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A Qualitative Analysis of Edge Closure in Information Networks

Social Networks Analysis is one of the cutting edge research areas which finds applications in Information Retrieval and Data Mining and which deals with very large amounts of data. A considerable amount of research in this field has focused on mining useful information patterns in networks. Other applications have focused primarily on structure of networks. Several models have been proposed to address both issues. Existing models have been developed and replaced with better ones. One direction of this research has focused on how to implement one method of analysis on several data associations in order to understand how different data models behave. Responses to such questions account for the underlying differences in properties of the data considered. In its broadest sense, a community is a large set of nodes that have been collected over a period of time. In the present scenario efforts are being made to develop a complete model that can correctly explain and predict how links are formed in networks and how a network of nodes dynamically ”progresses” into a bigger and more diverse one. If an idea is implemented on networks derived from different domains the results can account for the underlying differences in the properties of the data. Such an approach is useful because it helps to find not only differences based on domain but also identify similarity and therefore to correlate one domain with another.