Many important algorithms in computational biology and related subjects rely on the ability to extract and to identify sub-graphs of larger graphs; an example is to find common functional structures within Protein Interaction Networks. However, the increasing size of both the graphs to be searched and the target sub-graphs requires the use of large numbers of parallel conventional CPUs. This pa...

We compare a wide range of semi-supervised learning techniques both for Web spam filtering and for telephone user churn classification. Semisupervised learning has the assumption that the label of a node in a graph is similar to those of its neighbors. In this paper we measure this phenomenon both for Web spam and telco churn. We conclude that spam is often linked to spam while honest pages are...

In this thesis graph spectral methods and kernel methods are combined together for the tasks of rigid and non-rigid feature correspondence matching and consistent labelling. The thesis is divided into five chapters. In Chapter 1 we give a brief introduction and an outline of the thesis. In Chapter 2 we review the main techniques in the literature which are related to the developments in this th...

The (d,1)-total labelling of graphs was introduced by Havet and Yu. In this paper, we prove that, for planar graph G with maximum degree ∆ ≥ 12 and d = 2, the (2,1)-total labelling number λ2 (G) is at most ∆ + 2.

The problem of Radio Labelling is to assign distinct integer labels to all the vertices of a graph, such that adjacent vertices get labels at distance at least two. The objective is to minimize the label span. Radio labelling is a combinatorial model for frequency assignment in case that the transmitters are not allowed to operate at the same channel. We show that radio labelling is related to ...

We study the problem of predicting the labelling of a graph. The graph is given and a trial sequence of (vertex,label) pairs is then incrementally revealed to the learner. On each trial a vertex is queried and the learner predicts a boolean label. The true label is then returned. The learner’s goal is to minimise mistaken predictions. We propose minimum p-seminorm interpolation to solve this pr...

For given integers j ≥ k ≥ 1, an L(j, k)-labelling of a graph Γ is an assignment of labels—nonnegative integers—to the vertices of Γ such that adjacent vertices receive labels that differ by at least j, and vertices distance two apart receive labels that differ by at least k. The span of such a labelling is the difference between the largest and the smallest labels used, and the minimum span ov...

In structural pattern recognition, given a set of graphs, the computation of a Generalized Median Graph is a well known problem. Some methods approach the problem by assuming a relation between the Generalized Median Graph and the Common Labelling problem. However, this relation has still not been formally proved. In this paper, we analyse such relation between both problems. The main result pr...