We present a unified framework for learning link prediction and edge weight prediction functions in large networks, based on the transformation of a graph's algebraic spectru...
One of the core tasks in social network analysis is to predict the formation of links (i.e. various types of relationships) over time. Previous research has generally represented ...
Public biological databases contain vast amounts of rich data that can also be used to create and evaluate new biological hypothesis. We propose a method for link discovery in biol...
Petteri Sevon, Lauri Eronen, Petteri Hintsanen, Ki...
The problems of object classification (labeling the nodes of a graph) and link prediction (predicting the links in a graph) have been largely studied independently. Commonly, obje...
We introduce a novel Bayesian framework for hybrid community discovery in graphs. Our framework, HCDF (short for Hybrid Community Discovery Framework), can effectively incorporate...
Keith Henderson, Tina Eliassi-Rad, Spiros Papadimi...