A Potential Overlapping Community Detection Algorithm for Learners Based on High-order Organization

LI Chunying; LI Tiansong; LIN Weijie; TANG Zhikang; TANG Yong; LIN Ronghua

doi:10.6054/j.jscnun.2023004

Journal of South China Normal University (Natural Science Edition) > 2023 > 55(1): 45-54. > DOI: 10.6054/j.jscnun.2023004

LI Chunying, LI Tiansong, LIN Weijie, TANG Zhikang, TANG Yong, LIN Ronghua. A Potential Overlapping Community Detection Algorithm for Learners Based on High-order Organization[J]. Journal of South China Normal University (Natural Science Edition), 2023, 55(1): 45-54. DOI: 10.6054/j.jscnun.2023004

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A Potential Overlapping Community Detection Algorithm for Learners Based on High-order Organization

LI Chunying^{1, 2},
LI Tiansong¹,
LIN Weijie¹,
TANG Zhikang^1, ,,
TANG Yong^{3, 4},
LIN Ronghua^{3, 4}

1.
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China
2.
Guangdong Provincial Key Laboratory of Intellectual Property & Big Data, Guangdong Polytechnic Normal University, Guangzhou 510665, China
3.
School of Computer Science, South China Normal University, Guangzhou 510631, China
4.
Pazhou Lab, Guangzhou 510330, China

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Received Date: November 09, 2021
Available Online: April 11, 2023

Graphical Abstract

Abstract

Abstract

Learner network topology is sparse and traditional community detection algorithms cannot detect their potential communities for inert/cold start learners. To address this type of problems, a potential overlapping community detection algorithm (POCDL) for learners based on higher-order organizations is proposed. The POCDL algorithm is a local graph clustering algorithm that firstly uses the friend, classmate and teacher-student relationships in socialized online course platforms to solve the sparse learner network data problem, and then mines the higher-order organizations in the learner network and reconstructs the learner network; finally, the initial seed set is selected based on the degree centrality of learners, and local community detection based on community belongingness and community closeness. Experimental results on artificial networks and SCHOLAT dataset show that the POCDL algorithm can detect communities better for inert/cold-start learners, and also has some generalizability to other types of complex networks.
- learner network,
- potential community of learners,
- overlapping communities,
- local community testing,
- high level organization,
- degree centrality

