Network analysis

Network analysis is the analysis of networks through network theory (or more generally graph theory).

The networks may be social, transportation or virtual, such as the internet.

Analysis include descriptions of structure, such as small-world networks or scale-free networks, optimisation, such as Critical Path Analysis and PERT (Program Evaluation & Review Technique), and properties such as flow assignment.

Social network analysis maps relationships between individuals in social networks.

Network analysis, and its close cousin traffic analysis, has significant use in intelligence. By monitoring the communication patterns between the network nodes, its structure can be established. This can be used for uncovering insurgent networks of both hierarchical and leaderless nature.

Link analysis is a subset of network analysis, exploring associations between objects. An example may be examining the addresses of suspects and victims, the telephone numbers they dialed and financial transactions they partaked in in a given timeframe, and the familial relationships between these subjects as a part of police investigation. Link analysis here provides the crucial relationships and associations between very many objects of different types that are not apparent from isolated pieces of information. Computer-assisted or fully automatic computer-based link analysis is increasingly employed by banks and insurance agencies in fraud detection, by telecommunication operators in telecommunication network analysis, by medical sector in epidemiology and pharmacology, in law enforcement investigations, by search engines for relevance rating (and conversely by the spammers for spamdexing and by business owners for search engine optimization), and everywhere else where relationships between many objects have to be analyzed.

Analysis of Graphs

Information about the relative importance of nodes and edges in a graph can be mined by studying the Adjacency matrix. The eigenvectors of the said matrix contain information about the centrality of nodes besides other things. An example is the Page rank algorithm used by Google. The principal Eigenvector of the modified adjacency matrix of the www-graph gives the page ranks as its components.