Open AccessCapsule neural nets for graph objects classification
Author(s) -
К. А. Майков,
B. N. Smirnov,
A. N. Pylkin,
A. A. Bubnov
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1047/1/012136
Subject(s) - mnist database , computer science , artificial neural network , graph , artificial intelligence , permutation (music) , transformation (genetics) , theoretical computer science , pattern recognition (psychology) , biochemistry , chemistry , physics , acoustics , gene
A new way to solve the graph classification problem is addressed. The main method utilized is the application of a capsule neural network on graphs. The results achieved include, firstly, the enhancement of the base algorithm for training a capsule network with the possibility of using graphs as an input (a stage of training for permutation invariants of graph vertices’ transformation matrices is included as well as a memory block for trained matrices), and secondly, a proposition of a training set of labeled graph objects, transformed from the MNIST dataset. This opens a perspective for a better classification of graph objects due to preserving of their structure and transformation invariance between layers.