Review of Structural Diversity Studies on Social Networks
Lu Yingjie1,2,Zhang Yinglong2()
1School of Computer Science and Engineering, Minnan Normal University, Zhangzhou 363099, China 2School of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363099, China
【目的】 对基于社会网络的结构多样性相关研究成果进行总结梳理并展望,为后续的相关研究提供参考与借鉴。【文献范围】 以“Structural Diversity”“Structural Diversity and Social Networks”“结构多样性”“结构多样性 and 社会网络”等检索式分别在Web of Science、Microsoft Academic、DBLP等英文数据库以及CNKI、万方、维普等中文数据库进行检索,限定发表时间为2012年4月至2022年4月,共得到1 619篇文献,经过整理阅读并通过引文网络或数据库检索等方式对代表性文献涉及的相关理论进行溯源,最终筛选出55篇相关文献进行评述。【方法】 对结构多样性进行理论溯源,分析概括其存在的问题,从模型改进、高效算法、实际应用三个主要方面论述结构多样性的研究现状,并对未来研究提出展望。【结果】 结构多样性为基于网络拓扑结构特征,研究影响个体做出重大决策机制的模型。但原始模型存在普适性较差、模型精度不够高等问题,与图挖掘技术结合优化后表现优秀,已被应用于多领域中。【局限】 只针对结构多样性研究进行梳理总结,未能与其他社会传染理论进行比较。【结论】 图挖掘算法可以在一定程度上消除结构多样性模型存在的群体划分缺陷;结构多样性可以作为寻找高影响力节点的指标且需要高效搜索算法作为支撑;结构多样性已在行为预测、链接预测等领域有所应用,并可与其他特征组合优化模型,但依旧需要更多实际应用的检验。
[Objective] This paper reviews the latest developments of the structural diversity studies on social networks and discusses their future directions. [Coverage] We searched the Web of Science, Microsoft Academic, DBLP, CNKI, Wanfang Data and VIP with “Structural Diversity”, “Structural Diversity and Social Networks ”. A total of 55 representative and related literature published from April 2012 to April 2022 were retrieved. [Methods] First, we traced to the source of structural diversity studies and analyzed their existing issues. Then, we examined the structural diversity research from three perspectives: model improvements, efficient algorithms, and practical applications. Finally, we discussed future works. [Results] Structural diversity is a model based on network topology features, which studies factors affecting individuals’ major decision makings. The original model has the bad universality and low precision issues. Combined with graph mining technology, the new model performs well and has been applied in many fields. [Limitations] We only summarized research on structural diversity and did not compare them with other social contagion theories. [Conclusions] Graph mining algorithm could improve the structural diversity model in group division. Structural diversity is an indicator for finding highly influential nodes and required by efficient search algorithms. Structural diversity has been applied in the fields of behavior and link predictions. Features optimizing this model merit more research to evaluated their performance.
鲁英杰, 张应龙. 基于社会网络的结构多样性研究综述*[J]. 数据分析与知识发现, 2022, 6(8): 1-11.
Lu Yingjie, Zhang Yinglong. Review of Structural Diversity Studies on Social Networks. Data Analysis and Knowledge Discovery, 2022, 6(8): 1-11.
(Meng Xiaofeng, Li Yong, Zhu Jianhua. Social Computing in the Era of Big Data: Opportunities and Challenges[J]. Journal of Computer Research and Development, 2013, 50(12): 2483-2491.)
[4]
Ugander J, Backstrom L, Marlow C, et al. Structural Diversity in Social Contagion[J]. PNAS, 2012, 109(16): 5962-5966.
doi: 10.1073/pnas.1116502109
pmid: 22474360
[5]
Aggarwal C. Social Network Data Analytics[M]. New York: Springer, 2011.
[6]
Bon G L. The Crowd: A Study of the Popular Mind[M]. New York: Dover Publications, 2002.
[7]
Latané B. The Psychology of Social Impact[J]. American Psychologist, 1981, 36(4): 343-356.
doi: 10.1037/0003-066X.36.4.343
[8]
Jones M B, Jones D R. Preferred Pathways of Behavioral Contagion[J]. Journal of Psychiatric Research, 1995, 29(3): 193-209.
pmid: 7473296
[9]
Hodas N O, Lerman K. The Simple Rules of Social Contagion[J]. Scientific Reports, 2014, 4: Article No.4343.
[10]
Coleman J, Katz E, Menzel H. The Diffusion of an Innovation among Physicians[A]// Social Networks[M]. Amsterdam: Elsevier, 1977: 107-124.
