Abstract
Semantic overlapping community detection is an important research hotspot in social networks. However, most of the existing methods ignore the fusion of multi-semantic attributes of users and variability of topological features under the spatial context. Motivated by this, we propose a semantic overlapping community detection method with embedding multi-dimensional relationships and spatial context. Firstly, on the basis of modeling users’ Microblogs by LDA, we further extract the semantic topic information. Two core topic matrices are captured to quantify users’ interest preferences. And the structure of social network is optimized by a new index named Network Constructability in line with user interest preference. Secondly, taking the network topology embedded with implicit semantic information as the core, the seed nodes are generated by leveraging Entropy to fuse explicit information. Finally, under the spatial context, Friend Probability and the Preference Matching Degree are utilized to deeply search the corresponding neighbor nodes in the process of detection. The effectiveness of the method is verified on the real datasets of social networks. The experimental results show that the method has obvious advantages and can detect the high-quality semantic overlapping communities with spatial context in real social networks.
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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Ahn Y, Bagrow JP, Lehmann S (2010) Link communities reveal multiscale complexity in networks. Nature 466(7307):761–764. https://doi.org/10.1038/nature09182
Ahn Y-Y, Bagrow JP, Lehmann S (2010) Link communities reveal multiscale complexity in networks. Nature 466(7307):761–764. https://doi.org/10.1038/nature09182
Asmi K, Lotfi D, Abarda A (2022) The greedy coupled-seeds expansion method for the overlapping community detection in social networks. Computing 104(2):295–313. https://doi.org/10.1007/s00607-021-00948-4
Asmi K, Lotfi D, Abarda A (2022) The greedy coupled-seeds expansion method for the overlapping community detection in social networks. Computing 104(2):295–313. https://doi.org/10.1007/s00607-021-00948-4
Berahmand K, Mohammadi M, Faroughi A, Mohammadiani RP (2022) A novel method of spectral clustering in attributed networks by constructing parameter-free affinity matrix. Clust Comput 25:1–20. https://doi.org/10.1007/s10586-021-03430-0
Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022
Chang F, Zhang B, Li H, Huang M, Li B, Zhao Y (2019) Discovering overlapping communities in ego-nets using friend intimacy. J Intell Fuzzy Syst 36(6):5167–5175. https://doi.org/10.3233/JIFS-172242
Dey P, Chaterjee A, Roy S (2019) Influence maximization in online social network using different centrality measures as seed node of information propagation. Sādhanā 44(9):1–13. https://doi.org/10.1007/s12046-019-1189-7
Ding X, Yang H, Zhang J, Yang J, Xiang X (2022) Ceo: identifying overlapping communities via construction, expansion and optimization. Inf Sci 596:93–118. https://doi.org/10.1016/j.ins.2022.03.012
Fu L, Chen R, Hao W (2021) Weighted network overlapping community partition based on node membership. In: 2021 IEEE international conference on power electronics, computer applications (ICPECA). IEEE, pp 754–758. https://doi.org/10.1109/ICPECA51329.2021.9362636
Gan C, Cao X, Zhu Q (2023) Microblog sentiment analysis via user representative relationship under multi-interaction hybrid neural networks. Multimed Syst. https://doi.org/10.1007/s00530-023-01048-3
Gregory S (2010) Finding overlapping communities in networks by label propagation. New J Phys 12(10):103018. https://doi.org/10.1088/1367-2630/12/10/103018
Hajarathaiah K, Enduri MK, Anamalamudi S, Subba Reddy T, Tokala S (2022) Computing influential nodes using the nearest neighborhood trust value and pagerank in complex networks. Entropy 24(5):704. https://doi.org/10.3390/e24050704
Jia J, Liu P, Du X, Yao Y, Lei Z (2022) The overlapping community discovery algorithm based on the local interaction model. Intell Data Anal 26(1):153–171. https://doi.org/10.3233/IDA-215757
