Prediction of Top Trade Leading Countries Through Social Network Analysis Using Hybrid Classification Techniques

the network of international trade relationships is a natural domain in which to apply the discipline of network analysis. Network analysis can reveal insights into the structures of trade relationship and interdependencies between trade partners that are not immediately evident in a straight out statistical analysis of trade data. There has been a large body of work from the last decade on this very subject. However, most of this previous work has been focused on analyzing the structure of the network and its evolution through time on a global scale. Relative little work has been performed to predict the leading countries in global trade network. Can we build a model that can identify and predict the trade leading country in the trade network? This is the question that we seek to answer. The position of any country can be defined by analyzing the trade history of that country. We have put an effort to visualize the global trade network and predict the country with highest trade relations. The trade network consist of nodes and arcs between them representing countries and the relations respectively. Secondly, there are two views of the network i.e. buyer (Imports) and seller (Exports). The overall aim of our research is to predict the leading country in terms of trade by identifying the most influential country (node) in the trade network. The experimental results and a detailed quantitative analysis show that this is the more efficient and effective way to detect the influential nodes in a trade network.