75 citations · 161 across the 9 of their papers we have counts for
9 papers
Inference of hidden common driver dynamics by anisotropic self-organizing neural networks
Zsigmond Benkő, Marcell Stippinger, Zoltán Somogyvári
We are introducing a novel approach to infer the underlying dynamics of hidden common drivers, based on analyzing time series data from two driven dynamical systems. The inference…
Detecting Causality in the Frequency Domain with Cross-Mapping Coherence
Zsigmond Benkő, Bálint Varga, Marcell Stippinger +1
Understanding causal relationships within a system is crucial for uncovering its underlying mechanisms. Causal discovery methods, which facilitate the construction of such models f…
Feature space reduction method for ultrahigh-dimensional, multiclass data: Random forest-based multiround screening (RFMS)
Gergely Hanczár, Marcell Stippinger, Dávid Hanák +4
In recent years, numerous screening methods have been published for ultrahigh-dimensional data that contain hundreds of thousands of features; however, most of these features canno…
BiometricBlender: Ultra-high dimensional, multi-class synthetic data generator to imitate biometric feature space
Marcell Stippinger, Dávid Hanák, Marcell T. Kurbucz +3
The lack of freely available (real-life or synthetic) high or ultra-high dimensional, multi-class datasets may hamper the rapidly growing research on feature screening, especially…
Manifold-adaptive dimension estimation revisited
Zsigmond Benkő, Marcell Stippinger, Roberta Rehus +6
Data dimensionality informs us about data complexity and sets limit on the structure of successful signal processing pipelines. In this work we revisit and improve the manifold-ada…
Complete Inference of Causal Relations between Dynamical Systems
Zsigmond Benkő, Ádám Zlatniczki, Marcell Stippinger +5
From ancient philosophers to modern economists, biologists, and other researchers, there has been a continuous effort to unveil causal relations. The most formidable challenge lies…
Universality and scaling laws in the cascading failure model with healing
Marcell Stippinger, János Kertész
Cascading failures may lead to dramatic collapse in interdependent networks, where the breakdown takes place as a discontinuity of the order parameter. In the cascading failure (CF…
Hybrid Phase Transition into an Absorbing State: Percolation and Avalanches
Deokjae Lee, S. Choi, M. Stippinger +2
Interdependent networks are more fragile under random attacks than simplex networks, because interlayer dependencies lead to cascading failures and finally to a sudden collapse. Th…
Enhancing resilience of interdependent networks by healing
Marcell Stippinger, János Kertész
Interdependent networks are characterized by two kinds of interactions: The usual connectivity links within each network and the dependency links coupling nodes of different networ…