This paper presents a technique for visualizing the differences between two graphs. The technique assumes that a unique labeling of the nodes for each graph is available, where if...
Graphs are a commonly used formalism for modeling many different kinds of static and dynamic data. In many applications, data modeling can be improved by using hierarchically struc...
Giorgio Busatto, Gregor Engels, Katharina Mehner, ...
The paper presents a novel multi-view learning framework based on variational inference. We formulate the framework as a graph representation in form of graph factorization: the g...
We introduce a general method to count and randomly sample unlabeled combinatorial structures. The approach is based on pointing unlabeled structures in an “unbiased” way, i.e...
—Social and communication networks across the world generate vast amounts of graph-like data each day. The modeling and prediction of how these communication structures evolve ca...