Journal of Frontiers of Computer Science and Technology ›› 2025, Vol. 19 ›› Issue (1): 1-29.DOI: 10.3778/j.issn.1673-9418.2404034
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TU Jiaqi, ZHANG Hua, CHANG Xiaojie, WANG Ji, YUAN Shuhong
Online:
2025-01-01
Published:
2024-12-31
屠佳琪,张华,常晓洁,王佶,袁书宏
TU Jiaqi, ZHANG Hua, CHANG Xiaojie, WANG Ji, YUAN Shuhong. Heterogeneous Information Network Embedding Learning Based on Attention: a Survey[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(1): 1-29.
屠佳琪, 张华, 常晓洁, 王佶, 袁书宏. 融合注意力的异构信息网络嵌入学习综述[J]. 计算机科学与探索, 2025, 19(1): 1-29.
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[1] ZHANG J D, CHOW C Y. GeoSoCa: exploiting geographical, social and categorical correlations for point-of-interest recommendations[C]//Proceedings of the 38th Annual International ACM SIGIR Conference on Research and Deve-lopment in Information Retrieval, Santiago, Aug 9-13, 2015. New York: ACM, 2015: 443-452. [2] TAJEUNA E G, BOUGUESSA M, WANG S R. Modeling and predicting community structure changes in time-evolving social networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(6): 1166-1180. [3] YASUNAGA M, KASAI J, ZHANG R, et al. ScisummNet: a large annotated corpus and content-impact models for scientific paper summarization with citation networks[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence, Honolulu, Jan 27-Feb 1, 2019. Menlo Park: AAAI, 2019: 7386-7393. [4] HAMILTON W L, YING R, LESKOVEC J. Inductive representation learning on large graphs[C]//Proceedings of the 31st Annual Conference on Neural Information Processing Systems, Long Beach, Dec 4-9, 2017: 1024-1034. [5] REN X, LIU J L, YU X, et al. ClusCite: effective citation recommendation by information network-based clustering[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, Aug 24-27, 2014. New York: ACM, 2014: 821-830. [6] GENG X, ZHANG H W, BIAN J W, et al. Learning image and user features for recommendation in social networks[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, Dec 11-18, 2015. Washington: IEEE Computer Society, 2015: 4274-4282. [7] CAMACHO D M, COLLINS K M, POWERS R K, et al. Next-generation machine learning for biological networks[J]. Cell, 2018, 173(7): 1581-1592. [8] LI Y J, PATRA J C. Genome-wide inferring gene-phenotype relationship by walking on the heterogeneous network[J]. Bioinformatics, 2010, 26(9): 1219-1224. [9] YUE X, WANG Z, HUANG J G, et al. Graph embedding on biomedical networks: methods, applications and evaluations[J]. Bioinformatics, 2020, 36(4): 1241-1251. [10] FU X, ZHANG J, MEN Z, et al. MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding[C]//Proceedings of the 29th World Wide Web Conference, Taipei, China, Apr 20-24, 2020. New York: ACM, 2020: 2331-2341. [11] SHI C, LI Y, ZHANG J, et al. A survey of heterogeneous information network analysis[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(1): 17-37. [12] SEN P, NAMATA G, BILGIC M, et al. Collective classification in network data[J]. AI Magazine, 2008, 29(3): 93-106. [13] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[EB/OL]. [2024-02-26]. https://arxiv.org/abs/1609.02907. [14] MALLIAROS F D, VAZIRGIANNIS M. Clustering and community detection in directed networks: a survey[J]. Physics Reports-Review Section of Physics Letters, 2013, 533(4): 95-142. [15] ZHANG M, CHEN Y. Link prediction based on graph neural networks[C]//Proceedings of the 32nd Conference on Neural Information Processing Systems, Montreal, Dec 2-8, 2018: 5171-5181. [16] MA X, SUN P, QIN G. Nonnegative matrix factorization algorithms for link prediction in temporal networks using graph communicability[J]. Pattern Recognition, 2017, 71: 361-374. [17] YANG Y, LICHTENWALTER R N, CHAWLA N V. Evaluating link prediction methods[J]. Knowledge and Information Systems, 2015, 45(3): 751-782. [18] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[C]//Proceedings of the 1st International Conference on Learning Representations, Scottsdale, May 2-4, 2013. [19] PEROZZI B, AL-RFOU R, SKIENA S, et al. DeepWalk: online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, Aug 24-27, 2014. New York: ACM, 2014: 701-710. [20] GROVER A, LESKOVEC J, ASSOC COMP M. node2vec: scalable feature learning 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. [21] TANG J, QU M, WANG M, 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. [22] DEFFERRARD M, BRESSON X, VANDERGHEYNST P. Convolutional neural networks on graphs with fast localized spectral filtering[C]//Advances in Neural Information Processing Systems 29, Barcelona, Dec 5-10, 2016: 3837-3845. [23] LI Y, TARLOW D, BROCKSCHMIDT M, et al. Gated graph sequence neural networks[C]//Proceedings of the 4th International Conference on Learning Representations, San Juan, May 2-4, 2016. [24] ZHANG J, SHI X, XIE J, et al. GaAN: gated attention networks for learning on large and spatiotemporal graphs[C]//Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, Monterey, Aug 6-10, 2018: 339-349. [25] ZHANG J, SHI X, ZHAO S, et al. STAR-GCN: stacked and reconstructed graph convolutional networks for recommender systems[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, Aug 10-16, 2019: 4264-4270. [26] JIANG Z, LIU H, FU B, et al. Recommendation in heterogeneous information networks based on generalized random walk model and Bayesian personalized ranking[C]//Proceedings of the 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, Feb 5-9, 2018. New York: ACM, 2018: 288-296. [27] SHI C, HU B, ZHAO W X, et al. Heterogeneous information network embedding for recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(2): 357-370. [28] YANG C, ZHANG C, CHEN X, et al. Did you enjoy the ride: understanding passenger experience via heterogeneous network embedding[C]//Proceedings of the 34th IEEE International Conference on Data Engineering, Paris, Apr 16-19, 2018. Piscataway: IEEE, 2018: 1392-1403. [29] ZHANG Y, XIONG Y, KONG X, et al. Deep collective classification in heterogeneous information networks[C]//Proceedings of the 27th World Wide Web Conference, Lyon, Apr 23-27, 2018. New York: ACM, 2018: 399-408. [30] LI X, KAO B, REN Z, et al. Spectral clustering in heterogeneous information networks[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence, Honolulu, Jan 27-Feb 1, 2019. Menlo Park: AAAI, 2019: 4221-4228. [31] SUN Y, NORICK B, HAN J, et al. PathSelClus: integrating meta-path selection with user-guided object clustering in heterogeneous information networks[J]. ACM Transactions on Knowledge Discovery from Data, 2013, 7(3). [32] SUN Y, HAN J. Meta-path-based search and mining in heterogeneous information networks[J]. Tsinghua Science and Technology, 2013, 18(4): 329-338. [33] DONG Y, 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. [34] WU F, LU X, SONG J, et al. Learning of multimodal representations with random walks on the click graph[J]. IEEE Transactions on Image Processing, 2016, 25(2): 630-642. [35] FU T Y, LEE W C, LEI Z, et al. HIN2Vec: explore meta-paths in heterogeneous information networks for representation learning[C]//Proceedings of the 2017 ACM Conference on Information and Knowledge Management, Singapore, Nov 6-10, 2017. New York: ACM, 2017: 1797-1806. [36] HE Y, SONG Y, LI J, et al. HeteSpaceyWalk: a heterogeneous spacey random walk for heterogeneous information network embedding[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, Nov 3-7, 2019. New York: ACM, 2019: 639-648. [37] HUSSEIN R, YANG D, CUDRE-MAUROUX P. Are meta-paths necessary? Revisiting heterogeneous graph embeddings[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Oct 22-26, 2018. New York: ACM, 2018: 437-446. [38] LEE S, PARK C, YU H, et al. BHIN2vec: balancing the type of relation in heterogeneous information network[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, Nov 3-7, 2019. New York: ACM, 2019: 619-628. [39] WANG X, ZHANG Y, SHI C, et al. Hyperbolic heterogeneous information network embedding[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence, Honolulu, Jan 27-Feb 1, 2019. Menlo Park: AAAI, 2019: 5337-5344. [40] ZHANG D K, YIN J, ZHU X Q, et al. MetaGraph2Vec: complex semantic path augmented heterogeneous network embedding[C]//Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, Melbourne, Jun 3-6, 2018: 