Complex Network Formation and Analysis of Online Social Media Systems

To discover and identify the influential nodes in any complex network has been an important issue. It is a significant factor in order to control over the network. Through control on a network, any information can be spread and stopped in a short span of time. Both targets can be achieved, since network of information can be extended and as well destroyed. So, information spread and community formation have become one of the most crucial issues in the world of SNA (Social Network Analysis). In this work, the complex network of twitter social network has been formalized and results are analyzed. For this purpose, different network metrics have been utilized. Visualization of the network is provided in its original form and then filter out (different percentages) from the network to eliminate the less impacting nodes and edges for better analysis. This network is analyzed according to different centrality measures, like edge-betweenness, betweenness centrality, closeness centrality and eigenvector centrality. Influential nodes are detected and their impact is observed on the network. The communities are analyzed in terms of network coverage considering the Minimum Spanning Tree, shortest path distribution and network diameter. It is found that these are the very effective ways to find influential and central nodes from such big social networks like Facebook, Instagram, Twitter, LinkedIn, etc.