Collective Graph Identification


Professor, Computer Science Department at the University of California, Santa Cruz

Graph data (e.g., communication data, financial transaction networks, data describing biological systems, collaboration networks, the Web, etc.) is ubiquitous. While this observational data is useful, it is usually noisy, often only partially observed, and only hints at the actual underlying social, scientific or technological structures that give rise to the interactions. For example, an email communication network provides useful insight, but is not the same as the “real” social network among individuals. In this talk, I introduce the problem of graph identification, i.e., the discovery of the true graph structure underlying an observed network.

Lise’s research areas include machine learning, data integration and reasoning under uncertainty, with an emphasis on graph and network data.