A survey on heterogeneous network representation learning

Pattern Recognition - Tập 116 - Trang 107936 - 2021
Yu Xie1, Bin Yu2, Shengze Lv2, Chen Zhang2, Guodong Wang2, Maoguo Gong3
1Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006, China
2School of Computer Science and Technology, Xidian University, Xi’an, China
3School of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China

Tài liệu tham khảo

Yasunaga, 2019, ScisummNet: A large annotated corpus and content-impact models for scientific paper summarization with citation networks, 7386 Camacho, 2018, Next-generation machine learning for biological networks, Cell, 173, 1581, 10.1016/j.cell.2018.05.015 Zheng, 2020, Clustering social audiences in business information networks, Pattern Recognit., 100, 107126, 10.1016/j.patcog.2019.107126 Yongjun, 2018, A comment on ǣcross-platform identification of anonymous identical users in multiple social media networksǥ, IEEE Trans. Knowl. Data Eng., 30, 1409, 10.1109/TKDE.2018.2828812 Tajeuna, 2019, Modeling and predicting community structure changes in time-evolving social networks, IEEE Trans. Knowl. Data Eng., 31, 1166, 10.1109/TKDE.2018.2851586 Tang, 2015, LINE: large-scale information network embedding, 1067 Gong, 2020, Semi-supervised network embedding with text information, Pattern Recognit., 104, 107347, 10.1016/j.patcog.2020.107347 Martelot, 2018, Fast multi-scale detection of relevant communities in large-scale networks, Comput. J., 1136 Ma, 2017, Nonnegative matrix factorization algorithms for link prediction in temporal networks using graph communicability, Pattern Recognit., 71, 361, 10.1016/j.patcog.2017.06.025 Moreno, 2016, Web mining based framework for solving usual problems in recommender systems. a case study for movies recommendation, Neurocomputing, 176, 72, 10.1016/j.neucom.2014.10.097 Reihanian, 2018, Overlapping community detection in rating-based social networks through analyzing topics, ratings and links, Pattern Recognit., 81, 370, 10.1016/j.patcog.2018.04.013 Kim, 2019, Semi-supervised learning for hierarchically structured networks, Pattern Recognit., 191, 10.1016/j.patcog.2019.06.009 Shi, 2012, Relevance search in heterogeneous networks, 180 Wang, 2017, A survey on learning to hash, IEEE Trans. Pattern Anal. Mach. Intell., 40, 769, 10.1109/TPAMI.2017.2699960 Sun, 2013, Pathselclus: integrating meta-path selection with user-guided object clustering in heterogeneous information networks, ACM Trans. Knowl. Discovery Data, 7, 11 Kong, 2012, Meta path-based collective classification in heterogeneous information networks, 1567 Shi, 2019, Diffusion network embedding, Pattern Recognit., 88, 518, 10.1016/j.patcog.2018.12.004 Malliaros, 2013, Clustering and community detection in directed networks: a survey, Phys. Rep., 533, 95, 10.1016/j.physrep.2013.08.002 Yang, 2015, Evaluating link prediction methods, Knowl. Inf. Syst., 751, 10.1007/s10115-014-0789-0 Rauber, 2016, Visualizing time-dependent data using dynamic t-sne, 73 Perozzi, 2014, DeepWalk: Online learning of social representations, 701 Meng, 2015, Discovering meta-paths in large heterogeneous information networks, 754 Basu, 2007, 15 Tang, 2015, LINE: Large-scale information network embedding, 1067 Shi, 2016, A survey of heterogeneous information network analysis, IEEE Trans. Knowl. Data Eng., 29, 17, 10.1109/TKDE.2016.2598561 Yan, 2018, A survey on mining heterogeneous information network Hamilton, 2017, Inductive representation learning on large graphs, 1024 Quan, 2016, Multichannel convolutional neural network for biological relation extraction, BioMed Res. Int., 10.1155/2016/1850404 Ley, 2009, DBLP: Some lessons learned, Proc. VLDB Endowment, 1493, 10.14778/1687553.1687577 J.-P. Vasseur, G. Tolle, S. Rangwala, P. Buonadonna, Hierarchical schema to provide an aggregated view of device capabilities in a network, 2016, US Patent 9,253,021. Chen, 2017, Hine: Heterogeneous information network embedding, 180 Shi, 2018, Easing embedding learning by comprehensive transcription of heterogeneous information networks, 2190 Dong, 2017, metapath2vec: Scalable representation learning for heterogeneous networks, 135 Huang, 2017, Heterogeneous information network embedding for meta path based proximity, arXiv preprint arXiv:1701.05291 Shi, 2017, PReP: Path-based relevance from a probabilistic perspective in heterogeneous information networks, 425 Fu, 2017, HIN2vec: Explore meta-paths in heterogeneous information networks for representation learning, 1797 Fang, 2018, Transpath: representation learning for heterogeneous information networks via translation mechanism, IEEE Access, 6, 20712, 10.1109/ACCESS.2018.2827121 Tan, 2018, SERL: Semantic-path biased representation