196-208. [41] TANG J, QU M, MEI Q Z, et al. PTE: predictive text embedding through large-scale heterogeneous text networks[C]//Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Sydney, Aug 10-13, 2015. New York: ACM, 2015: 1165-1174. [42] CHEN H, YIN H, WANG W, et al. PME: projected metric embedding on heterogeneous networks for link prediction[C]//Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, London, Aug 19-23, 2018. New York: ACM, 2018: 1177-1186. [43] XU L, WEI X, CAO J, et al. Embedding of embedding (EOE): joint embedding for coupled heterogeneous networks[C]//Proceedings of the 10th ACM International Conference on Web Search and Data Mining, Cambridge, Feb 6-10, 2017. New York: ACM, 2017: 741-749. [44] SHI Y, GUI H, ZHU Q, et al. AspEm: embedding learning by aspects in heterogeneous information networks[C]//Proceedings of the 2018 SIAM International Conference on Data Mining. Philadelphia: SIAM, 2018: 144-152. [45] SHI Y, ZHU Q, GUO F, et al. Easing embedding learning by comprehensive transcription of heterogeneous information networks[C]//Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, London, Aug 19-23, 2018. New York: ACM, 2018: 2190-2199. [46] LU Y, SHI C, HU L, et al. Relation structure-aware heterogeneous information network embedding[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence, Honolulu, Jan 27-Feb 1, 2019. Menlo Park: AAAI, 2019: 4456-4463. [47] ZHOU S, BU J, WANG X, et al. HAHE: hierarchical attentive heterogeneous information network embedding[EB/OL]. [2024-02-26]. https://arxiv.org/abs/1902.01475. [48] GORI M, MONFARDINI G, SCARSELLI F, et al. A new model for learning in graph domains[C]//Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, Montreal, Jul 31-Aug 4, 2005. Piscataway: IEEE, 2005: 729-734. [49] GAO H, WANG Z, JI S, et al. Large-scale learnable graph convolutional networks[C]//Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, London, Aug 19-23, 2018. New York: ACM, 2018: 1416-1424. [50] GILMER J, SCHOENHOLZ S S, RILEY P F, et al. Neural message passing for quantum chemistry[C]//Proceedings of the 34th International Conference on Machine Learning, Sydney, Aug 6-11, 2017. [51] DONG Y, HU Z, WANG K, et al. Heterogeneous network representation learning[C]//Proceedings of the 29th International Joint Conference on Artificial Intelligence, Jan 7-15, 2020: 4861-4867. [52] ZHU S, ZHOU C, PAN S, et al. Relation structure-aware heterogeneous graph neural network[C]//Proceedings of the 19th IEEE International Conference on Data Mining, Beijing, Nov 8-11, 2019. Piscataway: IEEE, 2019: 1534-1539. [53] ZHANG C, SONG D, HUANG C, et al. Heterogeneous graph neural network[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, Aug 4-8, 2019. New York: ACM, 2019: 793-803. [54] HU Z, DONG Y, WANG K, et al. Heterogeneous graph transformer[C]//Proceedings of the 29th World Wide Web Conference, Taipei, China, Apr 20-24, 2020. New York: ACM, 2020: 2704-2710. [55] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st Annual Conference on Neural Information Processing Systems, Long Beach, Dec 4-9, 2017. [56] WANG X, JI H, SHI C, et al. Heterogeneous graph attention network[C]//Proceedings of the 2019 World Wide Web Conference, San Francisco, May 13-17, 2019. New York: ACM, 2019: 2022-2032. [57] WANG X, BO D, SHI C, et al. A survey on heterogeneous graph embedding: methods, techniques, applications and sources[J]. IEEE Transactions on Big Data, 2023, 9(2): 415-436. [58] 齐金山, 梁循, 李志宇, 等. 大规模复杂信息网络表示学习: 概念、方法与挑战[J]. 计算机学报, 2018, 41(10): 2394-2420. QI J S, LIANG X, LI Z Y, et al. Representation learning of large-scale complex information network: concepts, methods and challenges[J]. Chinese Journal of Computers, 2018, 41(10): 2394-2420. [59] YANG C, XIAO Y, ZHANG Y, et al. Heterogeneous network representation learning: a unified framework with survey and benchmark[J]. IEEE Transactions on Know-ledge and Data Engineering, 2022, 34(10): 4854-4873. [60] XIE Y, YU B, LV S, et al. A survey on heterogeneous network representation learning[J]. Pattern Recognition, 2021, 116: 107936. [61] 周丽华, 王家龙, 王丽珍, 等. 异质信息网络表征学习综述[J]. 