Vertex nomination: The canonical sampling and the extended spectral nomination schemes

Vertex Nomination via Content and Context

If I know of a few persons of interest, how can a combination of human language technology and graph theory help me find other people similarly interesting? If I know of a few people committing a crime, how can I determine their co-conspirators? Given a set of actors deemed interesting, we seek other actors who are similarly interesting. We use a collection of communications encoded as an attri...

KETAMINE Nomination 1.0 BASIS OF NOMINATION

Advanced Review Bayesian Vertex Nomination Using Content and Context

Using attributed graphs to model network data has become an attractive approach for various graph inference tasks. Consider a network containing a small subset of interesting entities whose identities are not fully known and that discovering them will be of some significance. Vertex nomination, a subclass of recommender systems relying on the exploitation of attributed graphs, is a task which s...

Vertex nomination via attributed random dot product graphs

The vertex nomination problem addressed in this paper, introduced in Coppersmith and Priebe [2011] and illustrated in Figure 1, involves a (simple, undirected) graph in which vertices have associated attributes (“1” and “2”, say; black and white in the figure). However, we observe the vertex attributes for only a (small) subset of the vertices. One of the vertex attributes identifies vertices o...

Likelihoods for fixed rank nomination networks

Many studies that gather social network data use survey methods that lead to censored, missing, or otherwise incomplete information. For example, the popular fixed rank nomination (FRN) scheme, often used in studies of schools and businesses, asks study participants to nominate and rank at most a small number of contacts or friends, leaving the existence of other relations uncertain. However, m...