The attainment of Google has made the phrase “to Google” synonymous with the search for anything. The success mantra behind Google’s dominance as a search engine is its ability to exploit the direct and indirect connections 173among the humongous amounts of data. Google has adopted the graph-centric approach to represent and model the relationships between documents and has used graph analysis techniques to understand their semantics and contexts and find out relevant information. The more vast and diverse data it can explore, the more relevant information can be extracted. With the explosion of the digital world, the amount of digital data originated from varied sources is increasing colossally. In today’s world, the devices, processes, people, and other entities are becoming more connected than ever before. As the number of data sources increases, it is vital that we define the underlying relationships between them. Social media, sensors, surveillance intelligence, industrial control systems, and connected devices have become visibly important in the data-procurement scenario. With this explosive increase of global data, the term “big data” was coined and mainly used to describe huge datasets that are generated from numerous heterogeneous sources [1]. Mining and analyzing these data helps us to get an in-depth knowledge of the hidden values that bring new opportunities for discovering new values. As the variety and volume of our data increase, more sophisticated methods are required to unveil the valuable information hidden in it.