Network analysis is hip. Applications range over social networks, security, biology, and economics. At this point, you'll hardly be the first one to the party, but if you want to give network science a try, here's a random grab-bag of resources to get started.
Learning network science
Jon Kleinberg, a professor of computer science at Cornell University, co-wrote Networks, Crowds, and Markets: Reasoning About a Highly Connected World along with David Easley. He also wrote Algorithm Design, an undergraduate textbook.
A 2004 review paper by Barabasi and Oltvai Network biology: understanding the cell's functional organization. covers a broad range of applications of networks in modern biology. Barabasi is also author of Linked.
A Science special issue on networks, from July 2009, revisits the foundations of network analysis, and delves into applications to ecological interactions, counter-terrorism, and finance.
Video and slides are available for Drew Conway's presentation on social network analysis in R, which mostly focuses on software tools.
Tools for analyzing networks
Software tools for working with networks include the R packages graph, igraph, network. Also, the NetworkX library for Python looks quite powerful. Visualization tools tend to come and go, but some well-known tools are: Cytoscape, Gephi, and GraphViz.
More network stuff
- Synthetic biology: predicting and optimizing gene regulatory networks
- Uri Alon's Network motifs: theory and experimental approaches