INTRODUCTION TO COMPLEX NETWORK ANALYSIS
Complex Network Analysis studies how to recognise, describe, visualise and analyse complex networks. The most prominent way of analysing networks is using Python Library NetworkX which provides a prominent way for constructing and drawing complex Neural Networks.
The prominent reason for the explosion of CNA research and applications is due to two factors - one is the availability of cheap and powerful computers that enables researchers and scientists with advanced training in mathematics, physics and social sciences to perform top notch researches and the other factor is the ever increasing complexity of social, behavioural, biological , financial and technological aspects of humanity.
Network is a relational form of organisation and representation of discrete data. The two most important concepts concerning networks are entities and the relationships between them. Entities are referred to as nodes and relationships are known as edges. Network nodes and edges are high level abstractions and for most networks their true nature is not essential. When it is essential, we represent nodes and edges by adding properties also known as attributes. A relationship or edge typically involves two discrete entities or nodes although an entity can be in a relationship with itself, such a relationship is referred to as reflexive.
