Attributed Bipartite Network Representation Learning

doi:10.3778/j.issn.1673-9418.2004034

Abstract

Abstract:

Existing network embedding models are mostly designed for homogeneous networks or heterogeneous networks, but ignore the special features of bipartite network which arise in recommender systems, search engines, question answering systems and so on. Meanwhile bipartite networks mostly include rich attribute information. To address the above challenging issues, this paper proposes ABNE (attributed bipartite network embedding). Specifically, ABNE first preserves the explicit relations in the bipartite network by decomposing edges into sets of indirect relationships between neighborhood nodes. Then it calculates attribute similarity matrix by cosine similarity and as part of the weight matrix to guide the biased random walk to embed implicit relations and attribute information. Finally, ABNE introduces an optimization framework to obtain the node representation vector which carries both structure information and attribute information. Several tasks have been conducted on four public datasets and compared with other state-of-the-art embedding models. The experimental results show superiority and rationality of ABNE model.

Key words: bipartite network, network representation learning, random walk, attribute network, machine learning

摘要：

现有的网络表示学习算法主要是针对同质网络或异质网络设计的，而忽略了在推荐系统、搜索引擎和问答系统等领域出现的二分网络的特殊特征以及这类网络所携带着的非常丰富的属性信息。为了解决上述问题，提出了一种结合属性信息的二分网络表示学习方法（ABNE）。该方法首先将连边分解成邻居节点间的间接关系集，嵌入显式关系，接着通过余弦相似性引入并定义节点的属性相似度矩阵，并将其作为权重矩阵的一部分指导有偏随机游走，从而嵌入隐式关系和属性信息。最后通过一个联合优化框架得到同时携带网络结构信息和属性信息的节点表示向量。在四个真实公开数据集上进行了推荐任务，并与其他现有方法进行比较，实验结果表明该算法的优越性和合理性。

关键词: 二分网络, 网络表示学习, 随机游走, 属性网络, 机器学习

ZHAO Xueli, LU Guangyue, LV Shaoqing, ZHANG Pan. Attributed Bipartite Network Representation Learning[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(3): 495-505.

赵雪莉, 卢光跃, 吕少卿, 张潘. 结合属性信息的二分网络表示学习[J]. 计算机科学与探索, 2021, 15(3): 495-505.

