Volodymyr Miz

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Publications associées (5)

Spikyball Sampling: Exploring Large Networks via an Inhomogeneous Filtered Diffusion

Nicolas Aspert, Benjamin Ricaud, Volodymyr Miz

Studying real-world networks such as social networks or web networks is a challenge. These networks often combine a complex, highly connected structure together with a large size. We propose a new approach for large scale networks that is able to automatic ...

2020

What is Trending on Wikipedia? Capturing Trends and Language Biases Across Wikipedia Editions

Nicolas Aspert, Pierre Vandergheynst, Benjamin Ricaud, Volodymyr Miz, Joëlle Hanna

In this work, we propose an automatic evaluation and comparison of the browsing behavior of Wikipedia readers that can be applied to any language editions of Wikipedia. As an example, we focus on English, French, and Russian languages during the last four ...

ACM2020

Dynamic pattern recognition in large-scale graphs with applications to social networks

Volodymyr Miz

A graph is a versatile data structure facilitating representation of interactions among objects in various complex systems. Very often these objects have attributes whose measurements change over time, reflecting the dynamics of the system. This general da ...

EPFL2020

A Graph-structured Dataset for Wikipedia Research

Nicolas Aspert, Pierre Vandergheynst, Benjamin Ricaud, Volodymyr Miz

Wikipedia is a rich and invaluable source of information. Its central place on the Web makes it a particularly interesting object of study for scientists. Researchers from different domains used various complex datasets related to Wikipedia to study langua ...

ACM2019

Anomaly detection in the dynamics of web and social networks

Pierre Vandergheynst, Kirell Maël Benzi, Benjamin Ricaud, Volodymyr Miz

In this work, we propose a new, fast and scalable method for anomaly detection in large time-evolving graphs. It may be a static graph with dynamic node attributes (e.g. time-series), or a graph evolving in time, such as a temporal network. We define an an ...

2019

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