arXiv subjects

Jisung Yoon

Publications and source records attributed to Jisung Yoon.

17 recordsLinked to original sources

arXiv2026

The selective use of physics knowledge in policy: how interdisciplinary physics bridges subfields and shapes policy influence

Scientific knowledge has become central to policymaking as societies face challenges related to technological change, climate risk, and public health. Despite the growing emphasis on evidence-based policy, a systematic understanding of how science is selectively used in policy, specifically which forms of knowledge are preferred and which scientific citations translate into influence, remains limited. We address these questions by constructing a novel dataset that links policy documents from the Overton database with publications from the American Physical Society, enabling an analysis of how physics knowledge enters and circulates in policy discourse. Using subfield classifications, we provide quantitative evidence for a gap between scientific communities and policymakers. First, we find that policy documents draw on broad and interdisciplinary areas of physics, such as General Physics and Interdisciplinary Physics, rather than mirroring the structure of physics research production. Second, we identify substantial institutional heterogeneity with systematic differences in subfield preferences across policy producing organizations and topics. Third, network analysis reveals that interdisciplinary areas of physics act as a central bridge connecting specialized subfields. Finally, regression analysis reveals a clear separation between policy visibility and policy influence. While interdisciplinary areas facilitate entry into policy discourse, it does not necessarily increase downstream policy influence. Conversely, documents citing geophysics are associated with approximately 24 percent higher policy influence, likely driven by the political salience of climate change policy. Our findings underscore the distinction between scientific visibility and policy influence, contributing to a deeper understanding of the complex relationship between scientific communities and policy system.

physics.soc-ph↗

arXiv2025

Artificial Intelligence and Market Entrant Game Developers

Artificial Intelligence (AI) is increasingly being used for generating digital assets, such as programming codes and images. Games composed of various digital assets are thus expected to be influenced significantly by AI. Leveraging public data and AI disclosure statements of games, this paper shows that relatively more independent developers entered the market when generative AI became more publicly accessible, but their purposes of using AI are similar with non-independent developers. Game features associated with AI hint nuanced impacts of AI on independent developers.

cs.CY↗

arXiv2025

When Common Law Ages: Two Centuries of Growing Inertia in US Judicial Opinions

Judicial opinions once considered sound can lose relevance over time. Yet, little has been known, both systematically and at scale, about how judicial reasoning has evolved. Here, we analyze four million US court decisions from 1800 to 2000, quantifying each rulings' disruptiveness, i.e., the extent to which it breaks from established citation pathways. We find that such pathbreaks have declined over time, indicating that courts have become increasingly constrained by precedent. This growing inertia appears to be driven by two structural factors. The first is precedent overload, evidenced by the volume of case law outpacing population growth (scaling exponent of 1.7). The second is the rise of ideological polarization within the judiciary, which introduces institutional uncertainty that prompts greater deference to established precedent. Despite this overall tendency toward path dependence, we find that a relatively small number of high-authority courts continue to shape legal discourse through top-down interventions. Our findings recast legal reasoning as an evolutionary process shaped by structural growth, institutional memory, and hierarchical structure, incorporating broader theories of innovation and organizational adaptation into the study of law.

physics.soc-ph↗

arXiv2025

A Century of Evolution in the Complexity of the United States Legal Code

As societies confront increasingly complex regulatory demands in domains such as digital governance, climate policy, and public health, there is a pressing need to understand how legal systems evolve, where they concentrate regulatory attention, and how their institutional architectures shape capacity for adaptation. Yet, the long-term structural dynamics of law remain empirically underexplored. Here, we provide a versioned, machine-readable record of the United States Code (U.S. Code), the primary compilation of federal statutory law in the United States, covering the entire history of the Code from 1926 to 2023. We include not only the curated text in Code but also its structural and linguistic complexity: word counts, vocabulary statistics, hierarchical organization (titles, chapters, sections, subsections), and cross-references among titles. In this way, the dataset offers an empirical foundation for large-scale and long-term interdisciplinary analysis of the growth, reorganization, and internal logic of statutory systems. The dataset is released on GitHub with comprehensive documentation to support reuse across legal studies, data science, complexity research, and institutional analysis.

physics.soc-ph↗

arXiv2024

Suspense and surprise in the book of technology: Understanding innovation dynamics

