Computer Science ›› 2025, Vol. 52 ›› Issue (6): 167-178.doi: 10.11896/jsjkx.240600032
• Database & Big Data & Data Science • Previous Articles Next Articles
WANG Jinghong1,2,3, WU Zhibing1, WANG Xizhao4, LI Haokang5
CLC Number:
[1]SUN Y,HAN J.Mining heterogeneous information networks:a structural analysis approach[J].ACM SIGKDD Explorations Newsletter,2013,14(2):20-28. [2]ATWOOD J,TOWSLEY D.Diffusion-convolutional neural networks[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems.New York:Curran Associates Inc,2016:2001-2009. [3]ZHAO M,JIA A L.DAHGN:Degree-Aware HeterogeneousGraph Neural Network[J].Knowledge-Based Systems,2024,285:111355. [4]KIPF T,WELLING M.Semi-supervised classification withgraph convolutional networks[EB/OL].(2017-02-09) [2024-03-11].https://arxiv.org/abs/1609.02907. [5]WENG L J,ZHANG Q H,LIN Z B,et al.Harnessing heteroge-neous social networks for better recommendations:A grey relational analysis approach[J].Expert Systems with Applications,2021,174:114771. [6]WANG D X,CUI P,ZHU W.Structural Deep Network Embedding[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:Association for Computing Machinery,2016:1225-1234. [7]BERG R,KIPF T N,WELLING M.Graph convolutional matrix completion[EB/OL].(2017-06-07) [2024-03-11].https://arxiv.org/abs/1706.02263. [8]ZHANG J,SHI X,ZHAO S,et al.STAR-GCN:Stacked and Re-constructed Graph Convolutional Networks for Recommender Systems[C]//Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence.Hawaii:AAAI Press,2019:4264-4270. [9]FOUT A,BYRD J,SHARIAT B,et al.Protein interface predic-tion using graph convolutional networks[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.New York:Curran Associates Inc,2017:6533-6542. [10]ZITNIK M,AGRAWAL M,LESKOVEC J.Modeling polypharmacy side effects with graph convolutional networks[J].Bioinformatics,2018,34(13):i457-i466. [11]LI Y,YU R,SHAHABI C,et al.Diffusion convolutional recurrent neural network:data-driven traffic forecasting[EB/OL].(2017-07-06)[2024-03-11].https://arxiv.org/abs/1707.01926. [12]ZHANG J,SHI X,XIE J,et al.Gaan:gated attention networks for learning on large and spatiotemporal graphs[EB/OL].(2018-03-20) [2024-03-11].https://arxiv.org/abs/1803.07294. [13]DONG Y,CHAWLA N,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.New York:Association for Computing Machinery,2017:135-144. [14]FU T Y,LEE W C,LEI Z.HIN2Vec:explore meta-paths in heterogeneous information networks for representation learning[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.New York:Association for Computing Machinery,2017:1797-1806. [15]ZHANG W,FANG Y,LIU Z,et al.Mg2vec:learning relationship-preserving heterogeneous graph representations via metagraph embedding[J].IEEE Transactions on Knowledge and Data Engineering,2020,34(3),1317-1329. [16]ZHAO K,BAI T,WU B,et al.Deep adversarial completion for sparse heterogeneous information network embedding[C]//Proceedings of The Web Conference 2020.New York:Association for Computing Machinery,2020:508-518. [17]HU Z,DONG Y,WANG K,et al.Heterogeneous graph Trans-former[C]//Proceedings of The Web Conference 2020.New York:Association for Computing Machinery,2020:2704-2710. [18]VASHISHTH S,SANYAL S,NITIN V,et al.Composition-based multi-relational graph convolutional networks[EB/OL].(2019-11-08) [2024-03-11].https://arxiv.org/abs/1911.03082. [19]YANG Y,GUAN Z,LI J,et al.Interpretable and efficient heterogeneous graph convolutional network[J].IEEE Transactions on Knowledge and Data Engineering,2021,35(2):1637-1650. [20]WANG X,JI H,SHI C,et al.Heterogeneous graph attention network[C]//The World Wide Web Conference.New York:Association for Computing Machinery,2019:2022-2032. [21]FU X,ZHANG J,MENG Z,et al.MAGNN:metapath aggregated graph neural network for heterogeneous graph embedding[C]//Proceedings of the Web Conference 2020.New York:Association for Computing Machinery,2020:2331-2341. [22]SHAO Z,XU Y,WEI W,et al.Heterogeneous Graph NeuralNetwork With Multi-View Representation Learning[J].IEEE Transactions on Knowledge & Data Engineering,2023,35(11):11476-11488. [23]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 Conference on Knowledge Discovery &Data Mining.New York:Association for Computing Machinery,2021:1150-1160. [24]VELIČKOVIĆ P,CUCURULL G,CASANOVA A,et al.Graph attention networks[EB/OL].(2017-10-30) [2024-03-11].https://arxiv.org/abs/1710.10903. [25]LI W,NI L,WANG J,et al.Collaborative representation lear-ning for nodes and relations via heterogeneous graph neural network[J].Knowledge-Based Systems,2022,255:109673. [26]YANG X,YAN M,PAN S,et al.Simple and efficient heterogeneous graph neural network[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2023:10816-10824. [27]HWANG H J,KIM G H,HONG S,et al.Multi-view representation learning via total correlation objective[J].Advances in Neural Information Processing Systems,2021,34:12194-12207. [28]TANG S,LI B,YU H.ChebNet:Efficient and stable constructions of deep neural networks with rectified power units using chebyshev approximations[EB/OL].