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Chapter and Conference Paper
Supervised Learning of Graph Structure
Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learn...
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Chapter and Conference Paper
Approximate Axial Symmetries from Continuous Time Quantum Walks
The analysis of complex networks is usually based on key properties such as small-worldness and vertex degree distribution. The presence of symmetric motifs on the other hand has been related to redundancy and...
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Chapter and Conference Paper
Information Theoretic Prototype Selection for Unattributed Graphs
In this paper we propose a prototype size selection method for a set of sample graphs. Our first contribution is to show how approximate set coding can be extended from the vector to graph domain. With this fr...
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Chapter and Conference Paper
Attributed Graph Similarity from the Quantum Jensen-Shannon Divergence
One of the most fundamental problem that we face in the graph domain is that of establishing the similarity, or alternatively the distance, between graphs. In this paper, we address the problem of measuring th...
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Chapter and Conference Paper
Transitive State Alignment for the Quantum Jensen-Shannon Kernel
Kernel methods provide a convenient way to apply a wide range of learning techniques to complex and structured data by shifting the representational problem from one of finding an embedding of the data to that...
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Chapter and Conference Paper
Node Centrality for Continuous-Time Quantum Walks
The study of complex networks has recently attracted increasing interest because of the large variety of systems that can be modeled using graphs. A fundamental operation in the analysis of complex networks is...
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Chapter and Conference Paper
The Average Mixing Matrix Signature
Laplacian-based descriptors, such as the Heat Kernel Signature and the Wave Kernel Signature, allow one to embed the vertices of a graph onto a vectorial space, and have been successfully used to find the opti...
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Chapter and Conference Paper
Edge Centrality via the Holevo Quantity
In the study of complex networks, vertex centrality measures are used to identify the most important vertices within a graph. A related problem is that of measuring the centrality of an edge. In this paper, we...