Graphfingerprint: graph embedding of graphs with almost constant sub-structures

In some machine learning applications, graphs tend to be composed of a large number of tiny almost constant sub-structures. The current embedding methods are not prepared for this type of graphs and thus, their representational power tends to be very low. Our aim is to define a new graph embedding that considers this specific type of graphs. We present GraphFingerprint, which is a new embedding method that specifically considers the fact that graphs are composed of millions of almost constant sub-structures. The three-dimensional characterisation of a chemical metal-oxide nanocompound easily fits in these types of graphs, which nodes are atoms and edges are their bonds. Our graph embedding method has been used to predict the toxicity of these nanocompounds, achieving a high accuracy compared to other embedding methods. The representational power of the current embedding methods do not properly satisfy the requirements of some machine learning applications based on graphs, for this reason, a new embedding method has been defined and heuristically demonstrated that achieves good accuracy.