[11]
Bakshy E, Eckles D, Yan R, et al. Social Influence in Social Advertising: Evidence from Field Experiments[C]// Proceedings of the 13th ACM Conference on Electronic Commerce. 2012: 146-161.
[12]
Watts D J. A Simple Model of Global Cascades on Random Networks[J]. PNAS, 2002, 99(9): 5766-5771.
doi: 10.1073/pnas.082090499
[13]
Hasan S, Ukkusuri S V. A Threshold Model of Social Contagion Process for Evacuation Decision Making[J]. Transportation Research Part B: Methodological, 2011, 45(10): 1590-1605.
doi: 10.1016/j.trb.2011.07.008
(He Gaoqi, Bian Xiaohui, Sun Fei, et al. Crowd Emotional Contagion Model Based on the Epidemic Mechanism under Emergencies[J]. Journal of East China University of Science and Technology (Natural Science Edition), 2018, 44(6):909- 917, 949.)
(Wang Zhenjun, Wang Shuhui, Zhang Weigang, et al. Social Content Based Latent Influence Propagation Model[J]. Chinese Journal of Computers, 2016, 39(8): 1528-1540.)
[16]
Su J, Kamath K, Sharma A, et al. An Experimental Study of Structural Diversity in Social Networks[C]// Proceedings of the 14th International AAAI Conference on Web and Social Media. 2020: 661-670.
[17]
Liang H. Decreasing Social Contagion Effects in Diffusion Cascades: Modeling Message Spreading on Social Media[J]. Telematics and Informatics, 2021, 62: Article No.101623.
[18]
Granovetter M S. The Strength of Weak Ties[J]. American Journal of Sociology, 1973, 78(6): 1360-1380.
doi: 10.1086/225469
[19]
Friedkin N E. Information Flow Through Strong and Weak Ties in Intraorganizational Social Networks[J]. Social Networks, 1982, 3(4): 273-285.
doi: 10.1016/0378-8733(82)90003-X
[20]
Lazega E, Burt R S. Structural Holes: The Social Structure of Competition[J]. Revue Française de Sociologie, 1995, 36(4): Article No.779.
[21]
Zhang Y F, Wang L, Zhu J J H, et al. The Strength of Structural Diversity in Online Social Networks[J]. Research, 2021, 2021(4): 117-126.
[22]
Huang J B, Huang X, Xu J L. Truss-Based Structural Diversity Search in Large Graphs[C]// Proceedings of 2021 IEEE 37th International Conference on Data Engineering. 2021: 2346-2347.
[23]
Huang J B, Huang X, Zhu Y Y, et al. Parameter-Free Structural Diversity Search[C]// Proceedings of Web Information Systems Engineering. 2019: 677-693.
[24]
Huang J B, Huang X, Zhu Y Y, et al. Parallel Algorithms for Parameter-Free Structural Diversity Search on Graphs[J]. World Wide Web, 2021, 24(1): 397-417.
doi: 10.1007/s11280-020-00843-6
[25]
Huang X, Cheng H, Li R H, et al. Top-K Structural Diversity Search in Large Networks[J]. The VLDB Journal, 2015, 24(3): 319-343.
doi: 10.1007/s00778-015-0379-0
[26]
Chang L J, Zhang C, Lin X M, et al. Scalable Top-K Structural Diversity Search[C]// Proceedings of the 33rd International Conference on Data Engineering.IEEE, 2017: 95-98.
[27]
Zhang Q, Li R H, Yang Q X, et al. Efficient Top-K Edge Structural Diversity Search[C]// Proceedings of the 36th International Conference on Data Engineering. IEEE, 2020: 205-216.
(Chen Fen, Fu Xi, He Yuan, et al. Identifying Weibo Opinion Leaders with Social Network Analysis and Influence Diffusion Model[J]. Data Analysis and Knowledge Discovery, 2018, 2(12): 60-67.)
[29]
Xu W Z, Liang W F, Lin X L, et al. Finding Top-K Influential Users in Social Networks Under the Structural Diversity Model[J]. Information Sciences, 2016, 355-356: 110-126.
[30]
Huang X Y, Tiwari M, Shah S. Structural Diversity in Social Recommender Systems[C]// Proceedings of the 5th ACM RecSys Workshop on Recommender Systems and the Social Web. 2013.
[31]
Fang Z P, Zhou X Y, Tang J, et al. Modeling Paying Behavior in Game Social Networks[C]// Proceedings of the 23rd ACM International Conference. 2014: 411-420.
[32]
Gao S, Ma J, Chen Z M. Popularity Prediction in Microblogging Network[C]// Proceedings of Asia-Pacific Web Conference. 2014: 379-390.
