|
|
|
| The Effect of Negative Links: How Innovation Network Evolves |
| Cheng Lu1, Li Li2 |
| (1.School of Maritime Economics and Management, Dalian Maritime University, Dalian 116024, China;2.College of Economics and Management, Zhengzhou University of Light Industry,Zhengzhou 450002, China) |
|
|
|
|
Abstract Combining the structural balance theory and the dynamic balance theory, this paper focuses on the influence mechanism of negative links of the inter-organizational innovation network structure. To overcome the difficulty of the acquisition of empirical data, the paper conducts an agent-based modeling and simulation research. An inter-organizational innovation network is composed of a group of firms or organizations whose network behaviors contribute the bottom-up result of network evolution.In order to reduce unnecessary complexity, the paper limits firms'network behavior as firms'knowledge search which is based on the “triadic” network mechanism.Then it identifies four kinds of knowledge search behavior of innovative agents or firms, two kind of shielding phenomenon and the dynamic characteristics of knowledge search behavior, and finally builds a multi-agent model to simulate inter-organizational innovation network evolution process of innovation network. #br#It is found that at first negative links will fill the status gaps between the individual agents and keep the whole innovation network at a high level of equality. Secondly, negative links will be make the whole innovation network keep an arresting faction structure and hinder the convergence between factions. Thus, individual agents will be dependent on their factions strongly. At last, different with the previous conclusions, the paper shows that the network cohesion depends not only on the well-known positive links which represent knowledge sharing and knowledge collaboration, but also on negative links which represent hostility, confrontation and knowledge blocking. On the one hand, negative links will suppress the increasing trend of innovation network's positive topology and weaken the small-world phenomenon of the whole network. On the other hand, under the influence of negative links, the individual agent increases the reliance on its own fraction, which leads to closer contact between individual agents within factions.#br#The theoretical contribution of this paper is reflected in three aspects. First this study uses the multi-agent modeling and simulation methods to study the impact of negative connections on the evolution of innovation network structure, providing new research ideas and research perspectives for the introduction of negative connections or innovation networks under the framework of symbolic networks. Secondly, it defines enterprise knowledge search behavior as "knowledge traceability search", "knowledge source sharing search", "diffusion source sharing search" and "alliance search" by introducing the idea of structural balance and dynamic balance of symbolic networks, and enriches knowledge search research.Thirdly, on the basis of the four types of knowledge search behaviors, this study builds a multi-agent simulation model for the evolution of inter-enterprise innovation network structure under the influence of negative connections, which enriches the application of multi-agent modeling and simulation methods in innovation networks, social networks and complex networks.#br#The limitations of this paper are reflected in the following two aspects. First of all, there are many typical network behaviors of innovative individuals,and each basic type governs multiple seed classes. In this study, the network behavior of innovation individuals is limited to the "knowledge search behavior" based on the network transmission mechanism, which will lead to the universality of the multi-agent simulation model of innovation symbol network established on this basis to a certain extent. Secondly, for the multi-agent modeling method itself, although it can overcome the difficulties in obtaining empirical research data to a certain extent, the application of this method in the field of innovation networks has just started, and there is still much room for progress in the corresponding aspects of the model and reality, mainly reflected in that the individual behavior in the simulation model can not be well connected with the actual network behavior of enterprises. The individual behavior described by computer language is too simple and abstract, while the real network behavior of enterprises is more complex and specific. This makes the relevant conclusions difficult to understand. In addition, network structure is the embodiment of network functions and performance, and the negative relationship that has an important impact on the evolution of innovation network structure is not involved in this study. Therefore, in the future research, it is necessary to carry out a deeper expansion in the innovation of individual network behavior and the corresponding relationship between network structure and network performance.#br#
|
|
Received: 09 September 2022
|
|
|
|
|
|
[1] GIULIANI E. Network dynamics in regional clusters:evidence from Chile[J]. Research Policy, 2013, 42(8): 1406-1419.
[2] 石乘齐. 基于组织间依赖的创新网络演化模型及仿真研究[J]. 管理工程学报, 2019, 33(1): 12-22.
[3] CHENG L, LYU Y, SU J, et al. Inbound openness and its impact on innovation performance: an agent-based and simulation approach[J]. R&D Management, 2020, 50(2): 212-226.
[4] RIBEIRO P, MARQUES T S, SANTOS H. Innovation networks anchored into a regional scale: looking for smart specialization tendencies in the portuguese Centro Region[C]//Third European Conference on Social Networks EUSN 2017. 2017.
[5] MENG F, MEDO M, BUECHEL B. Whom to trust in a signed network? optimal solution and two heuristic rules[J]. Information Sciences, 2022, 606: 742-762.
[6] SCHULER P. The broker's invisible ties: brokerage and balance in signed networks[EB/OL]. 2021. https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1595002&dswid=-8610
[7] HARRIGAN N M, LABIANCA G J, AGNEESSENS F. Negative ties and signed graphs research: stimulating research on dissociative forces in social networks[J]. Social Networks, 2020, 60: 1-10.
