Abstract
With the explosion of cellular data, the content sharing in proximity among offline Mobile Social Networks (MSNs) has received significant attention. It is necessary to understand the face-to-face (e.g. Device-to-Device, D2D) social network structure and to predict content propagation precisely, which can be conducted by learning the low-dimensional embedding of the network nodes, called Network Representation Learning (NRL). However, most existing NRL models consider each edge as a binary or continuous value, neglecting rich information between nodes. Besides, many traditional models are almost based on small-scale datasets or online Internet services, severely confining their applications in D2D scenarios. Therefore, we propose ResNel, a RESCAL-based network representation learning model, which aims to regard the multi-dimensional relations as a probability in third-order (3D) tensor space and achieve more accurate predictions for both discovered and undiscovered relations in the D2D social network. Specifically, we consider the Global Positioning System (GPS) information as a critical relation slice to avoid the loss of potential information. Experiments on a realistic large-scale D2D dataset corroborate the advantages of improving forecast accuracy.
Keywords
- Content sharing prediction
- Network representation learning
- Mobile social networks
- Device-to-Device (D2D)
- Relation network
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bedi, P., Sharma, C.: Community detection in social networks. Wiley Interdisc. Rev.: Data Mining Knowl. Disc. 6(3), 115–135 (2016). https://doi.org/10.1002/widm.1178
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Burges, C.J.C., Bottou, L., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)
Cha, M., Kwak, H., Rodriguez, P., Ahn, Y.Y., Moon, S.: I tube, you tube, everybody tubes: analyzing the world’s largest user generated content video system. In: Dovrolis, C., Roughan, M. (eds.) Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 1–14. ACM (2007). https://doi.org/10.1145/1298306.1298309
Chen, L., Shen, C., Vogelstein, J.T., Priebe, C.E.: Robust vertex classification. IEEE Trans. Pattern Anal. Mach. Intell. 38(3), 578–590 (2015). https://doi.org/10.1109/TPAMI.2015.2456913
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Macskassy, S.A., Perlich, C., Leskovec, J., Wang, W., Ghani, R. (eds.) Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM (2016). https://doi.org/10.1145/2939672.2939754
Han, Y., Wang, X., Leung, V., Niyato, D., Yan, X., Chen, X.: Convergence of edge computing and deep learning: A comprehensive survey. arXiv preprint arXiv:1907.08349 abs/1907.08349 (2020). https://doi.org/10.1109/COMST.2020.2970550
Lin, Y., Shen, S., Liu, Z., Luan, H., Sun, M.: Neural relation extraction with selective attention over instances. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. ACL (2016). https://doi.org/10.18653/v1/p16-1200
Lu, X., Yu, Z., Guo, B., Zhou, X.: Predicting the content dissemination trends by repost behavior modeling in mobile social networks. J. Netw. Comput. Appl. 42(3), 197–207 (2014). https://doi.org/10.1016/j.jnca.2014.01.015
Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. In: Getoor, L., Scheffer, T. (eds.) Proceedings of the 28th International Conference on International Conference on Machine Learning, pp. 809–816. Omnipress (2011)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: Online learning of social representations. In: Macskassy, S.A., Perlich, C., Leskovec, J., Wang, W., Ghani, R. (eds.) Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014). https://doi.org/10.1145/2623330.2623732
Shen, S., Han, Y., Wang, X., Wang, Y.: Computation offloading with multiple agents in edge-computing-supported IoT. ACM Trans. Sensor Netw. 16(1), 1–27 (2019). https://doi.org/10.1145/3372025
Sheng, H., et al.: Mining hard samples globally and efficiently for person re-identification. IEEE Internet Things J. (2020). https://doi.org/10.1109/JIOT.2020.2980549
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: Balcan, M., Weinberger, K.Q. (eds.) Proceedings of the 33nd International Conference on Machine Learning, pp. 2071–2080. JMLR (2016)
Tu, C., Zhang, Z., Liu, Z., Sun, M.: Transnet: translation-based network representation learning for social relation extraction. In: Sierra, C. (ed.) Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 2864–2870. IJCAI (2017). https://doi.org/10.24963/ijcai.2017/399
Verma, A., Bharadwaj, K.K.: Identifying community structure in a multi-relational network employing non-negative tensor factorization and GA k-means clustering. Wiley Interdisc. Rev.: Data Mining Knowl. Disc. 7(1), e1196 (2017). https://doi.org/10.1002/widm.1196
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Krishnapuram, B., Shah, M., Smola, A.J., Aggarwal, C.C., Shen, D., Rastogi, R. (eds.) Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225–1234. ACM (2016). https://doi.org/10.1145/2939672.2939753
Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., Chen, M.: In-edge AI: intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Netw. 33(5), 156–165 (2019)
Wang, X., Li, X., Pack, S., Han, Z., Leung, V.C.: STCS: spatial-temporal collaborative sampling in flow-aware software defined networks. IEEE J. Sel. Areas Commun. (2020). https://doi.org/10.1109/JSAC.2020.2986688
Wang, X., Wang, C., Li, X., Leung, V.C., Taleb, T.: Federated deep reinforcement learning for Internet of Things with decentralized cooperative edge caching. IEEE Internet Things J. (2020). https://doi.org/10.1109/JIOT.2020.2986803
Wang, X., Zhang, Y., Leung, V.C., Guizani, N., Jiang, T.: D2d big data: content deliveries over wireless device-to-device sharing in large-scale mobile networks. IEEE Wireless Commun. 25(1), 32–38 (2018). https://doi.org/10.1109/MWC.2018.1700215
Wang, Y., Wei, L., Vasilakos, A.V., Jin, Q.: Device-to-device based mobile social networking in proximity (MSNP) on smartphones: Framework, challenges and prototype. Future Gener. Comput. Syst. 74, 241–253 (2017). https://doi.org/10.1016/j.future.2015.10.020
Yuan, Z., Jiang, Y., Gidófalvi, G.: Geographical and temporal similarity measurement in location-based social networks. In: Chow, C., Shekhar, S. (eds.) Proceedings of the Second ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems, pp. 30–34. ACM (2013). https://doi.org/10.1145/2534190.2534192
Zhang, Y., Huang, Z., Wang, S., Wang, X., Jiang, T.: Spark-based measurement and analysis on offline mobile application market over device-to-device sharing in mobile social networks. In: 2017 IEEE 23rd International Conference on Parallel and Distributed Systems, pp. 545–552. IEEE (2017). https://doi.org/10.1109/ICPADS.2017.00077
Zhou, J., Fan, J.: Translink: user identity linkage across heterogeneous social networks via translating embeddings. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 2116–2124. IEEE (2019). https://doi.org/10.1109/INFOCOM.2019.8737542
Acknowledgment
This work was supported by the National Key Research and Development Program of China under Grant 2019YFB2101901 and 2018YFC0809803, the National Natural Science Foundation of China under Grant 61702364.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Q., Ren, X., Cao, Y., Zhang, H., Wang, X., Leung, V. (2020). Content Sharing Prediction for Device-to-Device (D2D)-based Offline Mobile Social Networks by Network Representation Learning. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12317. Springer, Cham. https://doi.org/10.1007/978-3-030-60259-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-60259-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60258-1
Online ISBN: 978-3-030-60259-8
eBook Packages: Computer ScienceComputer Science (R0)