计算机学报, 2022, 45(1): 160-189. ZHOU L H, WANG J L, WANG L Z, et al. Heterogeneous information network representation learning: a survey[J]. Chinese Journal of Computers, 2022, 45(1): 160-189. [62] 刘杰, 尚学群, 宋凌云, 等. 图神经网络在复杂图挖掘上的研究进展[J]. 软件学报, 2022, 33(10): 3582-3618. LIU J, SHANG X Q, SONG L Y, et al. Progress of graph neural networks on complex graph mining[J]. Journal of Software, 2022, 33(10): 3582-3618. [63] BING R, YUAN G, ZHU M, et al. Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications[J]. Artificial Intelligence Review, 2023, 56(8): 8003-8042. [64] HUANG Z P, ZHENG Y D, CHENG R, et al. Meta structure: computing relevance in large heterogeneous information 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: 1595-1604. [65] DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understan-ding[C]//Proceedings of the 2019 Conference of the North-American Chapter of the Association for Computational Linguistics Human Language Technologies, Minneapolis, Jun 2-7, 2019. Stroudsburg: ACL, 2019: 4171-4186. [66] LI Y, JIN Y, SONG G, et al. GraphMSE: efficient meta-path selection in semantically aligned feature space for graph neural networks[C]//Proceedings of the 35th AAAI Conference on Artificial Intelligence, Feb 2-9, 2021. Menlo Park: AAAI, 2021: 4206-4214. [67] LI J, PENG H, CAO Y, et al. Higher-order attribute-enhancing heterogeneous graph neural networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(1): 560-574. [68] JI H, WANG X, SHI C, et al. Heterogeneous graph propagation network[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(1): 521-532. [69] CHEN K J, LU H, LIU Z, et al. Heterogeneous graph convolutional network with local influence[J]. Knowledge-Based Systems, 2022, 236: 107699. [70] ZHONG H, WANG M, ZHANG X. HeMGNN: heterogeneous network embedding based on a mixed graph neural network[J]. Electronics, 2023, 12(9). [71] ZHAO Y, SUN Y, HUANG Y, et al. Link prediction in heterogeneous networks based on metapath projection and aggregation[J]. Expert Systems with Applications, 2023, 227: 120325. [72] YANG X, YAN M, PAN S, et al. Simple and efficient heterogeneous graph neural network[EB/OL]. [2024-02-26]. https://arxiv.org/abs/2207.02547. [73] PARK J, JEONG S, LEE B S, et al. MIGTNet: metapath instance-based graph transformation network for heterogeneous graph embedding[J]. Future Generation Computer Systems-the International Journal of Escience, 2023, 149: 390-401. [74] LI C, FU J, YAN Y, et al. Higher order heterogeneous graph neural network based on node attribute enhancement[J]. Expert Systems with Applications, 2024, 238: 122404. [75] QUOC L, MIKOLOV T. Distributed representation of sentences and documents[C]//Proceedings of the 2014 International Conference on Machine Learning, Bejing, Jun 22-24, 2014: 1188-1196. [76] HUANG Z, XU W, YU K. Bidirectional LSTM-CRF models for sequence tagging[EB/OL]. [2024-02-26]. https://arxiv.org/abs/1508.01991. [77] FU Y, XIONG Y, YU P S, et al. Metapath enhanced graph attention encoder for HINs representation learning[C]//Proceedings of the 2019 IEEE International Conference on Big Data, Los Angeles, Dec 9-12, 2019. Piscataway: IEEE, 2019: 1103-1110. [78] NIU X, LI B, LI C, et al. A dual heterogeneous graph attention network to improve long-tail performance for shop search in E-commerce[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug 23-27, 2020. New York: ACM, 2020: 3405-3415. [79] WANG X, LIU N, HAN H, et al. Self-supervised heterogeneous graph neural network with co-contrastive learning[C]//Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug 14-18, 2021. New York: ACM, 2021: 1726-1736. [80] WANG P, AGARWAL K, HAM C, et al. Self-supervised learning of contextual embeddings for link prediction in heterogeneous networks[C]//Proceedings of the 30th World Wide Web Conference, Apr 12-23, 2021. New York: ACM, 2021: 2946-2957. [81] DONG X, ZHANG Y, PANG K, et al. Heterogeneous graph neural networks with denoising for graph embeddings[J]. Knowledge-Based Systems, 2022, 238: 107899. [82] ZHU G, ZHU Z, CHEN H, et al. HAGNN: hybrid aggregation for heterogeneous graph neural networks[EB/OL]. [2024-02-26]. https://arxiv.org/abs/2307.01636. [83] LI