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.

Embedding spaces are one of the mainstream approaches when dealing with structured data. Granular Computing, in the last decade, emerged as a powerful paradigm for the automatic synthesis of embedding spaces that, at the same time, yield an interpretable model on the top of meaningful entities known as information granules. Usually, in these contexts, one aims at finding the smallest set of information granules in order to boost the model interpretability while keeping satisfactory performances.

Natural language processing and text mining applications have gained a growing attention and diffusion in the computer science and machine learning communities. In this work, a new embedding scheme is proposed for solving text classification problems. The embedding scheme relies on a statistical assessment of relevant words within a corpus using a compound index originally proposed in ecology: this allows to spot relevant parts of the overall text (e.g., words) on the top of which the embedding is performed following a Granular Computing approach.

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.

This paper investigates a novel graph embedding procedure based on simplicial complexes. Inherited from algebraic topology, simplicial complexes are collections of increasing-order simplices (e.g., points, lines, triangles, tetrahedrons) which can be interpreted as possibly meaningful substructures (i.e., information granules) on the top of which an embedding space can be built by means of symbolic histograms. In the embedding space, any Euclidean pattern recognition system can be used, possibly equipped with feature selection capabilities in order to select the most informative symbols.

Graphs are powerful structures able to capture topological and semantic information from data, hence suitable for modelling a plethora of real-world (complex) systems. For this reason, graph-based pattern recognition gained a lot of attention in recent years. In this paper, a general-purpose classification system in the graphs domain is presented. When most of the information of the available patterns can be encoded in edge labels, an information granulation-based approach is highly discriminant and allows for the identification of semantically meaningful edges.