arXiv subjects

Hang-Hyun Jo

Publications and source records attributed to Hang-Hyun Jo.

At least 19 recordsLinked to original sources

arXiv2026

Quantifying concurrency in event-based temporal network and hypergraph data

Many social, biological, and technological systems are recorded as sequences of time-stamped interactions. In such systems, concurrency, i.e., the tendency for an individual to participate in multiple interactions approximately at the same time, can strongly affect processes such as epidemic or information spreading. However, concurrency measures for event-based temporal network data are not established. We introduce edge-event correlation (EEC), a simple and interpretable measure that quantifies how similarly two connections are active over time. We apply EEC to empirical temporal networks and temporal hypergraphs, the latter allowing single events to involve more than two nodes. Across most datasets, pairs of edges or hyperedges that share a node show higher concurrency than pairs that do not. We further find that this elevated concurrency is mainly driven by pairs embedded in closed local structures, such as triangles in the aggregated network. EEC provides a practical tool for quantifying concurrency in event-based temporal data and may help identify network structures that facilitate rapid spreading or collective dynamics.

physics.soc-ph↗

arXiv2026

Editorial Trajectories in Wikipedia Reflect Underlying Hyperlink Structure

Wikipedia hyperlinks have primarily been studied as navigational tools for readers, but their role in how information providers move between articles during editing remains less explored. Here, we combine the hyperlink network among English Wikipedia articles with editorial histories to examine how article-to-article structure is associated with editors' transitions between articles. We first address the temporal aspect of edit transitions by showing that transitions between hyperlinked article pairs have shorter inter-event times (IETs) than those between non-hyperlinked pairs, indicating that connected articles are effectively closer in editing sequences. We then turn to the structural organization of editing behavior by coarse-graining the hyperlink network into 19 topical communities and measuring editors' topical diversity. Finally, we bring the temporal and structural views together by comparing each editor's transition network with the corresponding hyperlink subnetwork using Jaccard similarity. Combining the measures allows us to distinguish three editor types: 'Specialists' are characterized by focused editing within limited topical domains and transition patterns more closely aligned with the hyperlink structure (low topical diversity, shorter mean IETs, and higher Jaccard similarity), whereas 'generalists' cover broader topics and show weaker similarity to the hyperlink structure (high topical diversity, longer mean IETs, and lower Jaccard similarity). 'Bots' show a distinct algorithm-driven behavior, with low Jaccard similarity and the shortest mean IETs, a combination departing from human-editor patterns despite their often high topical diversity. Such findings demonstrate that the hyperlink structure is not just a static scaffold for reader navigation, but is observationally linked to the sequential organization of editorial activity in collaborative knowledge systems.

physics.soc-ph↗

arXiv2026

Modeling non-Poissonian temporal hypergraphs by Markovian node dynamics

Temporal hypergraphs capture time-resolved group interactions among nodes. Empirical data support that time-stamped group interactions show bursty event sequences and non-trivial temporal correlations. In the present study, we introduce node-driven temporal hypergraph models in which each node stochastically alternates between low- and high-activity states, and a hyperedge produces time-stamped events with a probability that depends on the number of high-state nodes in the hyperedge. For two event-generation rules, we analytically derive interevent time distributions and autocorrelation functions of event sequences, both for hyperedges and nodes. Despite Markovian node-state dynamics, the induced event processes become mixtures of Poissonian, short-tailed components, resulting in longer-tailed interevent time distributions and slowly decaying autocorrelation. The theory further shows the dependence of these features on the size of hyperedge, which largely agrees with various empirical data. We expect our models to provide a simple, interpretable framework for connecting individual-level activity fluctuations to the timing patterns observed in real group interactions.

physics.soc-ph↗

arXiv2025

Analysis framework for higher-order temporal correlations with applications to human heartbeats

We propose a time series analysis framework focused on higher-order temporal correlations in the event sequence beyond the interevent time distribution by employing the burst-tree decomposition method. Bursts are clustered events that rapidly occur within shorter time periods, and they are separated by relatively longer inactive periods. The burst-tree decomposition method exactly maps the event sequence onto a tree, called a burst tree, in which each internal node represents a merge of consecutive bursts at the timescale separating those bursts. The burst tree fully reveals the hierarchical structure of bursts, hence the higher-order temporal correlations for the entire range of timescales. Those correlations are quantified using novel and existing measures derived from the burst tree, such as the burst complexity, memory coefficient for bursts, and principal and secondary cross sections of the burst-merging kernel. We apply our framework to the heartbeat time series of healthy people and of those with heart disease to reveal distinct multiscale temporal properties in physiological time series.

