Vertex nomination
Glen Coppersmith · Wiley Interdisciplinary Reviews Computational Statistics · 2014
Vertex nomination is a subclass of recommender systems operating on attributed graphs. Attributed graphs are an attractive way to represent data from a diverse set of natural and manmade phenomena. Frequently, these data have latent attributes or class memberships that are of interest and uncovering them is of some intrinsic value. So‐called recommender systems address the ‘more like this’ problem: given a subset of the data labeled as ‘interesting’, find unlabeled examples that are ‘similarly interesting’, often with the assumption that they are from the same latent class or possess similar latent attributes. Unsurprisingly, recommender systems operating on attributed graphs have generated an interesting body of research, detailing both theoretical and practical advances for a range of applications. This advanced review examines the relevant literature, particularly focused on the importance and inclusion of edge‐ and vertex attributes, used in conjunction with the graph structure. We include example applications from human language technology, biology, and neuroscience for concreteness, although the algorithms discussed are widely applicable. WIREs Comput Stat 2014, 6:144–153. doi: 10.1002/wics.1294 This article is categorized under: Algorithms and Computational Methods > Quadratic and Nonlinear Programming