Publications
Scalable methods for representing, characterizing, and generating large graphs
Goal - design methods to characterize and identify a low dimensional representation of graphs. Impact - enabling predictive simulation; monitoring dynamics on graphs; and sampling and recovering network structure from limited observations. Areas to explore are: (1) Enabling technologies - develop novel algorithms and tailor existing ones for complex networks; (2) Modeling and generation - Identify the right parameters for graph representation and develop algorithms to compute these parameters and generate graphs from these parameters; and (3) Comparison - Given two graphs how do we tell they are similar? Some conclusions are: (1) A bad metric can make anything look good; (2) A metric that is based an edge-by edge prediction will suffer from the skewed distribution of present and absent edges; (3) The dominant signal is the sparsity, edges only add a noise on top of the signal, the real signal, structure of the graph is often lost behind the dominant signal; and (4) Proposed alternative: comparison based on carefully chosen set of features, it is more efficient, sensitive to selection of features, finding independent set of features is an important area, and keep an eye on us for some important results.