C, YAN Y, FU J, et al. HetReGAT-FC: heterogeneous residual graph attention network via feature completion[J]. Information Sciences, 2023, 632: 424-438. [84] MAO Q, LIU Z, LIU C, et al. HINormer: representation learning on heterogeneous information networks with graph transformer[EB/OL]. [2024-02-26]. https://arxiv.org/abs/2302.11329. [85] YAN Y, LI C, YU Y, et al. OSGNN: original graph and subgraph aggregated graph neural network[J]. Expert Systems with Applications, 2023, 225: 120115. [86] ZHAO Z, LIU Z, WANG Y, et al. RA-HGNN: attribute completion of heterogeneous graph neural networks based on residual attention mechanism[J]. Expert Systems with Applications, 2024, 243: 122945. [87] ZHOU Z, SHI J, YANG R, et al. SlotGAT: slot-based message passing for heterogeneous graph neural network[EB/OL]. [2024-06-03]. https://arxiv.org/abs/2405.01927. [88] 孟祥福, 温晶, 李子函, 等. 多重注意力指导下的异构图嵌入方法[J]. 智能系统学报, 2023, 18(4): 688-698. MENG X F, WEN J, LI Z H, et al. Heterogeneous graph embedding method guided by the multi-attention mechanism[J]. CAAI Transactions on Intelligent Systems, 2023, 18(4): 688-698. [89] FAN S, LIU G, LI J. A heterogeneous graph neural network with attribute enhancement and structure-aware attention[J]. IEEE Transactions on Computational Social Systems, 2024, 11(1): 829-838. [90] HONG H, GUO H, LIN Y, et al. An attention-based graph neural network for heterogeneous structural learning[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, Feb 7-12, 2020. Menlo Park: AAAI, 2020: 4132-4139. [91] CEN Y, ZOU X, ZHANG J, et al. Representation learning for attributed multiplex heterogeneous network[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, Aug 4-8, 2019. New York: ACM, 2019: 1358-1368. [92] THEKUMPARAMPIL K K, WANG C, OH S, et al. Attention-based graph neural network for semi-supervised learning[EB/OL]. [2024-06-03]. https://arxiv.org/abs/1803.03735. [93] HUANG J, WANG H, SUN Y, et al. HGAMN: heterogeneous graph attention matching network for multilingual POI retrieval at Baidu maps[C]//Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug 14-18, 2021. New York: ACM, 2021: 3032-3040. [94] CHAIRATANAKUL N, LIU X, MURATA T. PGRA: projected graph relation-feature attention network for heterogeneous information network embedding[J]. Information Sciences, 2021, 570: 769-794. [95] ZHAO J, WANG X, SHI C, et al. Heterogeneous graph structure learning for graph neural networks[C]//Proceedings of the 35th AAAI Conference on Artificial Intelligence, Feb 2-9, 2021. Menlo Park: AAAI, 2021: 4697-4705. [96] LEE S H, JI F, TAY W P, et al. Learning on heterogeneous graphs using high-order relations[C]//Proceedings of the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing, Jun 6-11, 2021. Piscataway: IEEE, 2021: 3175-3179. [97] XIA L, HUANG C, XU Y, et al. Multi-behavior graph neural networks for recommender system[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(4): 5473-5487. [98] YU L, SUN L, DU B, et al. Heterogeneous graph representation learning with relation awareness[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(6): 5935-5947. [99] WANG Y, LIU Z, XU J, et al. Heterogeneous network representation learning approach for ethereum identity identification[J]. IEEE Transactions on Computational Social Systems, 2023, 10(3): 890-899. [100] JIA X, WU P, CHEN L, et al. HDGT: heterogeneous driving graph transformer for multi-agent trajectory prediction via scene encoding[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(11): 13860-13875. [101] WANG Y, TANG S, LEI Y, et al. DisenHAN: disentangled heterogeneous graph attention network for recommendation[C]//Proceedings of the 29th ACM International Conference on Information and Knowledge Management, Oct 19-23, 2020. New York: ACM, 2020: 1605-1614. [102] MITRA A, VIJAYAN P, SINGH S R, et al. Revisiting link prediction on heterogeneous graphs with a multi-view perspective[C]//Proceedings of the 22nd IEEE International Conference on Data Mining, Orlando, Nov 28-Dec 1, 2022. Piscataway: IEEE, 2022: 358-367. [103] YANG S, ZHANG B, FENG S, et al. AHEAD: a triple attention based heterogeneous graph anomaly detection approach[EB/OL]. [2024-02-26]. https://arxiv.org/abs/2208. 