We envision future technologies through science fiction, strategic planning, or academic research. Yet, our expectations do not always match with what actually unfolds, much like navigating a story where some events align with expectations while others surprise us. This gap indicates the inherent uncertainty of innovation-how technologies emerge and evolve in unpredictable ways. Here, we elaborate on this inherent uncertainty of innovation in the way technologies emerge and evolve. We define suspense captures accumulated uncertainty and describing events anticipated before their realization, while surprise represents a dramatic shift in understanding when an event occurs unexpectedly. We identify those connections in U.S. patents and show that suspenseful innovations tend to integrate more smoothly into society, achieving higher citations and market value. In contrast, surprising innovations, though often disruptive and groundbreaking, face challenges in adoption due to their extreme novelty. We further show that these categories allow us to identify distinct stages of technology life cycles, suggesting a way to identify the systematic trajectory of technologies and anticipate their future paths.

physics.soc-ph↗

arXiv2024

What makes Individual I's a Collective We; Coordination mechanisms & costs

The collective effort exceeds the sum of its parts when individuals coordinate and regulate their activities and behaviors. This holds true even in self-organizing systems with open, voluntary participation where coordination occurs implicitly. Here, we analyze the non-functional actions of contributors, administrators, and bots on Wikipedia, categorizing them by their asymmetric authority: one-way oversight and two-way. This categorization helps us reveal comparable patterns. First, we find remarkably consistent scaling factors for each category relative to system size. Two-way coordination scales superlinearly (with an exponent of $1.3$), while oversight coordination grows sublinearly (with an exponent of $0.9$), suggesting an underlying mechanism for coordination across communities. Second, we identify the hierarchical modular structure of interactions as a key factor for the economy of scale in coordination, and we propose a mathematical model to explain these results. Finally, our temporal analysis shows a shift from two-way interactions to one-way oversight as system size increases. This suggests the emergence of a nascent hierarchical structure even in self-organizing systems, echoing Weber's theory of organizational evolution.

physics.soc-ph↗

arXiv2024

Bottom-up systems at scale: The case of Reddit

How do human collectives navigate increasing regulatory challenges to maintain order and avoid dysfunction as they grow in size? Here, we quantify how measurable actions, from user-to-user interactions to top-down enforcement, scale with size in Reddit subcommunities, spanning five orders of magnitude from 10^2 to 10^7 users. We find that regulatory actions scale systematically with community size across many different topics, with consistent scaling rates and empirically grounded governance modes that distinguish how communities regulate themselves. Observed scaling exponents align with well-known laws in urban systems: superlinear growth for peer interaction and enforcement, with beta values of approximately 1.12 to 1.18, and near-linear scaling for automated bot oversight, with beta approximately 0.95 and 95% confidence intervals spanning 1.0. These regularities invite cross-system comparison as a path toward identifying whether common generative processes underlie them. We identify three empirically grounded modes of regulatory functions: Intensity, explaining 54% of the variance; One-way versus Two-way Communication, explaining 25%; and Impersonal versus Personal Moderation, explaining 21%. Our temporal analysis shows that increasing regulatory intensity is most likely absorbed by one-way coordination. These observations align with classic governance frameworks, including Ostrom's self-governance in commons and organizational theories of bureaucratic versus discretionary control, and quantify previous qualitative observations of online systems. Our findings provide an empirical starting point for understanding how different regulatory mechanisms interact in bottom-up systems as they scale.

physics.soc-ph↗

arXiv2024

Implicit degree bias in the link prediction task

Link prediction -- a task of distinguishing actual hidden edges from random unconnected node pairs -- is one of the quintessential tasks in graph machine learning. Despite being widely accepted as a universal benchmark and a downstream task for representation learning, the validity of the link prediction benchmark itself has been rarely questioned. Here, we show that the common edge sampling procedure in the link prediction task has an implicit bias toward high-degree nodes and produces a highly skewed evaluation that favors methods overly dependent on node degree, to the extent that a ``null'' link prediction method based solely on node degree can yield nearly optimal performance. We propose a degree-corrected link prediction task that offers a more reasonable assessment that aligns better with the performance in the recommendation task. Finally, we demonstrate that the degree-corrected benchmark can more effectively train graph machine-learning models by reducing overfitting to node degrees and facilitating the learning of relevant structures in graphs.