physics.data-an↗

arXiv2025

Hierarchical organization of bursty trains in event sequences

Temporal sequences of discrete events that describe natural and social processes are often driven by non-Poisson dynamics. In addition to a heavy-tailed interevent time distribution, which primarily captures the deviation from a Poisson process, a heavy tail in the distribution of bursty train sizes is frequently observed, which implies the presence of higher-order temporal correlations that extend beyond interevent times. Here, we study empirical event sequences from different domains to show that the bursty trains in these processes are hierarchically structured across different timescales, and that such hierarchical organization gives rise to the higher-order temporal correlations. We propose a dynamic algorithm that generates event sequences with hierarchical structures with arbitrary precision. The algorithm successfully reproduces the features of real-world phenomena, implying the presence of memory mechanisms embedded in system dynamics across multiple timescales.

physics.soc-ph↗

arXiv2025

Copula-based analytical results of horizontal visibility graphs for correlated time series

The visibility graph (VG) algorithm and its variants have been extensively studied in the time series analysis as they transform the time series into the network of nodes and links, enabling to characterize the time series in terms of network measures such as degree distributions. Despite numerous practical applications of VGs in various disciplines, analytical, rigorous understanding of VGs for the correlated time series is still far from complete due to the lack of mathematical tools for modeling the correlation structure in the time series in a tractable form. In this work, we adopt the Farlie-Gumbel-Morgenstern (FGM) copula method to derive the analytical solutions of degree distributions of the horizontal visibility graph (HVG) and its directed version (DHVG) for the correlated time series. Our analytical results show exactly how the correlation between consecutive data points affects the degree distributions of HVGs and DHVGs up to the first order of the correlation parameter in the FGM copula. Thus, our findings shed light on the rigorous understanding of the VG algorithms.

physics.data-an↗

arXiv2025

Ranking dynamics in movies and music

Ranking systems are widely used to simplify and interpret complex data across diverse domains, from economic indicators and sports scores to online content popularity. While previous studies including the Zipf's law have focused on the static, aggregated properties of ranks, in recent years researchers have begun to uncover generic features in their temporal dynamics. In this work, we introduce and study a series of system-level indices that quantify the compositional changes in ranking lists over time, and also characterize the temporal ranking trajectories of individual items' ranking dynamics. We apply our method to analyze ranking dynamics of movies from the over-the-top services, including Netflix, as well as that of music items in Spotify charts. We find that newly released movies or music items influence most the system-level compositional changes of ranking lists; the highest ranks of items are strongly correlated with their lifetimes in the lists more than their first and last ranks. Our findings offer a novel lens to understand collective ranking dynamics and provide a basis for comparing fluctuation patterns across various ordered systems.

physics.soc-ph↗

arXiv2025

Maximum likelihood estimation of burst-merging kernels for bursty time series

Various time series in natural and social processes have been found to be bursty. Events in the time series rapidly occur within short time periods, forming bursts, which are alternated with long inactive periods. As the timescale defining bursts increases, individual events are sequentially merged to become small bursts and then bigger ones, eventually leading to the single burst containing all events. Such a merging pattern has been depicted by a tree that fully reveals the hierarchical structure of bursts, thus called a burst tree. The burst-tree structure can be simply characterized by a burst-merging kernel that dictates which bursts are merged together as the timescale increases. In this work, we develop the maximum likelihood estimation method of the burst-merging kernel from time series, which is successfully tested against the time series generated using several model kernels. We also apply our method to some empirical time series from various backgrounds. Our method provides a useful tool to precisely characterize the time series data, hence enabling to study their underlying mechanisms more accurately.

physics.data-an↗

arXiv2025

Analysis of the autocorrelation function for time series with higher-order temporal correlations: An exponential case

Temporal correlations in the time series observed in various systems have been characterized by the autocorrelation function. Such correlations can be explained by heavy-tailed interevent time distributions as well as by correlations between interevent times. The latter is called higher-order temporal correlations, and they have been captured by the notion of bursts; a burst indicates a set of consecutive events that rapidly occur within a short time period and are separated from other bursts by long time intervals. The number of events in the burst is called a burst size. Some empirical analyses have shown that consecutive burst sizes are correlated with each other. To study the impact of such correlations on the autocorrelation function, we devise a model generating a time series with higher-order temporal correlations by employing the copula method. We successfully derive the analytical solution of the autocorrelation function of the model time series for arbitrary distributions of interevent times and burst sizes when consecutive burst sizes are correlated. For the demonstration of our analysis, we adopt exponential distributions of interevent times and burst sizes to understand how the correlations between consecutive burst sizes affect the decaying behavior of the autocorrelation function.

