Abstract
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.
Highlights
The recent era has marked great development in network science
The progress in network science led to the concepts of expressing complex systems in the form of networks by representing the elements/entities of the system
If we talk about the World Wide Web, it can be represented as a complex network considering the systems or devices as the nodes and the communication between those systems, as ‘edges’ with the frequency of communication representing weights
Summary
After the concepts of Scale-free and Small-world models, complex systems are analyzed. The increasing number of users and online communities in social networks has attracted large amount of research and interest of organizations This is to discover the information dissemination patterns in large scale networks, leading to a wide range of research and much work to be done in this field. Research work led to the formation of networks from almost any problem, with the basic system elements as nodes and the relation between those elements represented by edges. This could be analyzed systematically to find the appropriate feasible solution. The network approach makes use of different models and methods to understand the system and propose the solution [13–15]
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