Model for Generating Scale-Free Artificial Social Networks Using Small-World Networks

The Internet of Things (IoT) has the potential to be applied to social networks due to innovative characteristics and sophisticated solutions that challenge traditional uses. Social network analysis (SNA) is a good example that has recently gained a lot of scientific attention. It has its roots in social and economic research, as well as the evaluation of network science, such as graph theory. Scientists in this area have subverted predefined theories, offering revolutionary ones regarding interconnected networks, and they have highlighted the mystery of six degrees of separation with confirmation of the small-world phenomenon. The motivation of this study is to understand and capture the clustering properties of large networks and social networks. We present a network growth model in this paper and build a scale-free artificial social network with controllable clustering coefficients. The random walk technique is paired with a triangle generating scheme in our proposed model. As a result, the clustering control mechanism and preferential attachment (PA) have been realized. This research builds on the present random walk model. We took numerous measurements for validation, including degree behavior and the measure of clustering decay in terms of node degree, among other things. Finally, we conclude that our suggested random walk model is more efficient and accurate than previous state-of-the-art methods, and hence it could be a viable alternative for societal evolution.