Abstract Heterogeneous information network representation learning is widely applied in many fields including node classification, link prediction and personalized recommendation. The existing heterogeneous information network representation learning methods mainly focus on static networks but ignore the influence of time on node representations. To address this problem, a meta-path and hierarchical attention based temporal heterogeneous network representation learning method is proposed. The meta-paths are utilized to capture the structural and semantic information in heterogeneous information networks. Through the time decay attention layer, the impact of different meta-path instances at a specific time on the target node is captured. Through the meta-path level attention, the node representation under different meta-paths is fused to obtain the final representation. The experiments on DBLP and IMDB datasets show that the proposed method achieves better results on the tasks of node classification and node clustering.
Fund:National Natural Science Foundation of China(No.62072288,61702306), Natural Science Foundation of Shandong Province(No.ZR2018BF013)
Corresponding Authors:
ZHAO Zhongying, Ph.D., associate professor. Her research interests include social network analysis and data mining.
About author:: QIN Haiying, master student. Her research interests include heterogeneous information network representation learning and application. LI Jianhui, master student. His research interests include network representation lear-ning and recommendation system. LI Chao, Ph.D., associate professor. His research interests include big data analysis, social network analysis and data mining.
[1] FAN S H, ZHU J X, HAN X T, et al. Metapath-Guided Heterogeneous Graph Neural Network for Intent Recommendation // Proc of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2019: 2478-2486. [2] SHAO Y B, LIU C.H2Rec: Homogeneous and Heterogeneous Network Embedding Fusion for Social Recommendation. International Journal of Computational Intelligence Systems, 2021, 14: 1303-1314. [3] DONG Y X, HU Z N, WANG K S, et al. Heterogeneous Network Representation Learning // Proc of the 29th International Joint Conference on Artificial Intelligence. San Francisco,USA: Morgan Kaufmann, 2020: 4861-4867. [4] JI H Y, WANG X, SHI C, et al. Heterogeneous Graph Propagation Network. IEEE Transactions on Knowledge and Data Engineering, 2021. DOI: 10.1109/TKDE.2021.3079239 [5] JACOB Y, DENOYER L, GALLINARI P.Learning Latent Representations of Nodes for Classifying in Heterogeneous Social Networks // Proc of the 7th ACM International Conference on Web Search and Data Mining. New York,USA: ACM, 2014: 373-382. [6] PHAM P, DO P.W-Mmp2vec: Topic-Driven Network Embedding Model for Link Prediction in Content-Based Heterogeneous Information Network. Intelligent Data Analysis, 2021, 25(3): 711-738. [7] CHEN H X, YIN H Z, WANG W Q, et al. PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction // Proc of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2018: 1177-1186. [8] SHI C, HU B B, ZHAO W X, et al. Heterogeneous Information Network Embedding for Recommendation. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(2): 357-370. [9] LI C, HU L M, SHI C, et al. Sequence-Aware Heterogeneous Graph Neural Collaborative Filtering // Proc of the SIAM International Conference on Data Mining. Philadelphia, USA: SIAM, 2021: 64-72. [10] XU J X, ZHU Z Z, ZHAO J X, et al. Gemini: A Novel and Universal Heterogeneous Graph Information Fusing Framework for Online Recommendations // Proc of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2020: 3356-3365. [11] CUI P, WANG X, PEI J, et al. A Survey on Network Embedding. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(5): 833-852. [12] YANG C, XIAO Y X, ZHANG Y, et al. Heterogeneous Network Representation Learning: Survey, Benchmark, Evaluation, And Beyond[C/OL].[2021-03-18]. https://arxiv.org/pdf/2004.00216v1.pdf. [13] SHI C, LI Y T, ZHANG J W, et al. A Survey of Heterogeneous Information Network Analysis. