Networks are complex systems, mapped connections of elements that may have passive or active participation in the system. For example, the networks in nature, such as the water cells, stark flying patterns and human hormones’ interactions demonstrate that network systems apply to both living and non-living nature, the whole, and the pieces of the whole. The networks that operate the technological systems such as the Internet are submerged into the same rules of self-governance as the networks in the outer world that were not created by humans. Interestingly, scholars suggest that these technological networks are closer to living organisms than people are used to thinking, and are controlled by similar rules of self-governance. This paper aims to discuss the networks and their complexity in the framework of technology and the history of mankind.
The programming languages in the information technology field are based on simple logic. The networks, including the Internet and the software of social networking, are the results or ‘surface’ of such programming. Moreover, the processes of their application or usage can be described by graphs, which are handy for measuring the related data. In general, all the systems are controlled by the mathematical truths that are present, regardless of whether they were already discovered or not (Barabási, 2003, p. 17). However, unlike the initial guesses of the best mathematicians in the early 20th century, most graphs that describe the real networks are not regular but rather highly complex, to the extent that they were desperately called ‘random.’
However, the ‘random’ connections in human networks are usually not random, but highly clustered, although the weak social links proved to have the highest impact on the connections with the outside world and social success. Examples of such success are activities like spreading rumors, advertising one’s goods or services, and getting a job (Barabási, 2003). Therefore, mathematicians developed graph theories that describe social reality long before the Internet and Facebook appeared. The described clustered world was enhanced by hubs and connectors, which sped up the interactions within the system.
Modern scholars study the science of networks and connection through many perspectives, including those mentioned above, and more. Caldarelli (2020) says that “complexity is indeed one of the features of this modern world, and it feels very familiar as we are embedded in the ever-increasing number of relationships we establish in this hyper-connected world” (p. 3). Therefore, knowledge of simple mathematical theories, such as random graphs, cluster theories, and the value of the weak social links, allows for a more organized consideration of modern complex social systems in the field of information technologies.
Many scientists are fascinated by the idea of an ever-growing volume of unique original data produced by billions of network participants. At the same time, there are widespread forecasts about the constant growth of the contribution of developing countries to these processes (Caldarelli, 2020). Interestingly, according to the latest IBM report, around 90% of data was created in the last two years, which gives an interesting perspective on the future of data (Caldarelli, 2020, p. 4). The author notes that “complexity science has many common approaches with statistical physics (another discipline that deals with a large number of elements)” (Caldarelli, 2020, p. 4). Interestingly, both sciences operate with such characteristics of processes as nonlinearity and high adaptability, although complex networks are rather the object of mathematical research. Visualization, such as graphics, is a critical element of network complexity science, as it contributes to understanding and analysis.
Objectively, it would be more logical to reduce the name of the discussed topic to ‘social networks informational complexity.’ Moreover, this informational complexity has the advantage of practical applicability in most social sciences. In this regard, Wang and Street (2018) discuss the viral marketing that uses the WOM tool. Marketers choose people with high social network potential – connectors or ‘influencers’ – and use them to spread influence on social networks. In this way, network effects, to which mathematical models can be applied, have a practical purpose and achieve it quite easily.
Another example of the adaptation of social systems to solving social problems can be seen in the example of cultural ideas and connections. Chua (2018) emphasizes that in companies having employees from different cultures, there was a higher level of new ideas related to the culture, regardless of belonging to the origin of the culture under the consideration. In other words, such a phenomenon proves the complexity of networks through the extrapolation of systemic connections in social networks.
Thus, the networks and their complexity in the framework of technology and the history of mankind were discussed. Interestingly, the science of the complexity of networks is well combined with the science of mathematical structure and is highly applicable for understanding society and its key processes. The set of relationships between members of society and institutions is constantly growing, expanding the reach of science. Therefore, understanding the structure of this interdependence is possible through mathematical analysis, which will simplify the work in various fields of social knowledge.
References
Barabási, A. L. (2003). Linked: The new science of networks. Perseus Publishing.
Caldarelli, G. (2020). A perspective on complexity and networks science. Journal of Physics: Complexity, 1(2), 1-12. Web.
Chua, R. Y. (2018). Innovating at cultural crossroads: How multicultural social networks promote idea flow and creativity. Journal of Management, 44(3), 1119-1146. Web.
Wang, W., & Street, W. N. (2018). Modeling and maximizing influence diffusion in social networks for viral marketing. Applied network science, 3(1), 1-26. Web.