Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Robardet, Celine"'
GNNs are powerful models based on node representation learning that perform particularly well in many machine learning problems related to graphs. The major obstacle to the deployment of GNNs is mostly a problem of societal acceptability and trustwor
Externí odkaz:
http://arxiv.org/abs/2406.11594
We study the real economic activity in the Bitcoin blockchain that involves transactions from/to retail users rather than between organizations such as marketplaces, exchanges, or other services. We first introduce a heuristic method to classify Bitc
Externí odkaz:
http://arxiv.org/abs/2307.08616
The need of predictive maintenance comes with an increasing number of incidents reported by monitoring systems and equipment/software users. In the front line, on-call engineers (OCEs) have to quickly assess the degree of severity of an incident and
Externí odkaz:
http://arxiv.org/abs/2108.03013
Publikováno v:
Journal of Complex Networks, Volume 10, Issue 2, April 2022, cnac006
Statistical graph models aim at modeling graphs as random realization among a set of possible graphs. One issue is to evaluate whether or not a graph is likely to have been generated by one particular model. In this paper we introduce the edit distan
Externí odkaz:
http://arxiv.org/abs/2106.13587
Graphs are widely used for describing systems made up of many interacting components and for understanding the structure of their interactions. Various statistical models exist, which describe this structure as the result of a combination of constrai
Externí odkaz:
http://arxiv.org/abs/2106.13579
Publikováno v:
Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 944. Springer, Cham
Community detection in graphs often relies on ad hoc algorithms with no clear specification about the node partition they define as the best, which leads to uninterpretable communities. Stochastic block models (SBM) offer a framework to rigorously de
Externí odkaz:
http://arxiv.org/abs/2106.13571
Publikováno v:
Data Min Knowl Disc (2021)
Modeling user preferences (long-term history) and user dynamics (short-term history) is of greatest importance to build efficient sequential recommender systems. The challenge lies in the successful combination of the whole user's history and his rec
Externí odkaz:
http://arxiv.org/abs/2008.05587
The statistical inference of stochastic block models as emerged as a mathematicaly principled method for identifying communities inside networks. Its objective is to find the node partition and the block-to-block adjacency matrix of maximum likelihoo
Externí odkaz:
http://arxiv.org/abs/1910.07879
Autor:
Bendimerad, Anes, Mel, Ahmad, Lijffijt, Jefrey, Plantevit, Marc, Robardet, Céline, De Bie, Tijl
Community detection in graphs, data clustering, and local pattern mining are three mature fields of data mining and machine learning. In recent years, attributed subgraph mining is emerging as a new powerful data mining task in the intersection of th
Externí odkaz:
http://arxiv.org/abs/1905.03040
Publikováno v:
In Data & Knowledge Engineering November 2022 142
