Hugo Gualdron

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Inference problems on networks and their algorithms were always important subjects, but more so now with so much data available and so little time to make sense of it. Common applications range from product recommendation to social networks and protein interaction. One of the main inferences in this types of networks is the guilty-by-association method,(More)
Recent graph computation approaches have demonstrated that a single PC can perform efficiently on billion-scale graphs. While these approaches achieve scalability by optimizing I/O operations, they do not fully exploit the capabilities of modern hard drives and processors. To overcome their performance, in this work, we introduce the Bimodal Block(More)
Given a large-scale graph with millions of nodes and edges, how to reveal macro patterns of interest, like cliques, bi-partite cores, stars, and chains? Furthermore, how to visualize such patterns altogether getting insights from the graph to support wise decision-making? Although there are many algorithmic and visual techniques to analyze graphs, none of(More)
The use of graph theory for analyzing network-like data has gained central importance with the rise of the Web 2.0. However, many graph-based techniques are not well-disseminated and neither explored at their full potential, what might depend on a complimentary approach achieved with the combination of multiple techniques. This paper describes the(More)
Currently, link recommendation has gained more attention as networked data becomes abundant in several scenarios. However, existing methods for this task have failed in considering solely the structure of dynamic networks for improved performance and accuracy. Hence, in this work, we present a methodology based on the use of multiple topological metrics in(More)
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