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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 live in a world characterized by massive information transfer and real-time communication. The demand for efficient yet low-complexity algorithms is widespread across different fields, including machine learning, signal processing and communications. Mo ...
Approximate graph matching (AGM) refers to the problem of mapping the vertices of two structurally similar graphs, which has applications in social networks, computer vision, chemistry, and biology. Given its computational cost, AGM has mostly been limited ...
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 paper we deal with the critical node problem (CNP), i.e., the problem of searching for a given number K of nodes in a graph G, whose removal minimizes the (weighted or unweighted) number of connections between pairs of nodes in the residual graph. ...
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 ...
We study the time complexity for deterministic broadcasting algorithms in mobile radio networks. The broadcast operation consists of a source node successfully communicating its message to every other node. In multi-hop radio networks such as MANETs, the m ...
We derive a framework for sampling online communities based on the mean hitting time of its members, considering that there are multiple graphs associated with the same vertex set V representing the social network. First, we formulate random walk models on ...
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 ...
Since the seminal work of Watts in the late 90s, graph-theoretic analyses have been performed on many complex dynamic networks, including brain structures. Most studies have focused on functional connectivity defined between whole brain regions, using imag ...