Lancichinetti A, Fortunato S, Kertész J (2009) Detecting the overlapping and hierarchical community structure in complex networks. New J Phys 11(3):033015. https://doi.org/10.1088/1367-2630/11/3/033015
Li Y, He J, Wu Y, Lv R (2020) Overlapping community discovery method based on two expansions of seeds. Symmetry 13(1):18. https://doi.org/10.3390/sym13010018
Li M, Lu S, Zhang L, Zhang Y, Zhang B (2021) A community detection method for social network based on community embedding. IEEE Trans Comput Soc Syst 8(2):308–318. https://doi.org/10.1109/TCSS.2021.3050397
Naderipour M, Fazel Zarandi MH, Bastani S (2022) Fuzzy community detection on the basis of similarities in structural/attribute in large-scale social networks. Artif Intell Rev 55(2):1373–1407. https://doi.org/10.1007/s10462-021-09987-x
Niu Y, Kong D, Liu L, Wen R, Xiao J (2023) Overlapping community detection with adaptive density peaks clustering and iterative partition strategy. Expert Syst Appl 213:119213. https://doi.org/10.1016/j.eswa.2022.119213
Palla G, Derényi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043):814–818. https://doi.org/10.1038/nature03607
Peng Y, Zhang B, Chang F (2021) Overlapping community detection of bipartite networks based on a novel community density. Future Internet 13(4):89. https://doi.org/10.3390/fi13040089
Raghavan UN, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E 76(3):036106. https://doi.org/10.1103/PhysRevE.76.036106
Serena L, Ferretti S, D’Angelo G (2022) Cryptocurrencies activity as a complex network: analysis of transactions graphs. Peer Peer Netw Appl 15(2):839–853. https://doi.org/10.1007/s12083-021-01220-4
Shang R, Zhang W, Zhang J, Feng J, Jiao L (2022) Local community detection based on higher-order structure and edge information. Phys A 587:126513. https://doi.org/10.1016/j.physa.2021.126513
Shang R, Zhang W, Zhang J, Jiao L, Li Y, Stolkin R (2022) Local community detection algorithm based on alternating strategy of strong fusion and weak fusion. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2022.3159584
Shen H, Cheng X, Cai K, Hu M (2009) Detect overlapping and hierarchical community structure in networks. Phys A 388(8):1706–1712. https://doi.org/10.1016/j.physa.2008.12.021
Singh RH, Maurya S, Tripathi T, Narula T, Srivastav G (2020) Movie recommendation system using cosine similarity and knn. Int J Eng Adv Technol 9(5):556–559. https://doi.org/10.35940/ijeat.E9666.069520
Sojahrood ZB, Taleai M (2021) A poi group recommendation method in location-based social networks based on user influence. Expert Syst Appl 171:114593. https://doi.org/10.1016/j.eswa.2021.114593
Wang X, Li J, Yang L, Mi H, Yu JY (2021) Weakly-supervised learning for community detection based on graph convolution in attributed networks. Int J Mach Learn Cybern 12(12):3529–3539. https://doi.org/10.1007/s13042-021-01400-x
Wang J, Li H, Liang L, Zhou Y (2022) Community discovery algorithm of complex network attention model. Int J Mach Learn Cybern 13(6):1619–1631. https://doi.org/10.1007/s13042-021-01471-w
Wu S, Gong J, Liu F, Huang L (2022) Multi-step locally expansion detection method using dispersed seeds for overlapping community. In: ITM web of conferences, vol 47. https://doi.org/10.1051/itmconf/20224702008
Yang X, Chen C, Wang Z (2017) Lfm community discovery algorithm based on node similarity. Complex Syst Complex Sci 14(03):85–90. https://doi.org/10.13306/j.1672-3813.2017.03.008
Zhang S, Wang R, Zhang X (2007) Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Phys A 374(1):483–490. https://doi.org/10.1016/j.physa.2006.07.023
Zhou Y, Chen Z, Liu Z (2023) Dynamic analysis and community recognition of stock price based on a complex network perspective. Expert Syst Appl 213:118944. https://doi.org/10.1016/j.eswa.2022.118944
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Cheng, S., Yang, S., Cheng, X. et al. Semantic overlapping community detection with embedding multi-dimensional relationships and spatial context. Soc. Netw. Anal. Min. 14, 14 (2024). https://doi.org/10.1007/s13278-023-01173-x
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DOI: https://doi.org/10.1007/s13278-023-01173-x