Shuffled Graph Classification: Theory and Connectome Applications

Shuffled Graph Classification: Theory and Connectome Applications

We develop a formalism to address statistical pattern recognition of graph valued data. Of particular interest is the case of all graphs having the same number of uniquely labeled vertices. When the vertex labels are latent, such graphs are called shuffled graphs. Our formalism provides insight to trivially answer a number of open statistical questions including: (i) under what conditions does ...

Applications of Graph Operations

In this paper, some applications of our earlier results in working with chemical graphs are presented.

GRAph Searching , Theory and Applications ( GRASTA 2012 )

Automatic Fingerprint Classification using Graph Theory

automatic fingerprint recognition system. This paper introduces a new structural approach to fingerprint classification by using the directional image of fingerprints to increase the number of subclasses. In this method, the directional image of fingerprints is segmented into regions consisting of pixels with the same direction. Afterwards the relational graph to the segmented image is construc...

Applications of ordinary voltage graph theory to graph embeddability

In part I, we showed that there was an infinite family of graphs not embeddable in the torus in such a way that a free action of any group on any of the graphs extends to a cellular automorphism of the torus. We used a homologically-driven analysis of ordinary voltage graph embeddings. An ordinary voltage graph embedding with voltage group A in a surface encodes a derived embedding of a derived...