[33]
Seidman S B. Network Structure and Minimum Degree[J]. Social Networks, 1983, 5(3): 269-287.
doi: 10.1016/0378-8733(83)90028-X
(Bai Lingen, Chen Zhiqun, Wang Rongbo, et al. Empirical Analysis on K-core of Microblog Following Relationship Network[J]. New Technology of Library and Information Service, 2013(11): 68-74.)
[35]
Emerson A I, Andrews S, Ahmed I, et al. K-Core Decomposition of a Protein Domain Co-occurrence Network Reveals Lower Cancer Mutation Rates for Interior Cores[J]. Journal of Clinical Bioinformatics, 2015, 5: Arcicle No.1.
[36]
He X, Zhao H, Cai W, et al. Analyzing the Structure of Earthquake Network by K-Core Decomposition[J]. Physica A: Statistical Mechanics and Its Applications, 2015, 421(1): 34-43.
doi: 10.1016/j.physa.2014.11.022
[37]
Cohen J D. Trusses: Cohesive Subgraphs for Social Network Analysis[J]. National Security Agency Technical Report, 2008: 1-29.
[38]
Moody J, White D R. Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups[J]. American Sociological Review, 2003, 68(1): 103-127.
doi: 10.2307/3088904
[39]
Guo R C, Shaabani E, Bhatnagar A, et al. Toward Early and Order-of-Magnitude Cascade Prediction in Social Networks[J]. Social Network Analysis and Mining, 2016, 6(1): 1-18.
doi: 10.1007/s13278-015-0311-z
[40]
Marin E, Guo R, Shakarian P. Temporal Analysis of Influence to Predict Users’ Adoption in Online Social Networks[C]// Proceedings of International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation. 2017: 254-261.
[41]
Yuan Y, Altenburger K M, Kooti F. Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests[C]// Proceedings of the Web Conference 2021. 2021: 3359-3370.
[42]
Sánchez-Arrieta N, González R A, Cañabate A, et al. Social Capital on Social Networking Sites: A Social Network Perspective[J]. Sustainability, 2021, 13(9): 5147.
doi: 10.3390/su13095147
[43]
Aral S, Nicolaides C. Exercise Contagion in a Global Social Network[J]. Nature Communications, 2017, 8(1): Article No. 14753.
[44]
Spiliotopoulos T, Oakley I. Understanding Motivations for Facebook Use: Usage Metrics, Network Structure, and Privacy[C]// Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2013: 3287-3296.
[45]
Zhang J, Liu B, Tang J, et al. Social Influence Locality for Modeling Retweeting Behaviors[C]// Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2013: 2761-2767.
[46]
Dong Y X, Johnson R A, Xu J, et al. Structural Diversity and Homophily: A Study Across More than One Hundred Big Networks[C]// Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017: 807-816.
[47]
Sanz-Cruzado J, Castells P. Enhancing Structural Diversity in Social Networks by Recommending Weak Ties[C]// Proceedings of the 12th ACM Conference on Recommender Systems. 2018: 233-241.
[48]
Bao Q, Cheung W K, Zhang Y, et al. A Component-Based Diffusion Model with Structural Diversity for Social Networks[J]. IEEE Transactions on Cybernetics, 2017, 47(4): 1078-1089.
doi: 10.1109/TCYB.2016.2537366
[49]
Backstrom L, Kleinberg J, Lee L, et al. Characterizing and Curating Conversation Threads: Expansion, Focus, Volume, Re-Entry[C]// Proceedings of the 6th ACM International Conference on Web Search and Data Mining. 2013: 13-22.
[50]
Qiu J Z, Tang J, Ma H, et al. DeepInf: Social Influence Prediction with Deep Learning[C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018: 2110-2119.
[51]
Gelper S, van der Lans R, van Bruggen G. Competition for Attention in Online Social Networks: Implications for Seeding Strategies[J]. Management Science, 2021, 67(2): 1026-1047.
doi: 10.1287/mnsc.2019.3564
[52]
Malliaros F D, Giatsidis C, Papadopoulos A N, et al. The Core Decomposition of Networks: Theory, Algorithms and Applications[J]. The VLDB Journal, 2020, 29(1): 61-92.
doi: 10.1007/s00778-019-00587-4
[53]
Chen C M. Searching for Intellectual Turning Points: Progressive Knowledge Domain Visualization[J]. PNAS, 2004, 101(S1): 5303-5310.
doi: 10.1073/pnas.0307513100
(Wang Linxu, Yan Chengxi. Bibliometric Analysis of Research on Artificial Inteligence in Information Science[J]. Documentation, Information & Knowledge, 2020(1): 53-62.)