Graph matching, also known as network alignment, aims at recovering the latent vertex
correspondence between two unlabeled, edge-correlated weighted graphs. To tackle this …

Loop closure is necessary for correcting errors accumulated in simultaneous localization
and mapping (SLAM) in unknown environments. However, conventional loop closure …

In this paper, we consider the graph alignment problem, which is the problem of recovering,
given two graphs, a one-to-one mapping between nodes that maximizes edge overlap. This …

In this paper we consider alignment of sparse graphs, for which we introduce the
Neighborhood Tree Matching Algorithm (NTMA). For correlated Erdős-R {é} nyi random …

Automatic inference of missing road attributes (eg, road type and speed limit) for enriching
digital maps has attracted significant research attention in recent years. A number of …

The primary concern of numerous online social media network (SMN) platforms is how to
provide users with effective and personalized web services. To achieve this goal, SMN …

For exact 3D shape correspondence (matching or alignment), ie, the task of matching each
point on a shape to its exact corresponding point on the other shape (or to be more specific …

Tensor Kronecker products, the natural generalization of the matrix Kronecker product, are
independently emerging in multiple research communities. Like their matrix counterpart, the …

Network alignment is the task of establishing one-to-one correspondences between the
nodes of different graphs and finds a plethora of applications in high-impact domains …

Graph matching aims at finding the vertex correspondence between two unlabeled graphs
that maximizes the total edge weight correlation. This amounts to solving a computationally …