(2019-11-07)[2024-03-11].https://arxiv.org/abs/1911.05467. [29]LEVIE R,MONTI F,BRESSON X,et al.Cayleynets:graphconvolutional neural networks with complex rational spectral filters[J].IEEE Transactions on Signal Processing,2018,67(1):97-109. [30]BRUNA J,ZAREMBA W,SZLAM A,et al.Spectral networks and locally Connected Networks on Graphs[EB/OL].(2013-12-21) [2024-03-11].https://arxiv.org/abs/1312.6203. [31]DEFFERRARD M,BRESSON X,VANDERGHEYNST P.Convolutional neural networks on graphs with fast localized spectral filtering[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems.New York:Curran Associates Inc,2016:3844-3852. [31]CHEN M,WEI Z,HUANG Z,et al.Simple and deep graph convolutional networks[C]//Proceedings of the 37th International Conference on Machine Learning.Online:JMLR.org,2020:1725-1735. [32]HAMILTON W L,YING Z,LESKOVEC J.Inductive representation learning on large graphs[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.New York:Curran Associates.2017:1025-1035. [33]PANG B,FU Y,REN S,et al.CGNN:traffic classification with graph neural network[EB/OL].(2021-10-19) [2024-03-11].https://arxiv.org/abs/2110.09726. [34]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 Conference on Knowledge Discovery &Data Mining.New York:Association for Computing Machinery,2021:1726-1736. [35]YUN S,JEONG M,KIM R,et al.Graph transformer networks[C]//Proceedings of the 33rd International Conference on Neural Information Processing Systems.New York:Curran Associates Inc,2019:11983-11993. [36]SCHLICHTKRULL M,KIPF T N,BLOEM P,et al.Modeling relational data with graph convolutional networks[C]////European Semantic Web Conference.Berlin:Springer-Verlag,2018:593-607. [37]ZHANG C,SONG D,HUANG C,et al.Heterogeneous graph neural network[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York:Association for Computing Machinery,2019:793-803. [38]BORDES A,USUNIER N,GARCIA-DURÁN A,et al.Translating embeddings for modeling multi-relational data[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems-Volume 2.New York:Curran Associates Inc,2013:2787-2795. [39]AN DER MAATEN L,HINTON G.Visualizing data usingt-SNE[J].Journal of Machine Learning Research,2008,9:2579-2605. |
[1] | GUO Xuan, HOU Jinlin, WANG Wenjun, JIAO Pengfei. Dynamic Link Prediction Method for Adaptively Modeling Network Dynamics [J]. Computer Science, 2025, 52(6): 118-128. |
[2] | TAN Qiyin, YU Jiong, CHEN Zixin. Outlier Detection Method Based on Adaptive Graph Autoencoder [J]. Computer Science, 2025, 52(6): 129-138. |
[3] | WU Pengyuan, FANG Wei. Study on Graph Collaborative Filtering Model Based on FeatureNet Contrastive Learning [J]. Computer Science, 2025, 52(5): 139-148. |
[4] | HU Haibo, YANG Dan, NIE Tiezheng, KOU Yue. Graph Contrastive Learning Incorporating Multi-influence and Preference for Social Recommendation [J]. Computer Science, 2024, 51(7): 146-155. |
[5] | WEI Ziang, PENG Jian, HUANG Feihu, JU Shenggen. Text Classification Method Based on Multi Graph Convolution and Hierarchical Pooling [J]. Computer Science, 2024, 51(7): 303-309. |
[6] | LIU Wei, SONG You, ZHUO Peiyan, WU Weiqiang, LIAN Xin. Study on Kcore-GCN Anti-fraud Algorithm Fusing Multi-source Graph Features [J]. Computer Science, 2024, 51(6A): 230600040-7. |
[7] | PENG Bo, LI Yaodong, GONG Xianfu, LI Hao. Method for Entity Relation Extraction Based on Heterogeneous Graph Neural Networks and TextSemantic Enhancement [J]. Computer Science, 2024, 51(6A): 230700071-5. |
[8] | LU Min, YUAN Ziting. Graph Contrast Learning Based Multi-graph Neural Network for Session-based RecommendationMethod [J]. Computer Science, 2024, 51(5): 54-61. |
[9] | ZHENG Cheng, SHI Jingwei, WEI Suhua, CHENG Jiaming. Dual Feature Adaptive Fusion Network Based on Dependency Type Pruning for Aspect-basedSentiment Analysis [J]. Computer Science, 2024, 51(3): 205-213. |
[10] | XU Tianyue, LIU Xianhui, ZHAO Weidong. Knowledge Graph and User Interest Based Recommendation Algorithm [J]. Computer Science, 2024, 51(2): 55-62. |
[11] | YANG Dongsheng, WANG Guiling, ZHENG Xin. Hierarchical Hypergraph-based Attention Neural Network for Service Recommendation [J]. Computer Science, 2024, 51(11): 103-111. |
[12] | LIU Zulong, CHEN Kejia. Structural Influence and Label Conflict Aware Based Graph Curriculum Learning Approach [J]. Computer Science, 2024, 51(10): 227-233. |
[13] | LIAO Bin, ZHANG Tao, YU Jiong, LI Min. NLGAE:A Graph Autoencoder Model Based on Improved Network Structure and Loss Functionfor Node Classification Task [J]. Computer Science, 2024, 51(10): 234-246. |
[14] | GUO Yuxing, YAO Kaixuan, WANG Zhiqiang, WEN Liangliang, LIANG Jiye. Black-box Graph Adversarial Attacks Based on Topology and Feature Fusion [J]. Computer Science, 2024, 51(1): 355-362. |
[15] | JIANG Linpu, CHEN Kejia. Self-supervised Dynamic Graph Representation Learning Approach Based on Contrastive Prediction [J]. Computer Science, 2023, 50(7): 207-212. |
|