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References

[1]	邬大光, 李文. 我国高校大规模线上教学的阶段性特征——基于对学生、教师、教务人员问卷调查的实证研究[J]. 华东师范大学学报(教育科学版), 2020, 38(7): 1-30. https://www.cnki.com.cn/Article/CJFDTOTAL-HDXK202007001.htm WU D G, LI W. Stage characteristics of large-scale online teaching in chinese universities: empirical research based on group investigation of students, faculty and academic staff[J]. Journal of East China Normal University (Educational Sciences), 2020, 38(7): 1-30. https://www.cnki.com.cn/Article/CJFDTOTAL-HDXK202007001.htm
[2]	周炫余, 李璇, 陈圆圆, 等. 联合知识图谱和时间特性的数学知识自动推荐方法[J]. 武汉大学学报(理学版), 2021, 67(6): 539-546. https://www.cnki.com.cn/Article/CJFDTOTAL-WHDY202106005.htm ZHOU X Y, LI X, CHEN Y Y, et al. Automatic recommendation method of mathematical knowledge based on combined knowledge graph and temporal feature[J]. Journal of Wuhan University (Natural Science Edition), 2021, 67(6): 539-546. https://www.cnki.com.cn/Article/CJFDTOTAL-WHDY202106005.htm
[3]	穆肃, 王孝金. 参与和投入而非肤浅和简单——在线学习中的深层次学习[J]. 中国远程教育, 2019(2): 17-25;92-93. https://www.cnki.com.cn/Article/CJFDTOTAL-DDJY201902004.htm
[4]	MUSTAFARAJ E, BU J. The visible and invisible in a mooc discussion forum[C]//Proceedings of the Second ACM Conference on Learning@Scale. New York: ACM, 2015: 351-354.
[5]	贾慧娟, 刘园, 史爱静, 等. 一种基于标签传播的重叠社区发现算法[J/OL]. 小型微型计算机系统, (2021-04-28)[2021-07-20]. http://kns.cnki.net/kcms/detail/21.1106.TP.20210428.1213.004.html. JIA H J, LIU Y, SHI A J, et al. An algorithm for overla-pping community detection based on label propagation[J/OL]. Journal of Chinese Computer Systems, (2021-04-28)[2021-07-20]. http://kns.cnki.net/kcms/detail/21.1106.TP.20210428.1213.004.html.
[6]	胡钦太. 关于虚拟学习社区的几个问题探讨[J]. 中国电化教育, 2005(6): 26-29. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDJY200506006.htm
[7]	XIE J, SZYMANSKI B K, LIU X. Slpa: uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process[C]//Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops. Vancouver: IEEE, 2011: 344-349.
[8]	GREGORY S. Finding overlapping communities in networks by label propagation[J]. New Journal of Physics, 2010, 12(10): 103018/1-26. doi: 10.1088/1367-2630/12/10/103018
[9]	KUMPULA J M, KIVELA M, KASKI K, et al. Sequential algorithm for fast clique percolation[J]. Physical Review E, 2008, 78(2): 026109/1-7. doi: 10.1103/PhysRevE.78.026109
[10]	LANCICHINETTI A, FORTUNATO S, KERTESZ J. Detecting the overlapping and hierarchical community structure of complex networks[J]. New Journal of Physics, 2009, 11(3): 033015/1-18. doi: 10.1088/1367-2630/11/3/033015
[11]	CHEN Q, WU T T, FANG M. Detecting local community structures in complex networks based on local degree central nodes[J]. Physica A: Statistical Mechanics and its Applications, 2013, 392(3): 529-537. doi: 10.1016/j.physa.2012.09.012
[12]	李艳, 贺静, 武优西. 种子节点贪婪扩张的重叠社区发现方法[J]. 小型微型计算机系统, 2019, 40(5): 1115-1119. https://www.cnki.com.cn/Article/CJFDTOTAL-XXWX201905039.htm LI Y, HE J, WU Y X. Overlapping community discovery method based on greedy expansion of seed nodes[J]. Journal of Chinese Computer Systems, 2019, 40(5): 1115-1119. https://www.cnki.com.cn/Article/CJFDTOTAL-XXWX201905039.htm
[13]	齐金山, 梁循, 王怡. 基于种子节点选择的重叠社区发现算法[J]. 计算机应用研究, 2017, 34(12): 3534-3537;3568. https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ201712003.htm QI J S, LIANG X, WANG Y. Overlapping community detection algorithm based on selection of seed nodes[J]. Application Research of Computers, 2017, 34(12): 3534-3537;3568. https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ201712003.htm
[14]	於志勇, 陈基杰, 郭昆, 等. 基于影响力与种子扩展的重叠社区发现[J]. 电子学报, 2019, 47(1): 153-160. https://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201901020.htm YU Z Y, CHEN J J, GUO K, et al. Overlapping community detection based on influence and seeds extension[J]. Acta Electronica Sinica, 2019, 47(1): 153-160. https://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201901020.htm
[15]	LUO F, WANG J Z, PROMISLOW E. Exploring local community structures in large networks[J]. Web Intelligence and Agent Systems: an International Journal, 2008, 6(4): 387-400.
[16]	BENSON A R, GLEICH D F, LESKOVEC J. Higher-order organization of complex networks[J]. Science, 2016, 353: 163-166.
[17]	孙圣波, 朱保平, 杨晓光. 基于三角模体的社团发现算法[J]. 南京理工大学学报, 2017, 41(1): 35-40. https://www.cnki.com.cn/Article/CJFDTOTAL-NJLG201701005.htm SUI S B, ZHU B P, YANG X G. Community discovery algorithm based on triangular motifs[J]. Journal of Nanjing University of Science and Technology, 2017, 41(1): 35-40. https://www.cnki.com.cn/Article/CJFDTOTAL-NJLG201701005.htm
[18]	LI P Z, HUANG L, WANG C D, et al. Edmot: an edge enhancement approach for motif-aware community detection[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York: ACM, 2019: 479-487.
[19]	LI C Y, TANG Y, TANG Z K, et al. Motif-based embe-dding label propagation algorithm for community detection[J]. International Journal of Intelligence Systems, 2021, 37(3): 1880-1902.
[20]	FREEMAN L C. Centrality in social networks conceptual clarification[J]. Social Networks, 1978-1979, 1(3): 215-239.
[21]	ZHANG L, YAN X. An improved LFM algorithm based on fitness function and community similarity[C]//Procee-dings of the IEEE Xplore. Xiamen: IEEE, 2019: 9-15.
[22]	COSCIA M, ROSSETTI G, GIANNOTTI F, et al. Demon: a local-first discovery method for overlapping communities[C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2012: 615-623.
[23]	LANCICHINETTI A, FORTUNATO S. Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities[J]. Physical Review E, 2009, 80(1): 016118/1-8.
[24]	MCDAID A F, GREENE D, HURLEY N. Normalized mutual information to evaluate overlapping community finding algorithms[J/OL]. ArXiv, (2013-08-02)[2021-07-20]. https://doi.org/10.48550/arXiv.1110.2515.
[25]	SHEN H W, CHENG X Q, CAI K, et al. Detect overlapping and hierarchical community structure in networks[J]. Physica A: Statistical Mechanics and its Applications, 2009, 388(8): 1706-1712.

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