Complexity vs. Performance in Granular Embedding Spaces for Graph Classification

Abstract

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. In this paper we investigate several classification techniques starting from Granular Computing-based embedding procedures and provide a thorough overview in terms of model complexity, embedding space complexity and performances on several open-access datasets for graph classification. We witness that certain classification techniques perform poorly both from the point of view of complexity and learning performances as the case of non-linear SVM, suggesting that high dimensionality of the synthesized embedding space can negatively affect the effectiveness of these approaches. On the other hand, linear support vector machines, neuro-fuzzy networks and nearest neighbour classifiers have comparable performances in terms of accuracy, with second being the most competitive in terms of structural complexity and the latter being the most competitive in terms of embedding space dimensionality.

Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis

“…Granular Computing Approach for Labelled Graphs (GRALG) is a classification system suitable for dealing with (fully) labelled graphs grounded on the Granular Computing paradigm [30,31]. GRALG was originally proposed in [32] and later improved in [33][34][35][36][37][38], addressing some computational drawbacks in the original implementation. As anticipated, GRALG follows the Granular Computing paradigm, hence it aims at automatically extracting pivotal mathematical entities known in the technical literature as information granules, able to characterize the data at hand as much as possible.…”