[8] TEIXEIRA A S, SANTOS F C, FRANCISCO A P. Emergence of social balance in signed networks[C]//International Workshop on Complex Networks. Springer, Cham, 2017.
[9] KEUCHENIUS A, TRNBERG P, UITERMARK J. Why it is important to consider negative ties when studying polarized debates: a signed network analysis of a Dutch cultural controversy on Twitter[J]. PloS one, 2021, 16(8): e0256696.
[10] SUMMERS T H, SHAMES I. Active influence in dynamical models of structural balance in social networks[J]. EPL (Europhysics Letters), 2013, 103(1): 18001.
[11] LERNER J. Structural balance in signed networks: separating the probability to interact from the tendency to fight[J]. Social Networks, 2016, 45: 66-77.
[12] ISAKOV A, FOWLER J H, AIROLDI E M, et al. The structure of negative social ties in rural village networks[J]. Sociological Science, 2019, 6: 197-218.
[13] STADTFELD C, TAKCS K, VRS A. The emergence and stability of groups in social networks[J]. Social Networks, 2020, 60: 129-145.
[14] YANG S W, TRINCADO F, LABIANCA G J, et al. Negative ties at work[M]//Social networks at work. Routledge, 2019: 49-78.
[15] 吕一博, 程露, 苏敬勤. 知识搜索行为与区域创新网络演化：一个多主体仿真研究[J]. 系统工程学报, 2014, 29(6): 725-733，753.
[16] ROPER S, LOVE J H. Knowledge context, learning and innovation: an integrating framework[J]. Industry and Innovation, 2018, 25(4): 339-364.
[17] BURT R S. Structural holes versus network closure as social capital[J]. Social Capital, 2017: 31-56.
[18] MOLLENHORST G, VLKER B, FLAP H. Shared contexts and triadic closure in core discussion networks[J]. Social Networks, 2011, 33(4): 292-302.
[19] YIN H, BENSON A R, UGANDER J. Measuring directed triadic closure with closure coefficients[J]. Network Science, 2020, 8(4): 551-573.
[20] FENG D, ALTMEYER R, STAFFORD D, et al. Testing for balance in social networks[J]. Journal of the American Statistical Association, 2022, 117(537): 156-174.
[21] FRITSCH M, KAUFFELD-MONZ M. The impact of network structure on knowledge transfer: an application of social network analysis in the context of regional innovation networks [J].The Annals of Regional Science, 2010, 44(1): 21-38.
[22] SNIJDERS, TAB, PATTISON P,et al. New specifica-tions for exponential random graph models [J]. Sociological Methodology, 2006, 36(1):99-153.
[23] ROBINS G, PATTISON P, WANG P. Closure, connectivity and degree distributions: Exponential random graph (p*) models for directed social networks [J]. Social Networks, 2009, 31(2): 105-117.
[24] KIRKLEY A, CANTWELL G T, NEWMAN M E J. Balance in signed networks[J]. Physical Review E, 2019, 99(1): 012320.
[25] YAP J, HARRIGAN N. Why does everybody hate me? balance, status, and homophily: the triumvirate of signed tie formation[J]. Social Networks, 2015, 40: 103-122.
[26] ESTRADA E, BENZI M. Are social networks really balanced [EB/OL]. 2014. https://arxiv.org/abs/1406.2132.
[27] HUMMON N P, DOREIAN P. Some dynamics of social balance processes: bringing Heider back into balance theory[J]. Social Networks, 2003, 25(1): 17-49.
[28] GAWRONSKI P, GRONEK P, KULAKOWSKI K. The Heider balance and social distance[J]. Acta Physica Polonica B, 2005,36(8):2549-2558.
[29] MARVEL S A, KLEINBERG J, KLEINBERG R D,et al. Continuous-time model of structural balance[J]. Proceedings of the National Academy of Sciences, 2011, 108(5): 1771-1776.
[30] HOWELLS J. Intermediation and the role of intermediaries in innovation [J]. Research policy, 2006, 35(5): 715-728.
[31] 吕一博, 程露, 苏敬勤. “资源导向” 下中小企业集群网络演进的仿真研究[J]. 科研管理, 2013, 34(1): 131-139.
[32] BONACICH P, LLOYD P. Calculating status with negative relations [J]. Social Networks, 2004, 26(4): 331-338.
[33] KAMVAR S D, SCHLOSSER M T, GARCIA-MOLINA H. The eigentrust algorithm for reputation management in p2p networks [C]//Proceedings of the 12th international conference on World Wide Web. ACM, 2003.
[34] O'MADADHAIN J, FISHERD, SMYTH P, et al. Analysis and visualization of network data using JUNG[J]. Journal of Statistical Software, 2005, 10(2): 1-35.
[35] WATTS D J, STROGATZ S H. Collective dynamics of 'small-world' networks [J]. Nature, 1998, 393(6684): 440-442.
[36] COLEMAN J S. Social capital in the creation of human capital [J]. American Journal of Sociology, 1988, 94: S95-S120.
[37] MOODY J. The structure of a social science collaboration network:disciplinary cohesion from 1963 to 1999[J]. American Sociological Review, 2004, 69(2): 213-238.
|
|
|
|
