Abstract
Social capital captures the positional advantage gained by an individual by being in a social network. A well-known dichotomy defines two types of social capital: bonding capital, which refers to welfare such as trust and norms, and bridging capital, which refers to benefits in terms of influence and power. We present a framework where these notions are mathematically conceptualized. Through the framework, we discuss the process when an individual gains social capital through building new edges. We explore two questions: (1) How would an individual optimally form new relations? (2) What are the impacts of the network structure on the individual’s social capital? For these questions, we adopt a paradigm where the individual is a utility-driven agent who acquires knowledge about the network through repeated trial-and-error. In this paradigm, we propose two reinforcement learning algorithms: one guarantees the convergence to optimal values in theory, while the other is efficient in practice. We conduct experiments over both synthetic and real-world networks. Experimental results indicate that a centralized structure can enhance the performance of learning.
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Notes
- 1.
In experiments, we set the restart probability \(\beta =0.15\).
- 2.
All three real-world network datasets are from the public Koblenz Network Collection. http://konect.uni-koblenz.de/networks/.
References
Alaa, A.M., Ahuja, K., van der Schaar, M.: A micro-foundation of social capital in evolving social networks. IEEE Trans. Netw. Sci. Eng. 5(1), 14–31 (2018)
Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Bourdieu, P.: The forms of capital. In: Handbook of Theory and Research for the Sociology of Education (1986)
Brânzei, S., Larson, K.: Social distance games. In: The 10th International Conference on Autonomous Agents and Multiagent Systems, vol. 3, pp. 1281–1282. International Foundation for Autonomous Agents and Multiagent Systems (2011)
Burt, R.S.: Structural holes and good ideas. Am. J. Sociol. 110(2), 349–399 (2004)
Cai, Y., Zheng, H., Liu, J., Yan, B., Su, H., Liu, Y.: Balancing the pain and gain of hobnobbing: utility-based network building over attributed social networks. In: Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems, pp. 193–201 (2018)
Coleman, J.S.: Foundations of Social Theory. Harvard University Press, Harvard (1994)
Coleman, J.S.: Social capital in the creation of human capital. Am. J. Sociol. 94, S95–S120 (1988)
Erdos, P., Rényi, A.: On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci 5(1), 17–60 (1960)
Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)
Granovetter, M.S.: The strength of weak ties. In: Social Networks, pp. 347–367. Elsevier (1977)
Jackson, M.O.: A survey of network formation models: stability and efficiency. In: Group Formation in Economics: Networks, Clubs, and Coalitions, vol. 664, pp. 11–49 (2005)
Lin, N.: Social Capital: A Theory of Social Structure and Action, vol. 19. Cambridge University Press, Cambridge (2002)
Moskvina, A., Liu, J.: How to build your network? A structural analysis. In: International Joint Conference on Artificial Intelligence (IJCAI-2016), pp. 2597–2603 (2016)
Myers, D.: Relationship rewards. In: Social Psychology, pp. 392–439 (2010)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. Technical report, Stanford InfoLab (1999)
Putnam, R.D.: Bowling Alone: The Collapse and Revival of American Community. Simon and Schuster, New York (2001)
Rossi, R.A., Ahmed, N.K.: The network data repository with interactive graph analytics and visualization. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
Skyrms, B., Pemantle, R.: A dynamic model of social network formation. In: Gross, T., Sayama, H. (eds.) Adaptive Networks. UCS, pp. 231–251. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01284-6_11
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2018)
Tong, H., Faloutsos, C., Pan, J.Y.: Fast random walk with restart and its applications. In: Sixth International Conference on Data Mining (ICDM 2006), pp. 613–622. IEEE (2006)
Watkins, C.J., Dayan, P.: Q-learning. Mach. Learn. 8(3–4), 279–292 (1992)
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440 (1998)
Yan, B., Chen, Y., Liu, J.: Dynamic relationship building: exploitation versus exploration on a social network (2017)
Yan, B., Liu, Y., Liu, J., Cai, Y., Su, H., Zheng, H.: From the periphery to the core: information brokerage in an evolving network. In: International Joint Conference on Artificial Intelligence (IJCAI-2018), pp. 3912–3918 (2018)
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Zhao, H., Su, H., Chen, Y., Liu, J., Yan, B., Zheng, H. (2020). Can Reinforcement Learning Enhance Social Capital?. In: U, L., Yang, J., Cai, Y., Karlapalem, K., Liu, A., Huang, X. (eds) Web Information Systems Engineering. WISE 2020. Communications in Computer and Information Science, vol 1155. Springer, Singapore. https://doi.org/10.1007/978-981-15-3281-8_14
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