Networking Strategies and Efficiency in Human Communication Networks
Individuals communicate with each other strategically to improve their access to information and to capitalize on social connections in attaining personal and professional goals. Yet, we know little about how specific networking strategies impact the efficiency of communication networks both at a global and local level. Here, we perform data-driven computer simulations that examine the effect of two predominant networking strategies: (i) structural change, involving addition and deletion of communication channels and (ii) frequency change, involving increase or decrease of communication on existing channels. In our proposed framework, these two strategies encompass the spectrum of exploring new connections and exploiting existing ones, and are implemented based on the generic social processes of interaction reciprocity and triadic closure. Three main results emerge from our simulations. First, our structural and frequency change strategies designed to reflect human behavior differ from null models represented by random strategies. Second, they have distinct effects on global and local efficiency. Third, these strategies work consistently across heterogeneous network structures and various network evolution scenarios. Taken together, our findings reassess conventional wisdom about the effectiveness of networking strategies and introduce novel frameworks to study the impact of networking via modeling approaches informed by social and communication theory.
KeywordsNetworking Strategies Efficiency Reciprocity Triadic Closure Human Communication Network Simulations
This research was partly supported by the National Science Foundation under Grant No. IIS-1755873. The authors would like to thank Zachary Gibson and Nick Hagar for their comments on an earlier version of the manuscript.
- 8.Brass, D.J., Galaskiewicz, J., Greve, H.R., Tsai, W.: Taking stock of networks and organizations: a multilevel perspective. Acad. Manag. J. 47, 795–817 (2004)Google Scholar
- 9.Buchanan, M.: The best is yet to come. Nature 447(39)Google Scholar
- 11.Burt, R.S.: Structural Holes: The Social Structure of Competition. Harvard University Press, Cambridge (1992)Google Scholar
- 13.Chan, H., Akoglu, L., Tong, H.: Make it or break it: Manipulating robustness in large networks. In: Proceedings of the 2014 SIAM International Conference on Data Mining, pp. 325–333 (2014)Google Scholar
- 14.Chen, C., Tong, H., Prakash, B.A., Eliassi-Rad, T., Faloutsos, M., Faloutsos, : C.: Eigen-optimization on large graphs by edge manipulation. ACM Trans. Knowl. Discov. Data 10(4), 49:1–49, 30 (2016)Google Scholar
- 15.Dube, D.E.: This is how much time you spend on work emails every day, according to a Canadian survey. https://globalnews.ca/news/3395457
- 18.Garimella, K., De Francisci Morales, G., Gionis, A., Mathioudakis, M.: Reducing controversy by connecting opposing views. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM 2017, pp. 81–90 (2017)Google Scholar
- 20.Kunegis, J.: Konect: The koblenz network collection. In: Proceedings of the 22nd International Conference on World Wide Web, WWW 2013 Companion, pp. 1343–1350 (2013)Google Scholar
- 21.Latora, V., Marchiori, M.: Efficient behavior of small-world networks. Phys. Rev. Lett. 87(19) (2001)Google Scholar
- 22.Leskovec, J., Krevl, A.: SNAP Datasets: stanford large network dataset collection (2014). http://snap.stanford.edu/data
- 23.Migliano, A.B., Page, A.E., Gómez-Gardeñes, J., Salali, G.D., Viguier, S., Dyble, M., Thompson, J., Chaudhary, N., Smith, D., Strods, J., Mace, R., Thomas, M.G., Latora, V., Vinicius, L.: Characterization of hunter-gatherer networks and implications for cumulative culture. Nat. Hum. Behav. 1, 0043 (2017)CrossRefGoogle Scholar
- 26.Papagelis, M.: Refining social graph connectivity via shortcut edge addition. ACM Trans. Knowl. Discov. Data 10(2), 12:1–12, 35 (2015)Google Scholar