Abstract
Community detection algorithms are used to analyze the community structure. However, existing algorithms are based on undirected or undimensional network structure, which cannot make full use of the user’s attributes. Based on the academic social network, we propose an algorithm that integrates multidimensional network structures and user attributes, which can divide community by using an improved Label Propagation Algorithm (LPA). We construct a real social network by integrating multidimensional network, and use the similarity of research fields of scholars to generate edges between nodes. The directed edges are weighted by the four-dimensional network and temporal attributes, while the nodes are weighted by the social status and influences in the network structure of users. Then, the propagation probability of label is defined by the weighted indicators, and the community is divided via the directed weighted academic network. The experimental results show that the proposed algorithm LPA-STN can effectively improve not only the accuracy of the community detection results, but also the compactness within the communities.
This work was supported in part by the National Natural Science Foundation of China under Grant 61772211 and Grant U1811263.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, N., et al.: A review of the research progress of social network structure. Complex 2021, 6 692 210:1–6 692 210:14 (2021). https://doi.org/10.1155/2021/6692210
Dall’Amico, L., Couillet, R., Tremblay, N.: Community detection in sparse time-evolving graphs with a dynamical bethe-hessian. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H., (eds.) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, 6–12 December 2020 (2020). https://proceedings.neurips.cc/paper/2020/hash/54391c872fe1c8b4f98095c5d6ec7ec7-Abstract.html
Ji, Y., et al.: Temporal heterogeneous interaction graph embedding for next-item recommendation. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds.) ECML PKDD 2020. LNCS (LNAI), vol. 12459, pp. 314–329. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67664-3_19
Meng, Q., Xiong, H.: A doctor recommendation based on graph computing and LDA topic model. Int. J. Comput. Intell. Syst. 14(1), 808–817 (2021). https://doi.org/10.2991/ijcis.d.210205.002
Maity, S., Rath, S.K.: Extended clique percolation method to detect overlapping community structure. In: 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014, Delhi, India, 24–27 September 2014, pp. 31–37. IEEE (2014). https://doi.org/10.1109/ICACCI.2014.6968420
He, K., Li, Y., Soundarajan, S., Hopcroft, J.E.: Hidden community detection in social networks. Inf. Sci. 425, 92–106 (2018). https://doi.org/10.1016/j.ins.2017.10.019
Pang, S., Ban, T., Kadobayashi, Y., Kasabov, N.K.: LDA merging and splitting with applications to multiagent cooperative learning and system alteration. IEEE Trans. Syst. Man Cybern. Part B 42(2), 552–564 (2012). https://doi.org/10.1109/TSMCB.2011.2169056
Zhang, J., Zhu, Y., Chen, Z.: Evolutionary game dynamics of multiagent systems on multiple community networks. IEEE Trans. Syst. Man Cybern. Syst. 50(11), 4513–4529 (2020). https://doi.org/10.1109/TSMC.2018.2854294
Li, P., Chien, I.E., Milenkovic, O.: Optimizing generalized pagerank methods for seed-expansion community detection. In: Wallach, M., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E.B., Garnett, R.H., (eds.) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8–14 December 2019, Vancouver, BC, Canada, pp. 11 705–11 716 (2019). https://proceedings.neurips.cc/paper/2019/hash/9ac1382fd8fc4b631594aa135d16ad75-Abstract.html
Shi, P., He, K., Bindel, D., Hopcroft, J.E.: Locally-biased spectral approximation for community detection. Knowl. Based Syst. 164, 459–472 (2019). https://doi.org/10.1016/j.knosys.2018.11.012
Deng, X., Wen, Y., Chen, Y.: Highly efficient epidemic spreading model based LPA threshold community detection method. Neurocomputing 210, 3–12 (2016). https://doi.org/10.1016/j.neucom.2015.10.142
Dey, A., Jenamani, M., Thakkar, J.J.: Lexical TF-IDF: an n-gram feature space for cross-domain classification of sentiment reviews. In: Shankar, B.U., Ghosh, K., Mandal, D.P., Ray, S.S., Zhang, D., Pal, S.K. (eds.) PReMI 2017. LNCS, vol. 10597, pp. 380–386. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69900-4_48
Dupuy, C., Bach, F., Diot, C.: Qualitative and descriptive topic extraction from movie reviews using LDA. In: Perner, P. (ed.) MLDM 2017. LNCS (LNAI), vol. 10358, pp. 91–106. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62416-7_7
Polpinij, J., Srikanjanapert, N., Sopon, P.: Word2Vec approach for sentiment classification relating to hotel reviews. In: Meesad, P., Sodsee, S., Unger, H. (eds.) IC2IT 2017. AISC, vol. 566, pp. 308–316. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-60663-7_29
Kirkley, A., Newman, M.E.J.: Representative community divisions of networks, CoRR, vol. abs/2105.04612 (2021). https://arxiv.org/abs/2105.04612
Singh, J., Singh, A.K.: NSLPCD: topic based tweets clustering using node significance based label propagation community detection algorithm. Ann. Math. Artif. Intell. 89(3–4), 371–407 (2021). https://doi.org/10.1007/s10472-020-09709-z
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gu, W., Mao, C., Lin, R., Chen, W., Tang, Y. (2022). An Improved Label Propagation Algorithm for Community Detection Fusing Temporal Attributes. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2021. Communications in Computer and Information Science, vol 1492. Springer, Singapore. https://doi.org/10.1007/978-981-19-4549-6_24
Download citation
DOI: https://doi.org/10.1007/978-981-19-4549-6_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-4548-9
Online ISBN: 978-981-19-4549-6
eBook Packages: Computer ScienceComputer Science (R0)