physics.comp-ph↗

arXiv2024

Measuring and Modeling Bursty Human Phenomena

Bursty dynamics characterizes systems that evolve through short active periods of several events, which are separated by long periods of inactivity. Systems with such temporal heterogeneities are not only found in nature but also include examples from most aspects of human dynamics. In this Chapter, we briefly introduce such bursty phenomena by first walking through the most prominent observations of bursty human behavior. We then introduce several conventional measures that have been developed for characterizing bursty phenomena. Finally, we discuss the fundamental modeling directions proposed to understand the emergence of burstiness in human dynamics through the assumption of task prioritization, temporal correlations, and external factors. This Chapter is only a concise introduction to the field. Still, it provides the most important references, which will help interested readers to learn in depth the ever-growing research area of bursty human dynamics.

physics.soc-ph↗

arXiv2024

Burst-tree structure and higher-order temporal correlations

Understanding characteristics of temporal correlations in time series is crucial for developing accurate models in natural and social sciences. The burst-tree decomposition method was recently introduced to reveal higher-order temporal correlations in time series in a form of an event sequence, in particular, the hierarchical structure of bursty trains of events for the entire range of timescales [Jo et al., Sci.~Rep.~\textbf{10}, 12202 (2020)]. Such structure has been found to be simply characterized by the burst-merging kernel governing which bursts are merged together as the timescale for defining bursts increases. In this work, we study the effects of kernels on the higher-order temporal correlations in terms of burst size distributions, memory coefficients for bursts, and the autocorrelation function. We employ several kernels, including the constant, sum, product, and diagonal kernels as well as those inspired by empirical results. We generically find that kernels with preferential merging lead to heavy-tailed burst size distributions, while kernels with assortative merging lead to positive correlations between burst sizes. The decaying exponent of the autocorrelation function depends not only on the kernel but also on the power-law exponent of the interevent time distribution. In addition, thanks to the analogy to the coagulation process, analytical solutions of burst size distributions for some kernels could be obtained. Our findings may shed light on the role of burst-merging kernels as underlying mechanisms of higher-order temporal correlations in time series.

physics.data-an↗

arXiv2024

Homophilic organization of egocentric communities in ICT services

Members of a society can be characterized by a large number of features, such as gender, age, ethnicity, religion, social status, and shared activities. One of the main tie-forming factors between individuals in human societies is homophily, the tendency of being attracted to similar others. Homophily has been mainly studied with focus on one of the features and little is known about the roles of similarities of different origins in the formation of communities. To close this gap, we analyze three datasets from Information and Communications Technology (ICT) services, namely, two online social networks and a network deduced from mobile phone calls, in all of which metadata about individual features are available. We identify communities within egocentric networks and surprisingly find that the larger the community is, the more overlap is found between features of its members and the ego. We interpret this finding in terms of the effort needed to manage the communities; the larger diversity requires more effort such that to maintain a large diverse group may exceed the capacity of the members. As the ego reaches out to her alters on an ICT service, we observe that the first alter in each community tends to have a higher feature overlap with the ego than the rest. Moreover the feature overlap of the ego with all her alters displays a non-monotonic behaviors as a function of the ego's degree. We propose a simple mechanism of how people add links in their egocentric networks of alters that reproduces all the empirical observations and shows the reason behind non-monotonic tendency of the egocentric feature overlap as a function of the ego's degree.

cs.SI↗

arXiv2024

Temporal scaling theory for bursty time series with clusters of arbitrarily many events

Long-term temporal correlations in time series in a form of an event sequence have been characterized using an autocorrelation function (ACF) that often shows a power-law decaying behavior. Such scaling behavior has been mainly accounted for by the heavy-tailed distribution of interevent times (IETs), i.e., the time interval between two consecutive events. Yet little is known about how correlations between consecutive IETs systematically affect the decaying behavior of the ACF. Empirical distributions of the burst size, which is the number of events in a cluster of events occurring in a short time window, often show heavy tails, implying that arbitrarily many consecutive IETs may be correlated with each other. In the present study, we propose a model for generating a time series with arbitrary functional forms of IET and burst size distributions. Then, we analytically derive the ACF for the model time series. In particular, by assuming that the IET and burst size are power-law distributed, we derive scaling relations between power-law exponents of the ACF decay, IET distribution, and burst size distribution. These analytical results are confirmed by numerical simulations. Our approach helps to rigorously and analytically understand the effects of correlations between arbitrarily many consecutive IETs on the decaying behavior of the ACF.

