arXiv subjects

Jan Chołoniewski

Publications and source records attributed to Jan Chołoniewski.

5 recordsLinked to original sources

arXiv2021

Detecting Hidden Layers from Spreading Dynamics on Complex Networks

When dealing with spreading processes on networks it can be of the utmost importance to test the reliability of data and identify potential unobserved spreading paths. In this paper we address these problems and propose methods for hidden layer identification and reconstruction. We also explore the interplay between difficulty of the task and the structure of the multilayer network describing the whole system where the spreading process occurs. Our methods stem from an exact expression for the likelihood of a cascade in the Susceptible-Infected model on an arbitrary graph. We then show that by imploring statistical properties of unimodal distributions and simple heuristics describing joint likelihood of a series of cascades one can obtain an estimate of both existence of a hidden layer and its content with success rates far exceeding those of a null model. We conduct our analyses on both synthetic and real-world networks providing evidence for the viability of the approach presented.

physics.soc-ph↗

arXiv2019

Modelling of temporal fluctuation scaling in online news network with independent cascade model

We show that activity of online news outlets follows a temporal fluctuation scaling law and we recover this feature using an independent cascade model augmented with a varying hype parameter representing a viral potential of an original article. We use the Event Registry platform to track activity of over 10,000 news outlets in 11 different topics in the course of the year 2016. Analyzing over 22,000,000 articles, we found that fluctuation scaling exponents $α$ depend on time window size $Δ$ in a characteristic way for all the considered topics -- news outlets activities are partially synchronized for $Δ>15\mathrm{min}$ with a cross-over for $Δ=1\mathrm{day}$. The proposed model was run on several synthetic network models as well as on a network extracted from the real data. Our approach discards timestamps as not fully reliable observables and focuses on co-occurrences of publishers in cascades of similarly phrased news items. We make use of the Event Registry news clustering feature to find correlations between content published by news outlets in order to uncover common information propagation paths in published articles and to estimate weights of edges in the independent cascade model. While the independent cascade model follows the fluctuation scaling law with a trivial exponent $α=0.5$, we argue that besides the topology of the underlying cooperation network a temporal clustering of articles with similar hypes is necessary to qualitatively reproduce the fluctuation scaling observed in the data.

physics.soc-ph↗

arXiv2016

Temporal Taylor's scaling of facial electromyography and electrodermal activity in the course of emotional stimulation

High frequency psychophysiological data create a challenge for quantitative modeling based on Big Data tools since they reflect the complexity of processes taking place in human body and its responses to external events. Here we present studies of fluctuations in facial electromyography (fEMG) and electrodermal activity (EDA) massive time series and changes of such signals in the course of emotional stimulation. Zygomaticus major (ZYG, "smiling" muscle) activity, corrugator supercilii (COR, "frowning"bmuscle) activity, and phasic skin conductance (PHSC, sweating) levels of 65 participants were recorded during experiments that involved exposure to emotional stimuli (i.e., IAPS images, reading and writing messages on an artificial online discussion board). Temporal Taylor's fluctuations scaling were found when signals for various participants and during various types of emotional events were compared. Values of scaling exponents were close to 1, suggesting an external origin of system dynamics and/or strong interactions between system's basic elements (e.g., muscle fibres). Our statistical analysis shows that the scaling exponents enable identification of high valence and arousal levels in ZYG and COR signals.

q-bio.NC↗

arXiv2016

Key courses of academic curriculum uncovered by data mining of students' grades

Learning is a complex cognitive process that depends not only on an individual capability of knowledge absorption but it can be also influenced by various group interactions and by the structure of an academic curriculum. We have applied methods of statistical analyses and data mining (Principal Component Analysis and Maximal Spanning Tree) for anonymized students' scores at Faculty of Physics, Warsaw University of Technology. A slight negative linear correlation exists between mean and variance of course grades, i.e. courses with higher mean scores tend to possess a lower scores variance. There are courses playing a central role, e.g. their scores are highly correlated to other scores and they are in the centre of corresponding Maximal Spanning Trees. Other courses contribute significantly to students' score variance as well to the first principal component and they are responsible for differentiation of students' scores. Correlations of the first principal component to courses' mean scores and scores variance suggest that this component can be used for assigning ECTS points to a given course. The analyse is independent from declared curricula of considered courses. The proposed methodology is universal and can be applied for analysis of student's scores and academic curriculum at any faculty.

physics.ed-ph↗

arXiv2014

The role of emotional variables in the classification and prediction of collective social dynamics

We demonstrate the power of data mining techniques for the analysis of collective social dynamics within British Tweets during the Olympic Games 2012. The classification accuracy of online activities related to the successes of British athletes significantly improved when emotional components of tweets were taken into account, but employing emotional variables for activity prediction decreased the classifiers' quality. The approach could be easily adopted for any prediction or classification study with a set of problem-specific variables.

cs.SI↗