Ricerc@Sapienza

The most distinctive trait in structural pattern recognition in graph domain is the ability to deal with the organization and relations between the constituent entities of the pattern. Even if this can be convenient and/or necessary in many contexts, most of the state-of the art classification techniques can not be deployed directly in the graph domain without first embedding graph patterns towards a metric space. Granular Computing is a powerful information processing paradigm that can be employed in order to drive the synthesis of automatic embedding spaces from structured domains.

Granular Computing is a powerful information processing paradigm for synthesizing advanced pattern recognition systems in non-conventional domains. In this paper, a novel procedure for the automatic synthesis of suitable information granules is proposed. The procedure leverages a joint sensitivity-vs-specificity score that accounts the meaningfulness of candidate information granules for each class considered in the classification problem at hand.

The most fascinating aspect of graphs is their ability to encode the information contained in the inner structural organization between its constituting elements. Learning from graphs belong to the so-called Structural Pattern Recognition, from which Graph Embedding emerged as a successful method for processing graphs by evaluating their dissimilarity in a suitable geometric space.