Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Viard, Tiphaine"'
Recent years have witnessed a rapid growth of recommender systems, providing suggestions in numerous applications with potentially high social impact, such as health or justice. Meanwhile, in Europe, the upcoming AI Act mentions \emph{transparency} a
Externí odkaz:
http://arxiv.org/abs/2407.12357
We are faced with data comprised of entities interacting over time: this can be individuals meeting, customers buying products, machines exchanging packets on the IP network, among others. Capturing the dynamics as well as the structure of these inte
Externí odkaz:
http://arxiv.org/abs/2107.13329
Wikipedia is a huge opportunity for machine learning, being the largest semi-structured base of knowledge available. Because of this, many works examine its contents, and focus on structuring it in order to make it usable in learning tasks, for examp
Externí odkaz:
http://arxiv.org/abs/2001.07558
Graphs are commonly used in mathematics to represent some relationships between items. However, as simple objects, they sometimes fail to capture all relevant aspects of real-world data. To address this problem, we generalize them and model interacti
Externí odkaz:
http://arxiv.org/abs/1910.01511
Publikováno v:
In: Holme P., Saram\"aki J. (eds) Temporal Network Theory. Computational Social Sciences. Springer, 2019
We recently introduced a formalism for the modeling of temporal networks, that we call stream graphs. It emphasizes the streaming nature of data and allows rigorous definitions of many important concepts generalizing classical graphs. This includes i
Externí odkaz:
http://arxiv.org/abs/1906.04840
This paper aims at precisely detecting and identifying anomalous events in IP traffic. To this end, we adopt the link stream formalism which properly captures temporal and structural features of the data. Within this framework, we focus on finding an
Externí odkaz:
http://arxiv.org/abs/1906.02524
Autor:
Viard, Tiphaine
Les interactions sont partout : il peut s'agir de contacts entre individus, d'emails, d'appels téléphoniques, de trafic IP, d'achats en ligne, d'exécution de code, etc. Les interactions peuvent être dirigées, pondérées, enrichies d'information
Externí odkaz:
http://www.theses.fr/2016PA066730
While graph-based collaborative filtering recommender systems have been introduced several years ago, there are still several shortcomings to deal with, the temporal information being one of the most important. The new link stream paradigm is aiming
Externí odkaz:
http://arxiv.org/abs/1805.02893
Publikováno v:
Information Processing Letters, 133, 44-48, 2018
Link streams model interactions over time, and a clique in a link stream is defined as a set of nodes and a time interval such that all pairs of nodes in this set interact permanently during this time interval. This notion was introduced recently in
Externí odkaz:
http://arxiv.org/abs/1712.06970
Studying IP traffic is crucial for many applications. We focus here on the detection of (structurally and temporally) dense sequences of interactions, that may indicate botnets or coordinated network scans. More precisely, we model a MAWI capture of
Externí odkaz:
http://arxiv.org/abs/1710.07107