Graph Creation and Analysis for Linking Actors: Application to Social Data
Link : http://store.elsevier.com/Automating-Open-Source-Intelligence/Robert-Layton-/isbn-9780128029169/
Introduction
The world is evolving and increasingly getting more and more complex. From a technological perspective, this is expressed by the amount of gener- ated data, for example with personalized services provided to Internet users. The challenge of modeling such complexity for gathering information requires appropriate models and algorithms to work with. One of the most natural ways to represent our world is to consider objects and their interactions and communication/exchange. Internet, humans, enterprises, proteins are all related to this paradigm of interactions. An enterprise, a society or a social network takes roots in relationships between employees, individuals. Genes, proteins are getting efficient in the way they interact together through chemical exchanges. The secrets of our brain may be related to the challenge of modeling the interactions between neural cells.
The graph theory provides a model for analyzing entities and the relationships between them. In this chapter, we introduce key concepts of graph theory and more precisely of social network analysis. From graph creation through en- tity disambiguation, relationships identification, and weighting to the graph analysis, we present here a set of metrics and tools that can contribute to the information gathering. In particular, a case study of this chapter presents how to use social media to gather intelligence. We highlight the graph creation and analysis using a Twitter dataset of approximately 100,000 tweets related to open source intelligence from the January 26, 2015 to the April 26, 2015.