17 Result(s)
within
Chapter
Luca Rossi
Algorithm Analysis and Problem Complexity
Pattern Recognition
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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
A Quantum Jensen-Shannon Graph Kernel Using Discrete-Time Quantum Walks
In this paper, we develop a new graph kernel by using the quantum Jensen-Shannon divergence and the discrete-time quantum walk. To this end, we commence by performing a discrete-time quantum walk to compute a ...
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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...
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Chapter and Conference Paper
Measuring Vertex Centrality Using the Holevo Quantity
In recent years, the increasing availability of data describing the dynamics of real-world systems led to a surge of interest in the complex networks of interactions that emerge from such systems. Several meas...
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Chapter and Conference Paper
Adaptive Feature Selection Based on the Most Informative Graph-Based Features
In this paper, we propose a novel method to adaptively select the most informative and least redundant feature subset, which has strong discriminating power with respect to the target label. Unlike most tradit...
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Chapter and Conference Paper
A Nested Alignment Graph Kernel Through the Dynamic Time War** Framework
In this paper, we propose a novel nested alignment graph kernel drawing on depth-based complexity traces and the dynamic time war** framework. Specifically, for a pair of graphs, we commence by computing the...
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Chapter and Conference Paper
A Mixed Entropy Local-Global Reproducing Kernel for Attributed Graphs
In this paper, we develop a new mixed entropy local-global reproducing kernel for vertex attributed graphs based on depth-based representations that naturally reflect both local and global entropy based graph ...
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Chapter and Conference Paper
A Preliminary Survey of Analyzing Dynamic Time-Varying Financial Networks Using Graph Kernels
In this paper, we investigate whether graph kernels can be used as a means of analyzing time-varying financial market networks. Specifically, we aim to identify the significant financial incident that changes ...
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Chapter and Conference Paper
A Novel Graph Kernel Based on the Wasserstein Distance and Spectral Signatures
Spectral signatures have been used with great success in computer vision to characterise the local and global topology of 3D meshes. In this paper, we propose to use two widely used spectral signatures, the He...
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Chapter and Conference Paper
Classifying Me Softly: A Novel Graph Neural Network Based on Features Soft-Alignment
Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. In this paper we propose a new graph neural network architecture based on the soft-alignment of the gra...