cs.SI↗

arXiv2023

Unsupervised embedding of trajectories captures the latent structure of scientific migration

Human migration and mobility drives major societal phenomena including epidemics, economies, innovation, and the diffusion of ideas. Although human mobility and migration have been heavily constrained by geographic distance throughout the history, advances and globalization are making other factors such as language and culture increasingly more important. Advances in neural embedding models, originally designed for natural language, provide an opportunity to tame this complexity and open new avenues for the study of migration. Here, we demonstrate the ability of the model word2vec to encode nuanced relationships between discrete locations from migration trajectories, producing an accurate, dense, continuous, and meaningful vector-space representation. The resulting representation provides a functional distance between locations, as well as a digital double that can be distributed, re-used, and itself interrogated to understand the many dimensions of migration. We show that the unique power of word2vec to encode migration patterns stems from its mathematical equivalence with the gravity model of mobility. Focusing on the case of scientific migration, we apply word2vec to a database of three million migration trajectories of scientists derived from the affiliations listed on their publication records. Using techniques that leverage its semantic structure, we demonstrate that embeddings can learn the rich structure that underpins scientific migration, such as cultural, linguistic, and prestige relationships at multiple levels of granularity. Our results provide a theoretical foundation and methodological framework for using neural embeddings to represent and understand migration both within and beyond science.

cs.LG↗

arXiv2022

COVID-19 confines recreational gatherings in Seoul to familiar, less crowded, and neighboring urban areas

Recreational gatherings are sources of the spread of infectious diseases. Understanding the dynamics of recreational gatherings is essential to building effective public health policies but challenging as the interaction between people and recreational places is complex. Recreational activities are concentrated in a set of urban areas and establish a recreational hierarchy. In this hierarchy, higher-level regions attract more people than lower-level regions for recreational purposes. Here, using customers' motel booking records which are highly associated with recreational activities in Korea, we identify that recreational hierarchy, geographical distance, and attachment to a location are crucial factors of recreational gatherings in Seoul, Republic of Korea. Our analyses show that after the COVID-19 outbreak, people are more likely to visit familiar recreational places, avoid the highest level of the recreational hierarchy, and travel close distances. Interestingly, the recreational visitations were reduced not only in the highest but also in low-level regions. Urban areas at low levels of the recreational hierarchy were more severely affected by COVID-19 than urban areas at high and middle levels of the recreational hierarchy.

physics.soc-ph↗

arXiv2022

Revealing role of Korean Physics Society with keyword co-occurrence network

Science and society inevitably interact with each other and evolve together. Studying the trend of science helps recognize leading topics significant for research and establish better policies to allocate funds efficiently. Scholarly societies such as the Korean Physics Society (KPS) also play an important role in the history of science. Figuring out the role of these scholarly societies motivate our research related with our society since societies pay attention to improve our society. Although several studies try to capture the trend of science leveraging scientific documents such as paper or patents, but these studies limited their research scope only to the academic world, neglecting the interaction with society. Here we try to understand the trend of science along with society using a public magazine named "Physics and High Technology," published by the Korean Physics Society (KPS). We build keyword co-occurrence networks for each time period and applied community detection to capture the keyword structure and tracked the structure's evolution. In the networks, a research-related cluster is consistently dominant over time, and sub-clusters of the research-related cluster divide into various fields of physics, implying specialization of the physics discipline. Also, we found that education and policy clusters appear consistently, revealing the KPS's contribution to science and society. Furthermore, we applied PageRank algorithm to selected keywords ('semiconductor', 'woman', 'evading'...) to investigate the temporal change of the importance of keywords in the network. For example, the importance of the keyword 'woman' increases as time goes by, indicating that academia also pays attention to gender issues reflecting the social movement in recent years.

physics.soc-ph↗

arXiv2022

Quantifying the topic disparity of scientific articles

Citation count is a popular index for assessing scientific papers. However, it depends on not only the quality of a paper but also various factors, such as conventionality, team size, and gender. Here, we examine the extent to which the conventionality of a paper is related to its citation percentile in a discipline by using our measure, topic disparity. The topic disparity is the cosine distance between a paper and its discipline on a neural embedding space. Using this measure, we show that the topic disparity is negatively associated with the citation percentile in many disciplines, even after controlling team size and the genders of the first and last authors. This result indicates that less conventional research tends to receive fewer citations than conventional research. Our proposed method can be used to complement the raw citation counts and to recommend papers at the periphery of a discipline because of their less conventional topics.

