Spectral algorithms are classic approaches to clustering and community detection in
networks. However, for sparse networks the standard versions of these algorithms are …

Spectral clustering (SC) is a large family of grou** methods that partition data using
eigenvectors of an affinity matrix derived from the data. Though SC methods have been …

In this paper, we develop a novel backtrackless aligned-spatial graph convolutional network
(BASGCN) model to learn effective features for graph classification. Our idea is to transform …

Graph-based representations are of broad use and applicability in pattern recognition. They
exhibit, however, a major drawback with regards to the processing tools that are available in …

In this paper, a new binary linear programming formulation for computing the exact Graph
Edit Distance (GED) between two graphs is proposed. A fundamental strength of the …

In this paper, we propose a novel framework of computing the Quantum-based Entropic
Representations (QBER) for un-attributed graphs, through the Continuous-time Quantum …

Structural pattern recognition approaches offer the most expressive, convenient, powerful
but computational expensive representations of underlying relational information. To benefit …

Loss of information is the major challenge in graph embedding in vector space which
reduces the impact of representational power of graphs in pattern recognition tasks. The …

The aim of this paper is to explore the use of backtrackless walks and prime cycles for
characterizing both labeled and unlabeled graphs. The reason for using backtrackless walks …

In this paper we aim to characterize graphs in terms of a structural measure of complexity.
Our idea is to decompose a graph into layered substructures of increasing size, and then to …