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(1): 17-37. [14] DONG Y X, CHAWLA N V, SWAMI A.Metapath2vec: Scalable Representation Learning for Heterogeneous Networks // Proc of the 23rd ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining. New York, USA: ACM, 2017: 135-144. [15] FU T Y, LEE W C, LEI Z.Hin2Vec: Explore Meta-Paths in He-terogeneous Information Networks for Representation Learning // Proc of the ACM Conference on Information and Knowledge Ma-nagement. New York, USA: ACM, 2017: 1797-1806. [16] ZHANG C X, SONG D J, HUANG C, et al. Heterogeneous Graph Neural Network // Proc of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2019: 793-803. [17] WANG X, JI H Y, SHI C, et al. Heterogeneous Graph Attention Network // Proc of the International Conference on World Wide Web . New York,USA: ACM, 2019: 2022-2032. [18] FU X Y, ZHANG J N, MENG Z Q, et al. MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding // Proc of the International Conference on World Wide Web. New York, USA: ACM, 2020: 2331-2341. [19] HONG H T, GUO H T, LIN Y C, et al.An Attention-Based Graph Neural Network for Heterogeneous Structural Learning // Proc of the 34th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2020: 4132-4139. [20] HU B B, FANG Y, SHI C.Adversarial Learning on Heterogeneous Information Networks // Proc of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2019: 120-129. [21] DU L, WANG Y, SONG G J, et al. Dynamic Network Embe-dding: An Extended Approach for Skip-Gram Based Network Embedding // Proc of the 27th International Joint Conferences on Artificial Intelligence. San Francisco, USA: Morgan Kaufmann, 2018: 2086-2092. [22] ZHU L H, GUO D, YIN J M, et al. Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(10): 2765-2777. [23] ZHOU L K, YANG Y, REN X, et al.Dynamic Network Embe-dding by Modeling Triadic Closure Process // Proc of the 32nd AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2018: 571-578. [24] YANG L W, XIAO Z B, JIANG W, et al. Dynamic Heterogeneous Graph Embedding Using Hierarchical Attentions // Proc of the European Conference on Information Retrieval. Berlin, Germany: Springer, 2020: 425-432. [25] ZUO Y, LIU G N, LIN H, et al. Embedding Temporal Network via Neighborhood Formation // Proc of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2018: 2857-2866. [26] LU Y F, WANG X, SHI C, et al. Temporal Network Embedding with Micro-and Macro-Dynamics // Proc of the 28th ACM International Conference on Information and Knowledge Management. New York, USA: ACM, 2019: 469-478. [27] SUN Y Z, HAN J W, YAN X F, et al. PathSim: Meta Path-Based Top-k Similarity Search in Heterogeneous Information Networks. Proceedings of the VLDB Endowment, 2011, 4(11): 992-1003. [28] FARD A M, BAGHERI E, WANG K.Relationship Prediction in Dynamic Heterogeneous Information Networks // Proc of the European Conference on Information Retrieval. Berlin, Germany: Sprin-ger, 2019: 19-34. [29] HAMILTON W L, BAJAJ P, ZITNIK M, et al.Embedding Logical Queries on Knowledge Graphs // Proc of the 32nd International Conference on Neural Information Processing Systems. Cambridge, USA: The MIT Press, 2018: 2030-2041. [30] SUN Z Q, DENG Z H, NIE J Y, et al. RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space[C/OL].[2021-03-18]. https://arxiv.org/pdf/1902.10197.pdf. [31] GAO J, LIANG F, FAN W, et al.Graph-Based Consensus Maximization among Multiple Supervised and Unsupervised Models // Proc of the 22nd International Conference on Neural Information Processing Systems. Cambridge, USA: The MIT Press, 2009: 585-593. [32] KIPF T N, WELLING M.Semi-supervised Classification with Graph Convolutional Networks[C/OL]. [2021-03-18].https://openreview.net/pdf?id=sju4ayygl. [33] VELIČKOVIĆ P, CUCURULL G, CASANOVA A, et al. Graph Attention Networks[C/OL]. [2021-03-18]. https://openreview.net/pdf?id=rjxmpikcz. [34] BORDES A, USUNIER N, GARCIA-DURÁN A, et al. Translating Embeddings for Modeling Multi-relational Data // Proc of the 26th International Conference on Neural Information Processing Systems. Cambridge, USA: The MIT Press, 2013, II: 2787-2795. [35] VAN DER MAATEN L, HINTON G. Visualizing Data Using T-SNE. Journal of Machine Learning Research, 2008, 9: 2579-2605.
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