learning of heterogeneous information network, 287 Zhang, 2018, Metagraph2vec: Complex semantic path augmented heterogeneous network embedding, 196 Sun, 2018, Joint embedding of meta-path and meta-graph for heterogeneous information networks, 131 Gui, 2017, Embedding learning with events in heterogeneous information networks, IEEE Trans. Knowl. Data Eng., 29, 2428, 10.1109/TKDE.2017.2733530 Tu, 2018, Structural deep embedding for hyper-networks, 426 Zhang, 2018, CARL: Content-aware representation learning for heterogeneous networks, arXiv preprint arXiv:1805.04983 Qu, 2018, Curriculum learning for heterogeneous star network embedding via deep reinforcement learning, 468 Fu, 2019, Representation learning for heterogeneous information networks via embedding events, arXiv preprint arXiv:1901.10234 Zhou, 2019, HAHE: Hierarchical attentive heterogeneous information network embedding, Proc. 27th ACM Int. Conf. Inf. Knowl. Manage. Mikolov, 2013, Efficient estimation of word representations in vector space, arXiv preprint arXiv:1301.3781 Grover, 2016, node2vec: Scalable feature learning for networks, 855 Mikolov, 2013, Distributed representations of words and phrases and their compositionality, Adv Neural Inf Process Syst, 3111 Solé-Ribalta, 2016, Random walk centrality in interconnected multilayer networks, Physica D, 323, 73, 10.1016/j.physd.2016.01.002 Masuda, 2017, Random walks and diffusion on networks, Phys. Rep., 716, 1, 10.1016/j.physrep.2017.07.007 Tommiska, 2003, Efficient digital implementation of the sigmoid function for reprogrammable logic, IEE Proceedings-Computers and Digital Tech., 150, 403, 10.1049/ip-cdt:20030965 Wei, 2011, Rational research model for ranking semantic entities, Inf. Sci., 181, 2823, 10.1016/j.ins.2011.02.028 Congdon, 2010, Gaussian markov random fields: theory and applications, J. Royal Stat. Soc., 170, 858, 10.1111/j.1467-985X.2007.00485_8.x Blei, 2010, Supervised topic models, Adv Neural Inf Process Syst, 3, 327 Michielssen, 1996, A multilevel matrix decomposition algorithm for analyzing scattering from large structures, IEEE Trans. Antennas Propag., 44, 1086, 10.1109/8.511816 Huang, 2016, Meta structure: Computing relevance in large heterogeneous information networks, 1595 Sun, 2011, Pathsim: meta path-based top-k similarity search in heterogeneous information networks, Proc. VLDB Endowment, 4, 992, 10.14778/3402707.3402736 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 He, 2015, Revealing multiple layers of hidden community structure in networks, arXiv preprint arXiv:1501.05700 Li, 2016, Multi-bias non-linear activation in deep neural networks, 221 Gao, 2017, On the properties of the softmax function with application in game theory and reinforcement learning, arXiv preprint arXiv:1704.00805 Schmidt-Hieber, 2017, Nonparametric regression using deep neural networks with relu activation function, arXiv preprint arXiv:1708.06633 Wang, 2016, Structural deep network embedding, 1225 Cao, 2016, Deep neural networks for learning graph representations, 1145 Ruck, 1990, The multilayer perceptron as an approximation to a bayes optimal discriminant function, IEEE Trans. Neural Networks, 1, 296, 10.1109/72.80266 Valsamis, 2017, Employing traditional machine learning algorithms for big data streams analysis: the case of object trajectory prediction, J. Syst. Software, 127, 249, 10.1016/j.jss.2016.06.016 Schmidhuber, 2015, Deep learning in neural networks: an overview, Neural Networks, 61, 85, 10.1016/j.neunet.2014.09.003 Tu, 2017, TransNet: Translation-based network representation learning for social relation extraction, 2864 Prasojo, 2018, Modeling and summarizing news events using semantic triples, 512 Lin, 2015, The extra, restricted connectivity and conditional diagnosability of split-star networks, IEEE Trans. Parallel Distrib. Syst., 27, 533, 10.1109/TPDS.2015.2400459 Smith, 2015, Unpacking the learning–work nexus:primingas lever for high-quality learning outcomes in work-integrated learning curricula, Studies in Higher Education, 40, 22, 10.1080/03075079.2013.806456 Krishnamurthy, 2016 Du, 2018, GPSP: Graph partition and space projection based approach for heterogeneous network embedding, 59 Kheirkhahzadeh, 2016, Efficient community detection of network flows for varying markov times and bipartite networks, Phys. Rev. E, 93, 032309, 10.1103/PhysRevE.93.032309 Tu, 2016, Max-Margin DeepWalk: Discriminative learning of network representation., 3889 Gkantsidis, 2005, Network coding for large scale content distribution, 4, 2235 Yin, 2007, Object distinction: Distinguishing objects with identical names, 1242 Wang, 2010, Mining advisor-advisee relationships from research publication networks, 203