physics.data-an↗

arXiv2024

Exact solutions of the simplified March model for organizational learning

James G. March's celebrated agent-based simulation model for organizational learning [March, Organization Science \textbf{2}, 71 (1991)] has been extensively studied for the last decades. Yet the model was not fully understood due to the lack of analytical solutions of the model. We simplify the March model to take an analytical approach using master equations. We then derive exact solutions for some simplest yet nontrivial cases, and perform numerical estimation of master equations for more complicated cases. Both analytical and numerical results are in good agreement with agent-based simulations. These results are also compared to those of the original March model. Our approach enables us to rigorously understand the results of the simplified model as well as the original model to a large extent.

physics.soc-ph↗

arXiv2023

Multiple gravity laws for human mobility within cities

The gravity model of human mobility has successfully described the deterrence of travels with distance in urban mobility patterns. While a broad spectrum of deterrence was found across different cities, yet it is not empirically clear if movement patterns in a single city could also have a spectrum of distance exponents denoting a varying deterrence depending on the origin and destination regions in the city. By analyzing the travel data in the twelve most populated cities of the United States of America, we empirically find that the distance exponent governing the deterrence of travels significantly varies within a city depending on the traffic volumes of the origin and destination regions. Despite the diverse traffic landscape of the cities analyzed, a common pattern is observed for the distance exponents; the exponent value tends to be higher between regions with larger traffic volumes, while it tends to be lower between regions with smaller traffic volumes. This indicates that our method indeed reveals the hidden diversity of gravity laws that would be overlooked otherwise.

physics.soc-ph↗

arXiv2023

Statistical properties of COVID-19 transmission intervals in Republic of Korea

A transmission interval for an infectious disease is important to understand epidemic processes in complex networks. The transmission interval is defined as a time interval between one person's infection and their infection to another person. To study statistical properties of transmission intervals, we analyze a COVID-19 dataset of confirmed cases in Republic of Korea that has been collected for two years since the confirmation of the first case on 19 January 2020. Utilizing demographic information of confirmed individuals, such as sex, age, residence location, and the nature of relation between infectors and infectees, we find that transmission intervals are rarely affected by sexes, but they tend to have larger values for the youngest and oldest age groups than other groups. We also find some metropolitan cities or provinces with relatively larger (smaller) transmission intervals than other locations. These empirical findings might help us to better understand dynamical mechanisms of epidemic processes in complex social systems.

physics.soc-ph↗

arXiv2022

Copula-based analysis of the generalized friendship paradox in clustered networks

A heterogeneous structure of social networks induces various intriguing phenomena. One of them is the friendship paradox, which states that on average your friends have more friends than you do. Its generalization, called the generalized friendship paradox (GFP), states that on average your friends have higher attributes than yours. Despite successful demonstrations of the GFP by empirical analyses and numerical simulations, analytical, rigorous understanding of the GFP has been largely unexplored. Recently, an analytical solution for the probability that the GFP holds for an individual in a network with correlated attributes was obtained using the copula method but by assuming a locally tree structure of the underlying network [Jo~et~al., Physical Review E~\textbf{104}, 054301 (2021)]. Considering the abundant triangles in most social networks, we employ a vine copula method to incorporate the attribute correlation structure between neighbors of a focal individual in addition to the correlation between the focal individual and its neighbors. Our analytical approach helps us rigorously understand the GFP in more general networks such as clustered networks and other related interesting phenomena in social networks.

physics.soc-ph↗

arXiv2022

Copula-based analysis of the autocorrelation function for simple temporal networks

To characterize temporal correlations in temporal networks, we define an autocorrelation function (ACF) for temporal networks in terms of the similarity between two snapshot networks separated by a certain time interval. By employing a copula-based method recently developed for a single time series, we analyze the ACF for the temporal network in which activity patterns of links are independent of each other but their activity levels are heterogeneous. By assuming that exponential distributed interevent times are weakly correlated with each other in each link, we obtain an analytical solution of the ACF. The validity of the analytical solution is tested against the numerical simulations to find that the numerical results are comparable to the analytical solution.

physics.soc-ph↗