Structural Hole Theory & Metrics

• {Kleinberg08} Jon Kleinberg, Siddharth Suri, Éva Tardos, Tom Wexler. Strategic Network Formation with Structural Holes. Proc. of ACM EC, 2008.
• {Burt13} Ronald S. Burt, Martin Kilduff, and Stefano Tasselli. Social Network Analysis: Foundations and Frontiers on Advantage. Annual Review of Psychology, 2013, 64:527-547.
• {Li16} Ping Li, Xian Sun, Kai Zhang, Jie Zhang, Juergen Kurths. Role of structural holes in containing spreading processes. Physical Review E, 2016, 93:032312. Based on the distribution of the structural hole numbers associated with each node, we propose a simple yet effective approach for choosing the most influential nodes to immunize in containing the spreading processes
• {Everett20} Martin G. Everett, Stephen P. Borgatti. Unpacking Burt’s constraint measure. Social Networks, 2020, 62:50-57.
• {Chen24} Bo Chen, Panling Jiang, Zhengtao Xiang, Xiue Gao, and Yufeng Chen. Novel Approach to Edge Importance Ranking: Balancing Network Structure and Transmission Performance. To appear: IEEE Systems Journal. the novel edge importance ranking method based on the combination of the improved structure hole and information entropy is proposed

Structural Hole Spanner Discovery

• {Lou13} Tiancheng Lou and Jie Tang. Mining Structural Hole Spanners Through Information Diffusion in Social Networks. Proc. of WWW, 2013. 1% of Twitter users who span structural holes control 25% of the information diffusion on Twitter [Code]
• {Rezvani15} Mojtaba Rezvani, Weifa Liang, Wenzheng Xu, Chengfei Liu. Identifying Top-k Structural Hole Spanners in Large-Scale Social Networks. Proc. of ACM CIKM, 2015. consider the structural hole spanners problem as a set of vertices whose removal will result in the maximum increase on the mean distance of the network
• {Song15} Chonggang Song, Wynne Hsu, Mong Li Lee. Mining Brokers in Dynamic Social Networks. Proc. of ACM CIKM, 2015. design two incremental algorithms WeakTie-Local and WeakTie-Bi to find brokers in evolving social networks
• {He16} Lifang He, Chun-Ta Lu, Jiaqi Ma, Jianping Cao, Linlin Shen, and Philip S. Yu. Joint Community and Structural Hole Spanner Detection via Harmonic Modularity. Proc. of ACM KDD, 2016. use the harmonic function to model the topological nature of community and SH spanners (high space complexity according to the TKDE'17 paper) Code
• {Xu17} Wenzheng Xu, Mojtaba Rezvani, Weifa Liang, Jeffrey Xu Yu, Chengfei Liu. Efficient Algorithms for the Identification of Top-$k$ Structural Hole Spanners in Large Social Networks. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(5):1017-1030.
• {Jiang18} Fei Jiang, Lifang He, Yi Zheng, Enqiang Zhu, Jin Xu and Philip S. Yu. On Spectral Graph Embedding: A Non-Backtracking Perspective and Graph Approximation. Proceedings of the SIAM International Conference on Data Mining (SDM), 2018.
• {Xu19} Wenzheng Xu, Tong Li, Weifa Liang, Jeffrey Xu Yu, Ning Yang, Shaobing Gao. Identifying structural hole spanners to maximally block information propagation. Information Sciences, 2019, 505:100-126.
• {Li19} Faming Li, Zhaonian Zou, Jianzhong Li, Yingshu Li, Yubiao Chen. Distributed Parallel Structural Hole Detection on Big Graphs. Proc. of DASFAA, 2019. proposes a structural hole detection algorithm ESH based on distributed parallel graph processing frameworks
• {Lu20} Mengke Lu. Node importance evaluation based on neighborhood structure hole and improved TOPSIS. Computer Networks, 2020, 178:107336.
• {Gao21} Jie Gao, Fei Hao, Zheng Pei, Geyong Min. Learning Concept Interestingness for Identifying Key Structures From Social Networks. IEEE Transactions on Network Science and Engineering, 2021, 8(4):3220-3232. this paper proposes a novel approach for handling key structures identification tasks simultaneously under the unified Formal Concept Analysis (FCA) framework
• {Li22} Mengshi Li, Jian Peng, Shenggen Ju, Quanhui Liu, Hongyou Li, Weifa Liang, Jeffrey Xu Yu, Wenzheng Xu. Efficient algorithms for finding diversified top-k structural hole spanners in social networks. Information Sciences, 2022, 602:236-258. not only study a problem of finding top-k hole spanners that connect nonredundant communities in the social network, but also consider the tie strengths between different pairs of users and the different information sharing rates of different users
• {Goel22} Diksha Goel, Hong Shen, Hui Tian, Mingyu Guo. Discovering Structural Hole Spanners in Dynamic Networks via Graph Neural Networks. Proc. of IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2022. we aim to discover SHSs in dynamic networks; we design a Graph Neural Network-based model, GNN-SHS, to discover SHSs in dynamic networks, aiming to reduce the computational cost while achieving high accuracy

