nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

A New Model for Predicting Node Type Based on Deep Learning

verfasst von : Bo Gong, Daji Ergu, Kuiyi Liu, Ying Cai

Erschienen in: Data Science

Verlag: Springer Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

With the development of the Internet, a large number of data sets are generated, which contain valuable resources. Meanwhile, there are various graphical representations in real life, such as social networks, citation networks, and user networks. For user networks, there also exists rich information about entities except the network structure. Therefore, predicting the type of nodes in the network can help us quickly identify user type, citations type etc. In this paper, a new method based on deep learning is proposed to predict the class of node. Two public data sets are used as training sets. First, the node features are embedded to pre-train the neighbor’s neighborhood structure features, then the pre-trained data is used to input to the classification model, and the structural feature parameters are loaded. The final result shows that the prediction accuracy is increased by nearly 25% higher than the baseline model. The F1 scores of the model tested on the two data sets are 83.5% and 80.2%, respectively.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel An Extension Preprocessing Model for Multi-Criteria Decision Making Based on Basic-Elements Theory

Nächstes Kapitel Flexible Shapelets Discovery for Time Series Classification

Litvak, M., Last, M.: Graph-based keyword extraction for single-document summarization. In: Proceedings of the workshop on Multi-source Multilingual Information Extraction and Summarization, pp. 17–24. Association for Computational Linguistics (2008)

Al-Hami, M., Lakaemper, R., Rawashdeh, M., et al.: Camera localization for a human-pose in 3D space using a single 2D human-pose image with landmarks: a multimedia social network emerging demand. Multimedia Tools Appl. 78(3), 3587–3608 (2019)CrossRef

Collobert, R., Weston, J., Bottou, L., et al.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)

Walke, D., Xie, H., Yan, K.-K., Maslov, S.: Ranking scientific publication using a model of network traffic. J. Stat. Mech. Theory Exp. 2007(06), P06010 (2007)

Lin, M., Chen, Q., Yan, S.: Network in network, arXiv preprint arXiv:1312.4400 (2013)

Bengio, Y., LeCun, Y.: Scaling learning algorithms towards AI. Large-scale kernel machines 34(5), 1–41 (2007)

Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theor. 13(1), 21–27 (1967)CrossRef

Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference, Knowledge Discovery and Data Mining, pp. 855–864. ACM (2016)

Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

Lu, Q., Getoor, L.: Link-based classification. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp. 496–503 (2003)

Géron, A.: Praxiseinstieg Machine Learning mit Scikit-Learn und TensorFlow: Konzepte, Tools und Techniken für intelligente Systeme. O'Reilly (2018)

Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)

Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: Online learning of social representations. In: SIGKDD, pp. 701–710 (2014)

Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 1097–1105 (2012)

Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRef

Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)

Ferdowsi, A., Saad, W.: Deep learning-based dynamic watermarking for secure signal authentication in the Internet of Things. In: Proceedings of IEEE International Conference Communications (ICC), pp. 1–6 (2018)

Association J S Accuracy (trueness and precision) of measurement methods and results - Part 6: Use in practice of accuracy values

Gao, M., Jin, C.Q., Qian, Q., et al.: The real-time personalized recommendation for the micro-blog system. J. Comput. Sci. 04, 963–975 (2014)

Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1225–1234 (2016)

Duvenaud, D.K., Maclaurin, D., Iparraguirre, J., et al.: Convolutional networks on graphs for learning molecular fingerprints. Adv. Neural Inf. Process. Syst. 2224–2232 (2015)

Bhagat, S., Cormode, G., Muthukrishnan, S.: Node classification in social networks. In: Aggarwal, C. (ed) Social Network Data Analytics, pp. 115–148. Springer, Boston (2011). https://doi.org/10.1007/978-1-4419-8462-3_5

Cao, S., Lu, W., Xu, Q.: Deep neural networks for learning graph representations. In: AAAI, pp. 1145–1152 (2016)

Luo, X., Xuan, J., Liu, H.: Web event state prediction model: combining prior knowledge with real time data. J. Web Eng. 13(5&6), 483–506 (2014)

Vaswani, A., Bengio, S., Brevdo, E., et al.: Tensor2tensor for neural machine translation. arXiv preprint arXiv:1803.07416 (2018)

Bayer, A.O., Riccardi, G.: Semantic language models with deep neural networks. Comput. Speech Lang. 40, 1–22 (2016)

Titel: A New Model for Predicting Node Type Based on Deep Learning
verfasst von: Bo Gong
Daji Ergu
Kuiyi Liu
Ying Cai
Verlag: Springer Singapore
Buch: Data Science
Print ISBN: 978-981-15-2809-5

Electronic ISBN: 978-981-15-2810-1

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-981-15-2810-1_20

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner