A survey on heterogeneous network representation learning
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