Structural Hole Applications

• {Gao13} Ge Gao, Pamela Hinds, Chen Zhao. Closure vs. Structural Holes: How Social Network Information and Culture Affect Choice of Collaborators. Proc. of ACM CSCW, 2013. The Chinese, consistent with the cultural value of Guanxi, more closely followed a closure model, whereas Americans favored neither a closure nor a structural holes model; collaborator seekers from the U.S. based their decision largely on the type of connections (e.g. experts vs. people in important positions) in the candidate’s social network whereas collaborators from China tended to consider the existence of shared contacts and type of connections in potential collaborators’ networks together when making a decision.
• {Bhowmik15} Tanmay Bhowmik, Nan Niu, Prachi Singhania, and Wentao Wang. On the Role of Structural Holes in Requirements Identification: An Exploratory Study on Open-Source Software Development. ACM Trans. Manage. Inf. Syst. 2015, 6(3):Article 10, 30 pages. stakeholders positioned in structural holes are positively related to proposing new requirements, and stake- holders’ roles also play an important part in requirements identification
• {Choi16} Eunyoung Choi, Kun Chang Lee. Relationship between social network structure dynamics and innovation: Micro-level analyses of virtual cross-functional teams in a multinational B2B firm. Computers in Human Behavior, 2016, 65:151-162. examine whether social network analysis has a significant effect on new business development (NBD) performance; adpot NBD of patented products to measure innovation performance of ten project groups quantitatively
• {Ying18} Qiu Fang Ying, Dah Ming Chiu, and Xiaopeng Zhang. Diversity of a User’s Friend Circle in OSNs and Its Use for Profiling. Proc. of SocInfo, 2018. develop metrics to measure diversity of a user’s friend circle, borrowing concepts from classic works on structural holes and community detection
• {Kwon19} Young D. Kwon, Reza Hadi Mogavi, Ehsan Ul Haq, Youngjin Kwon, Xiaojuan Ma, and Pan Hui. Effects of Ego Networks and Communities on Self-Disclosure in an Online Social Network. Proc. of ASONAM, 2019.
• {Yang21} Liping Yang, Yu Yang, Gervas Batister Mgaya, Bo Zhang, Long Chen, and Hongbo Liu. Novel Fast Networking Approaches Mining Underlying Structures From Investment Big Data. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2021, 51(10):6319-6329. develop a novel linear-time structure mining algorithm in network (SMAN) for investigating investment groups and structural holes from the investment pedigree
• {Yang21} Aimei Yang, Adam J Saffer. Standing out in a networked communication context: Toward a network contingency model of public attention. New Media & Society, 2021, 23(10):2902–2925. Combine data mining, social network analysis, and machine learning techniques to analyze a large-scale Twitter discussion network. Structural hole constraint significantly and negatively affects mention frequency.
• {Guo21} Min Guo, Naiding Yang, Jingbei Wang, Yanlu Zhang, Yan Wang. How do structural holes promote network expansion? Technological Forecasting and Social Change, 2021, 173:121129. structural holes have a positive impact on network expansion
• {Zamani22} Mehdi Zamani, Haydar Yalcin, Ali Bonyadi Naeini, Gordana Zeba, Tugrul U Daim. Developing metrics for emerging technologies: identification and assessment, Technological Forecasting & Social Change, 2022, 176:121456. Structural hole analysis has been applied to determine the sub-technologies of these technologies that have begun to mature
• {Choi22} Hyeri Choi and Hangjung Zo. Network Closure Versus Structural Hole: The Role of Knowledge Spillover Networks in National Innovation Performance. IEEE Transactions on Engineering Management, 2022, 69(4):1011-1021. The findings show an overall tendency of a negative effect of network closure and a positive one of structural holes on a country's innovative outcome
• {Tsugawa23} Sho Tsugawa, Kohei Watabe. Identifying Influential Brokers on Social Media from Social Network Structure. Proc. of AAAI ICWSM, 2023. compare influential brokers and central nodes based on betweenness, which are expected to be structural hole spanners, and we examine the relationship between the concepts of structural hole spanners and brokers
• {Yang24} Jinyu Yang, Qingqing Bi. R&D Partner's Network Position and Focal Firm's Innovation Performance: A Knowledge Spill-In Perspective. IEEE Transactions on Engineering Management, 2024, 71:5982-5997. R&D partners who have advantageous network positions, such as centrality and structural holes, have greater incoming knowledge flow into the focal firm, which in turn improves the firm’s innovation capability

