep neural networks. IJCAI, pp. 3155–3161 (2018). https://doi.org/10.24963/ijcai.2018/438

  102. Zheng, S., Guan, D., Yuan, W.: Semantic-aware heterogeneous information network embedding with incompatible meta-paths. WWW 25(1), 1–21 (2022). https://doi.org/10.1007/s11280-021-00903-5

    Article  Google Scholar 

  103. Zhou, C., Liu, Y., Liu, X., Liu, Z., Gao, J.: Scalable graph embedding for asymmetric proximity. AAAI, pp. 2942–2948 (2017)

  104. Zhou, S., Yang, H., Wang, X., Bu, J., Ester, M., Yu, P., Zhang, J., Wang, C.: Prre: Personalized relation ranking embedding for attributed networks. CIKM, pp. 823–832 (2018). https://doi.org/10.1145/3269206.3271741

  105. Zhu, R., Zhao, K., Yang, H., Lin, W., Zhou, C., Ai, B., Li, Y., Zhou, J.: Aligraph: a comprehensive graph neural network platform. PVLDB 12(12), 2094–2105 (2019). https://doi.org/10.14778/3352063.3352127

    Article  Google Scholar 

  106. Zhu, Z., Xu, S., Tang, J., Qu, M.: Graphvite: A high-performance cpu-gpu hybrid system for node embedding. TheWebConf, pp. 2494–2504 (2019). https://doi.org/10.1145/3308558.3313508

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Acknowledgements

This work is supported by the National University of Singapore SUG grant R-252-000-686-133, Singapore Government AcRF Tier-2 Grant MOE2019-T2-1-029, NPRP grant #NPRP10-0208-170408 from the Qatar National Research Fund (Qatar Foundation), and the financial support of Hong Kong RGC ECS (No. 25201221) and Start-up Fund (P0033898) by PolyU. The findings herein reflect the work, and are solely the responsibility of the authors.

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Authors and Affiliations

  1. Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Renchi Yang

  2. The Hong Kong Polytechnic University, Hung Hom, Hong Kong

    Jieming Shi

  3. National University of Singapore, Singapore, Singapore

    Xiaokui Xiao & Juncheng Liu

  4. Hamad Bin Khalifa University, Al Rayyan, Qatar

    Yin Yang

  5. Nanyang Technological University (NTU), Singapore, Singapore

    Sourav S. Bhowmick

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  1. Renchi Yang
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  2. Jieming Shi
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  3. Xiaokui Xiao
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  4. Yin Yang
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  5. Sourav S. Bhowmick
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  6. Juncheng Liu
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Correspondence to Sourav S. Bhowmick.

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Yang, R., Shi, J., Xiao, X. et al. PANE: scalable and effective attributed network embedding. The VLDB Journal 32, 1237–1262 (2023). https://doi.org/10.1007/s00778-023-00790-4

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