cs.DL↗

arXiv2022

Quantifying knowledge synchronisation in the 21st century

Humans acquire and accumulate knowledge through language usage and eagerly exchange their knowledge for advancement. Although geographical barriers had previously limited communication, the emergence of information technology has opened new avenues for knowledge exchange. However, it is unclear which communication pathway is dominant in the 21st century. Here, we explore the dominant path of knowledge diffusion in the 21st century using Wikipedia, the largest communal dataset. We evaluate the similarity of shared knowledge between population groups, distinguished based on their language usage. When population groups are more engaged with each other, their knowledge structure is more similar, where engagement is indicated by socioeconomic connections, such as cultural, linguistic, and historical features. Moreover, geographical proximity is no longer a critical requirement for knowledge dissemination. Furthermore, we integrate our data into a mechanistic model to better understand the underlying mechanism and suggest that the knowledge "Silk Road" of the 21st century is based online.

physics.soc-ph↗

arXiv2021

Residual2Vec: Debiasing graph embedding with random graphs

Graph embedding maps a graph into a convenient vector-space representation for graph analysis and machine learning applications. Many graph embedding methods hinge on a sampling of context nodes based on random walks. However, random walks can be a biased sampler due to the structural properties of graphs. Most notably, random walks are biased by the degree of each node, where a node is sampled proportionally to its degree. The implication of such biases has not been clear, particularly in the context of graph representation learning. Here, we investigate the impact of the random walks' bias on graph embedding and propose residual2vec, a general graph embedding method that can debias various structural biases in graphs by using random graphs. We demonstrate that this debiasing not only improves link prediction and clustering performance but also allows us to explicitly model salient structural properties in graph embedding.

cs.LG↗

arXiv2020

Tracing the evolution of physics with a keyword co-occurrence network

Describing the evolution of science is a salient work not only for revealing the scientific trend but also for establishing a scientific classification system. In this paper, we investigate the evolution of science by observing the structure and change of keyword co-occurrence networks. Starting from seven target physics fields and their initial keywords selected by experts from the Korean Physical Society, we generate keyword co-occurrence networks better to capture topological structure with our proposed approach. In this way, we can construct a more relevant and abundant keyword network from a small set of initial keywords. With these networks, we successfully identify the scientific sub-field by detecting communities and extracting core keywords of each community. Furthermore, we trace the temporal evolution of sub-fields with the time-snapshot keyword network, the resultant temporal change of the community membership explains the evolution of the research field well. Our approach for tracing the evolution of the research field with a keyword co-occurrence network can shed light on identifying and assessing the evolution of science.

physics.soc-ph↗

arXiv2020

Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs

Graph embedding techniques, which learn low-dimensional representations of a graph, are achieving state-of-the-art performance in many graph mining tasks. Most existing embedding algorithms assign a single vector to each node, implicitly assuming that a single representation is enough to capture all characteristics of the node. However, across many domains, it is common to observe pervasively overlapping community structure, where most nodes belong to multiple communities, playing different roles depending on the contexts. Here, we propose persona2vec, a graph embedding framework that efficiently learns multiple representations of nodes based on their structural contexts. Using link prediction-based evaluation, we show that our framework is significantly faster than the existing state-of-the-art model while achieving better performance.

cs.SI↗

arXiv2018

Build up of a subject classification system from collective intelligence

Systematized subject classification is essential for funding and assessing scientific projects. Conventionally, classification schemes are founded on the empirical knowledge of the group of experts; thus, the experts' perspectives have influenced the current systems of scientific classification. Those systems archived the current state-of-art in practice, yet the global effect of the accelerating scientific change over time has made the updating of the classifications system on a timely basis vertually impossible. To overcome the aforementioned limitations, we propose an unbiased classification scheme that takes advantage of collective knowledge; Wikipedia, an Internet encyclopedia edited by millions of users, sets a prompt classification in a collective fashion. We construct a Wikipedia network for scientific disciplines and extract the backbone of the network. This structure displays a landscape of science and technology that is based on a collective intelligence and that is more unbiased and adaptable than conventional classifications.

physics.soc-ph↗