08200. [104] JIN B, ZHANG Y, ZHU Q, et al. Heterformer: transformer-based deep node representation learning on heterogeneous text-rich networks[C]//Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach, Aug 6-10, 2023. New York: ACM, 2023: 1020-1031. [105] TANG J, YANG Y, WEI W, et al. HiGPT: heterogeneous graph language model[EB/OL]. [2024-06-03]. https://arxiv. org/abs/2402.16024. [106] ZHANG C, HUANG C, YU L, et al. Camel: content-aware and meta-path augmented metric learning for author identification[C]//Proceedings of the 27th World Wide Web Conference, Lyon, Apr 23-27, 2018. New York: ACM, 2018: 709-718. [107] CHEN T, SUN Y. Task-guided and path-augmented heterogeneous network embedding for author identification[C]//Proceedings of the 10th ACM International Conference on Web Search and Data Mining, Cambridge, Feb 6-10, 2017. New York: ACM, 2017: 295-304. [108] 黄丽, 朱焱, 李春平. 基于异构网络表征学习的作者学术行为预测[J]. 计算机科学, 2022, 49(9): 76-82. HUANG L, ZHU Y, LI C P. Authors academic behavior prediction based on heterogeneous network representation learning[J]. Computer Science, 2022, 49(9): 76-82. [109] PARK C, KIM D, ZHU Q, et al. Task-guided pair embedding in heterogeneous network[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, Nov 3-7, 2019. New York: ACM, 2019: 489-498. [110] RAO S X, ZHANG S, HAN Z, et al. xFraud: explainable fraud transaction detection[J]. Proceedings of the VLDB Endowment, 2021, 15(3): 427-436. [111] HOU S, YE Y, SONG Y, et al. HinDroid: an intelligent Android malware detection system based on structured heterogeneous information network[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Know-ledge Discovery and Data Mining, Halifax, Aug 13-17, 2017. New York: ACM, 2017: 1507-1515. [112] LIU Z, CHEN C, YANG X, et al. Heterogeneous graph neural networks for malicious account detection[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Oct 22-26, 2018. New York: ACM, 2018: 2077-2085. [113] FAN Y, HOU S, ZHANG Y, et al. Gotcha-Sly Malware! Scorpion: a metagraph2vec based malware detection system[C]//Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, London, Aug 19-23, 2018. New York: ACM, 2018: 253-262. [114] ZHANG Y, FAN Y, YE Y, et al. Key player identification in underground forums over attributed heterogeneous information network embedding framework[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, Nov 3-7, 2019. New York: ACM, 2019: 549-558. [115] FAN Y, ZHANG Y, HOU S, et al. iDev: enhancing social coding security by cross-platform user identification between github and stack overflow[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, Aug 10-16, 2019: 2272-2278. [116] ZHANG Y, FAN Y, SONG W, et al. Your style your identity: leveraging writing and photography styles for drug trafficker identification in darknet markets over attributed heterogeneous information network[C]//Proceedings of the 2019 World Wide Web Conference, San Francisco, May 13-17, 2019. New York: ACM, 2019: 3448-3454. [117] YE Y, HOU S, CHEN L, et al. Out-of-sample node representation learning for heterogeneous graph in real-time Android malware detection[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, Aug 10-16, 2019: 4150-4156. [118] HOU S, FAN Y, ZHANG Y, et al. αCyber: enhancing robustness of Android malware detection system against adversarial attacks on heterogeneous graph based model[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, Nov 3-7, 2019. New York: ACM, 2019: 609-618. [119] SHI B, DONG B, XU Y, et al. An edge feature aware heterogeneous graph neural network model to support tax evasion detection[J]. Expert Systems with Applications, 2023, 213: 118903. [120] ZHAO J, SHAO M, TANG H, et al. RHGNN: fake reviewer detection based on reinforced heterogeneous graph neural networks[J]. Knowledge-Based Systems, 2023, 280: 111029. [121] HU B, ZHANG Z, SHI C, et al. Cash-out user detection based on attributed heterogeneous information network with a hierarchical attention mechanism[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence, Honolulu, Jan 27-Feb 1, 2019. Menlo