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We compare the structural properties of the street networks of ten different European cities using their primal representation. We investigate the properties of the geometry of the networks and a set of centrality measures highlighting differences and simi ...
Machine learning algorithms are widely used today for analytical tasks such as data cleaning, data categorization, or data filtering. At the same time, the rise of social media motivates recent uptake in large scale graph processing. Both categories of alg ...
Synthetic social contact networks play a central role in the study of epidemics and methods to control them. In this paper we propose a new methodology that combines subjective surveys and data obtained using digital devices to synthesize detailed social n ...
It was recently observed from empirical traffic data that by aggregating the highly scattered plots of flow versus density from individual loop detectors for city regions with homogeneous spatial distribution of congestion, the scatter significantly decrea ...
Adaptive networks consist of a collection of agents with adaptation and learning abilities. The agents interact with each other on a local level and diffuse information across the network through their collaboration. In this work, we consider two types of ...
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Machine learning algorithms are widely used today for analytical tasks such as data cleaning, data categorization, or data filtering. At the same time, the rise of social media motivates recent uptake in large scale graph processing. Both categories of alg ...
We consider the contact process on a random graph with a fixed degree distribution given by a power law. We follow the work of Chatterjee and Durrett [2], who showed that for arbitrarily small infection parameter lambda, the survival time of the process is ...
The task of a neural associative memory is to retrieve a set of previously memorized pat- terns from their noisy versions by using a net- work of neurons. Hence, an ideal network should be able to 1) gradually learn a set of patterns, 2) retrieve the corre ...
Over the past decade, investigations in different fields have focused on studying and understanding real networks, ranging from biological to social to technological. These networks, called complex networks, exhibit common topological features, such as a h ...
In this thesis, we propose novel solutions to similarity learning problems on collaborative networks. Similarity learning is essential for modeling and predicting the evolution of collaborative networks. In addition, similarity learning is used to perform ...