52 citations · 199 across the 19 of their papers we have counts for
7 papers · 1 filter
Efficient Information Diffusion in Time-Varying Graphs through Deep Reinforcement Learning
Matheus R. F. Mendonça, André M. S. Barreto, Artur Ziviani
Network seeding for efficient information diffusion over time-varying graphs~(TVGs) is a challenging task with many real-world applications. There are several ways to model this sp…
An Algorithmic Information Distortion in Multidimensional Networks
Felipe S. Abrahão, Klaus Wehmuth, Hector Zenil +1
Network complexity, network information content analysis, and lossless compressibility of graph representations have been played an important role in network analysis and network m…
Emergence of complex data from simple local rules in a network game
Felipe S. Abrahão, Klaus Wehmuth, Artur Ziviani
As one of the main subjects of investigation in data science, network science has been demonstrated a wide range of applications to real-world networks analysis and modeling. For e…
On the existence of hidden machines in computational time hierarchies
Felipe S. Abrahão, Klaus Wehmuth, Artur Ziviani
Challenging the standard notion of totality in computable functions, one has that, given any sufficiently expressive formal axiomatic system, there are total functions that, althou…
Approximating Network Centrality Measures Using Node Embedding and Machine Learning
Matheus R. F. Mendonça, André M. S. Barreto, Artur Ziviani
Extracting information from real-world large networks is a key challenge nowadays. For instance, computing a node centrality may become unfeasible depending on the intended central…
You Shall not Pass: Avoiding Spurious Paths in Shortest-Path Based Centralities in Multidimensional Complex Networks
Klaus Wehmuth, Artur Ziviani, Leonardo Chinelate Costa +2
In complex network analysis, centralities based on shortest paths, such as betweenness and closeness, are widely used. More recently, many complex systems are being represented by…
DJEnsemble: On the Selection of a Disjoint Ensemble of Deep Learning Black-Box Spatio-Temporal Models
Yania Molina Souto, Rafael Pereira, Rocío Zorrilla +6
In this paper, we present a cost-based approach for the automatic selection and allocation of a disjoint ensemble of black-box predictors to answer predictive spatio-temporal queri…