Park: AAAI, 2019: 946-953. [122] HU B, SHI C, ZHAO W X, et al. Leveraging meta-path based context for top-N recommendation with a neural co-attention model[C]//Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, London, Aug 19-23, 2018. New York: ACM, 2018: 1531-1540. [123] FAN S, ZHU J, HAN X, et al. Metapath-guided heterogeneous graph neural network for intent recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, Aug 4-8, 2019. New York: ACM, 2019: 2478-2486. [124] ZHAO J, ZHOU Z, GUAN Z, et al. IntentGC: a scalable graph convolution framework fusing heterogeneous information for recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, Aug 4-8, 2019. New York: ACM, 2019: 2347-2357. [125] XU Y, ZHU Y, SHEN Y, et al. Learning shared vertex representation in heterogeneous graphs with convolutional networks for recommendation[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, Aug 10-16, 2019: 4620-4626. [126] WANG Z, LIU H, DU Y, et al. Unified embedding model over heterogeneous information network for personalized recommendation[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, Aug 10-16, 2019: 3813-3819. [127] HU L, XU S, LI C, et al. Graph neural news recommendation with unsupervised preference disentanglement[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Jul 5-10, 2020. Stroudsburg: ACL, 2020: 4255-4264. [128] JIN J, QIN J, FANG Y, et al. An efficient neighborhood-based interaction model for recommendation on heterogeneous graph[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug 23-27, 2020. New York: ACM, 2020: 75-84. [129] IANA A, PAULHEIM H. GraphConfRec: a graph neural network-based conference recommender system[C]//Proceedings of the 21st ACM/IEEE Joint Conference on Digital Libraries, Sep 27-30, 2021. Piscataway: IEEE, 2021: 90-99. [130] ZHENG J, MA Q, GU H, et al. Multi-view denoising graph auto-encoders on heterogeneous information networks for cold-start recommendation[C]//Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug 14-18, 2021. New York: ACM, 2021: 2338-2348. [131] GUAN W, JIAO F, SONG X, et al. Personalized fashion compatibility modeling via metapath-guided heterogeneous graph learning[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Jul 11-15, 2022. New York: ACM, 2022: 482-491. [132] ZHAO Y, XU Q, ZOU Y, et al. Modeling user reviews through Bayesian graph attention networks for recommendation[J]. ACM Transactions on Information Systems, 2023, 41(3). [133] HU L, LI C, SHI C, et al. Graph neural news recommendation with long-term and short-term interest modeling[J]. Information Processing & Management, 2020, 57(2): 102142. [134] ZHU R, ZHAO K, YANG H, et al. AliGraph: a comprehensive graph neural network platform[J]. Proceedings of the VLDB Endowment, 2019, 12(12): 2094-2105. [135] WANG M, ZHENG D, YE Z, et al. Deep graph library: a graph-centric, highly-performant package for graph neural networks[EB/OL]. [2024-02-26]. https://arxiv.org/abs/1909. 01315. [136] FEY M, ERIC LENSSEN J. Fast graph representation learning with PyTorch geometric[EB/OL]. [2024-02-26]. https://arxiv.org/abs/1903.02428. [137] LV Q, DING M, LIU Q, et al. Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks[C]//Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug 14-18, 2021. New York: ACM, 2021: 1150-1160. [138] HAN H, ZHAO T, YANG C, et al. OpenHGNN: an open-source toolkit for heterogeneous graph neural networks[C]//Proceedings of the 31st ACM International Conference on Information and Knowledge Management, Atlanta, Oct 17-21, 2022. New York: ACM, 2022: 3993-3997. [139] ZHAO T, YANG C, LI Y, et al. Space4HGNN: a novel, modularized and reproducible platform to evaluate heterogeneous graph neural network[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Jul 11-15, 2022. New York: ACM, 2022: 2776-2789. [140] ETEMADI R, ZIHAYAT M, FENG K, et al. OpenAtt-HetRL: an open source toolkit for attributed heterogeneous network representation learning[C]//Proceedings of the 30th ACM International Conference on Information and Knowledge Management, Nov 1-5, 2021. New York: ACM, 2021: 4706-4710. |
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