SH-Aware Prediction

• {Zhao13} Yuchen Zhao, Guan Wang, Philip S. Yu, Shaobo Liu, Simon Zhang. Inferring Social Roles and Statuses in Social Networks. Proc. of ACM KDD, 2013. Clearly, different roles represent diverse degrees to structural holes. The values of HR and EXE are about four and three times of the value of R&D, respectively.
• {Yang15} Yang Yang, Jie Tang, Cane Wing-ki Leung, Yizhou Sun, Qicong Chen, Juanzi Li, and Qiang Yang. RAIN: social role-aware information diffusion. Proc. of AAAI, 2015.
• {Yang16} Yang Yang, Jia Jia, Boya Wu, Jie Tang. Social Role-Aware Emotion Contagion in Image Social Networks. Proc. of AAAI, 2016. users with social roles of opinion leaders and structural hole spanners tend to be more influential than ordinary users in positive emotion contagion while be less influential in negative emotion contagion
• {Wang16} Zhiyuan Wang, Yun Zhou, Jie Tang, Jar-Der Luo. The Prediction of Venture Capital Co-Investment Based on Structural Balance Theory. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(2):537-550. formulate the problem of co-investment prediction into a factor graph model incorporating structural balance theory; in the VC network, the co-investor of my co-investor tends to be my co-investor; VC pairs from the same country, of the same investor type, with short distance, with more common neighbors or with appropriate Jaccard similarity of invested fields are likely to co-invest; VCs of large betweenness or of a large number of invested fields have advantage in the VC network; investors of Asian countries, especially of China, are more likely to have social relations than other countries
• {Tang16} Jie Tang, Tiancheng Lou, Jon Kleinberg, and Sen Wu. Transfer Learning to Infer Social Ties across Heterogeneous Networks. ACM Transactions on Information Systems, 2016, 34(2):Article 7. users are more likely (up to +152% higher than chance) to have the same type of relationship with a user who spans a structural hole
• {Cai18} Wenjing Cai, Jia Jia, Wentao Han. Inferring Emotions from Image Social Networks Using Group-Based Factor Graph Model. Proc. of IEEE ICME, 2018. a joint emotion inference model combining image con-tent, user personalization and group information
• {Ye19} Yuyang Ye, Hengshu Zhu, Tong Xu, Fuzhen Zhuang, Runlong Yu, Hui Xiong. Identifying High Potential Talent: A Neural Network based Dynamic Social Profiling Approach. Proc. of IEEE ICDM, 2019. model the social profiles of employees with both Graph Convolutional Network (GCN) and social centrality analysis in a comprehensive way PDF
• {Zhang23} Fanjin Zhang, Jie Tang, Xueyi Liu, Zhenyu Hou, Yuxiao Dong, Jing Zhang, Xiao Liu, Ruobing Xie, Kai Zhuang, Xu Zhang, Leyu Lin, Philip S. Yu. Understanding WeChat User Preferences and “Wow” Diffusion. IEEE Transactions on Knowledge and Data Engineering}, 2022, 34(12):6033-6046.
• {Zhao23} Zhili Zhao, Ding Li, Yue Sun, Ruisheng Zhang, Jun Liu. Ranking influential spreaders based on both node k-shell and structural hole. Knowledge-Based Systems, 2023, 260:110163. Inspired by the strengths of the k-shell decomposition method, this work improves it on the basis of structural holes (SH) and proposes a novel ranking method, SHKS. Based on the flexible combination of k-shell and SH, SHKS can identify not only the core nodes with large k-shell indices but also the nodes that have small k-shell indices but play an important role in bridging different parts of a network
• {Qin23} Fangyun Qin, Zheng Zheng, Xiaohui Wan, Zhihao Liu, and Zhiping Shi. Predicting Aging-Related Bugs Using Network Analysis on Aging-Related Dependency Networks. IEEE Transactions on Emerging Topics in Computing, 2023, 11(3):566-579. possible to hypothesize that an aging-related dependency network with higher structural hole measures results in a higher ARB tendency
• {Li23} Kunhao Li, Zhenhua Huang, and Zhaohong Jia. RAHG: A Role-Aware Hypergraph Neural Network for Node Classification in Graphs. IEEE Transactions on Network Science and Engineering, 2023, 10(4):2098-2108. propose RAHG, a novel method aggregating node role and adjacency representations by an attention mechanism and hypergraph neural networks

Miscellaneous

• {Huang15} Hong Huang, Jie Tang, Lu Liu, JarDer Luo, Xiaoming Fu. Triadic Closure Pattern Analysis and Prediction in Social Networks. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(12):3374-3389. Structural hole spanner is eager to close an open triad for more social resources (>10×higher than that of three ordinary users). On the other hand, they are also reluctant to have two disconnected friends to be linked together.
• {Wang22} Yaojing Wang, Yuan Yao, Hanghang Tong, Feng Xu, and Jian Lu. Auditing Network Embedding: An Edge Influence Based Approach. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(11):5211-5224. the structural hole is perhaps the most indicative property for high edge influence; the structural hole property has a relatively large impact on the learned embeddings for skip-gram model, i.e., when a few such edges are deleted, the performance of the learned emebddings would significantly decrease

Links

ego networks(https://faculty.ucr.edu/~hanneman/nettext/C9_Ego_networks.html#holes)