Abstract
We describe a guilt-by-association system that can be used to rank
entities by their suspiciousness. We demonstrate the algorithm on a
suite of data sets generated by a terrorist-world simulator developed
under a DoD program. The data sets consist of thousands of people and
some known links between them. We show that the system ranks truly
mali-cious individuals highly, even if only relatively few are known
to be malicious ex ante. When used as a tool for identifying promising
data-gathering opportunities, the sys-tem focuses on gathering more
information about the most suspicious people and thereby increases the
density of link-age in appropriate parts of the network. We assess
per-formance under conditions of noisy prior knowledge (score quality
varies by data set under moderate noise), and whether augmenting the
network with prior scores based on profiling information improves the
scoring (it doesnt). Al-though the level of performance reported here
would not support direct action on all data sets, it does recommend
the consideration of network-scoring techniques as a new source of
evidence in decision making. For example, the system can operate on
networks far larger and more com-plex than could be processed by a
human analyst.