References

[1] MORRIS S A, YEN G G. Construction of bipartite and uni-partite weighted networks from collections of journal papers[J]. arXiv:physics/0503061, 2005.
[2] BHAGAT S, CORMODE G, MUTHUKRISHNAN S, et al. Node classification in social networks[J]. arXiv:1101.3291, 2011.
[3] LYU L, ZHOU T. Link prediction in complex networks: a survey[J]. Physica A: Statistical Mechanics and Its Applica-tions, 2011, 390(6): 1150-1170.
[4] FORTUNATO S. Community detection in graphs[J]. Physics Reports, 2010, 486: 75-174.
[5] CHEN J, WU Y, FAN L, et al. N2VSCDNNR: a local reco-mmender system based on node2vec and rich information network[J]. IEEE Transactions on Computational Social Sys-tems, 2019, 6(3): 456-466.
[6] SHI C, HU B B, ZHAO W X, et al. Heterogeneous infor-mation network embedding for recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(2): 357-370.
[7] LAURENS V D M, HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9: 2579-2605.
[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 Dis-covery and Data Mining, New York, Aug 24-27, 2014. New York: ACM, 2014: 701-710.
[9] GROVER A, LESKOVEC J. Node2vec: scalable feature lear-ning for networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, Aug 13-17, 2016. New York: ACM, 2016: 855-864.
[10] 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, Florence, May 18-22, 2015. New York: ACM, 2015: 1067-1077.
[11] WANG D X, CUI P, ZHOU W W. Structural deep network embedding[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, Aug 13-17, 2016. New York: ACM, 2016: 1225-1234.
[12] YU L, ZHANG C, PEI S, et al. WalkRanker: a unified pair-wise ranking model with multiple relations for item recom-mendation[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, Feb 2-7, 2018. Menlo Park: AAAI, 2018: 2596-2603.
[13] ALZAHRANI T, HORADAM K J, BOZTAS S. Community detection in bipartite networks using random walks[C]//Pro-ceedings of the 5th Workshop on Complex Networks, Bologna, Mar 12-14, 2014. Berlin, Heidelberg: Springer, 2014: 157-165.
[14] DONG Y X, CHAWLA N V, SWAMI A, et al. Metapath2vec: scalable representation learning for heterogeneous networks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, Aug 13-17, 2017. New York: ACM, 2017: 135-144.
[15] GAO M, CHEN L, HE X, et al. BiNE: bipartite network embedding[C]//Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Infor-mation Retrieval, Ann Arbor, Jul 8-12, 2018. New York: ACM, 2018: 715-724.
[16] GAO M, HE X, CHEN L, et al. Learning vertex represen-tations for bipartite networks[J]. arXiv:1901.09676, 2019.
[17] SYBRANDT J, SAFRO I. FOBE and HOBE: first- and high-order bipartite embeddings[J]. arXiv:1905.10953, 2019.
[18] NARITA A, HAYASHI K, TOMIOKA R, et al. Tensor fac-torization using auxiliary information[J]. Data Mining and Knowledge Discovery, 2012, 25(2): 298-324.
[19] RAFAILIDIS D, NANOPOULOS A. Modeling users prefer-ence dynamics and side information in recommender sys-tems[J]. IEEE Transactions on Systems, Man, and Cyberne-tics: Systems, 2016, 46(6): 782-792.
[20] WANG Z, ZHANG D Q, ZHOU X S, et al. Discovering and profiling overlapping communities in location-based social networks[J]. IEEE Transactions on Systems, Man, and Cybe-rnetics: Systems, 2014, 44(4): 499-509.
[21] XUAN Q, FANG H, FU C, et al. Temporal motifs reveal collaboration patterns in online task-oriented networks[J]. Physical Review E, 2015, 91(5): 52813-52813.
[22] SCHEIN A I, POPESCUL A, UNGAR L H, et al. Methods and metrics for cold-start recommendations[C]//Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Tampere, Aug 11-15, 2002. New York: ACM, 2002: 253-260.
[23] LIAO L Z, HE X N, ZHANG H W, et al. Attributed social network embedding[J]. IEEE Transactions on Knowledge & Data Engineering, 2018, 30(12): 2257-2270.
[24] BEEL J, LANGER S, NURNBERGER A, et al. The impact of demographics (age and gender) and other user-charac-teristics on evaluating recommender systems[C]//LNCS 8092: Proceedings of the International Conference on Theory and Practice of Digital Libraries, Valletta, Sep 22-26, 2013. Berlin, Heidelberg: Springer, 2013: 396-400.
[25] 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, Buenos Aires, Jul 25- 31, 2015. Menlo Park: AAAI, 2015: 2111-2117.
[26] TU C C, LIU H, LIU Z Y, et al. CANE: context-aware network embedding for relation modeling[C]//Proceedings of the 55th Annual Meeting of the Association for Computa-tional Linguistics, Vancouver, Jul 30-Aug 4, 2017. Strou-dsburg: ACL, 2017: 1722-1731.
[27] JACOB Y, DENOYER L, GALLINARI P. Learning latent representations of nodes for classifying in heterogeneous social networks[C]//Proceedings of the 7th ACM International Conference on Web Search and Data Mining, New York, Feb 24-28, 2014. New York: ACM, 2014: 373-382.
[28] DENG H B, LYU M R, KING I. A generalized Co-HITS algorithm and its application to bipartite graphs[C]//Procee-dings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, Jun 28-Jul 1, 2009. New York: ACM, 2009: 239-248.
[29] MASROUR F, TAN P N, ESFAHANIAN A H, et al. Attributed network representation learning approaches for link prediction[C]//Proceedings of the 2018 International Conference on Advances in Social Networks Analysis and Mining, Barcelona, Aug 28-31, 2018: 560-563.
[30] MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositio-nality[C]//Proceedings of the 27th Annual Conference on Neural Information Processing Systems, Lake Tahoe, Dec 5-8, 2013. Red Hook: Curran Associates, 2013: 3111-3119.
[31] WANG H, CAO J, SHU L C, et al. Locality sensitive hashing revisited: filling the gap between theory and algorithm ana-lysis[C]//Proceedings of the 22nd ACM International Con-ference on Information and Knowledge Management, San Fran-cisco, Oct 27-Nov 1, 2013. New York: ACM, 2013: 1969-1978.