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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
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
An Edge-Based Matching Kernel Through Discrete-Time Quantum Walks
In this paper, we propose a new edge-based matching kernel for graphs by using discrete-time quantum walks. To this end, we commence by transforming a graph into a directed line graph. The reasons of using the...
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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...