arXiv subjects

Yong-Yeol Ahn

Publications and source records attributed to Yong-Yeol Ahn.

At least 55 records · Page 3Linked to original sources

arXiv2021

Principled approach to the selection of the embedding dimension of networks

Network embedding is a general-purpose machine learning technique that encodes network structure in vector spaces with tunable dimension. Choosing an appropriate embedding dimension -- small enough to be efficient and large enough to be effective -- is challenging but necessary to generate embeddings applicable to a multitude of tasks. Existing strategies for the selection of the embedding dimension rely on performance maximization in downstream tasks. Here, we propose a principled method such that all structural information of a network is parsimoniously encoded. The method is validated on various embedding algorithms and a large corpus of real-world networks. The embedding dimension selected by our method in real-world networks suggest that efficient encoding in low-dimensional spaces is usually possible.

physics.soc-ph↗

arXiv2021

Misinformation, Believability, and Vaccine Acceptance Over 40 Countries: Takeaways From the Initial Phase of The COVID-19 Infodemic

The COVID-19 pandemic has been damaging to the lives of people all around the world. Accompanied by the pandemic is an infodemic, an abundant and uncontrolled spreading of potentially harmful misinformation. The infodemic may severely change the pandemic's course by interfering with public health interventions such as wearing masks, social distancing, and vaccination. In particular, the impact of the infodemic on vaccination is critical because it holds the key to reverting to pre-pandemic normalcy. This paper presents findings from a global survey on the extent of worldwide exposure to the COVID-19 infodemic, assesses different populations' susceptibility to false claims, and analyzes its association with vaccine acceptance. Based on responses gathered from over 18,400 individuals from 40 countries, we find a strong association between perceived believability of misinformation and vaccination hesitancy. Additionally, our study shows that only half of the online users exposed to rumors might have seen the fact-checked information. Moreover, depending on the country, between 6% and 37% of individuals considered these rumors believable. Our survey also shows that poorer regions are more susceptible to encountering and believing COVID-19 misinformation. We discuss implications of our findings on public campaigns that proactively spread accurate information to countries that are more susceptible to the infodemic. We also highlight fact-checking platforms' role in better identifying and prioritizing claims that are perceived to be believable and have wide exposure. Our findings give insights into better handling of risk communication during the initial phase of a future pandemic.

cs.SI↗

arXiv2021

Neural Embeddings of Scholarly Periodicals Reveal Complex Disciplinary Organizations

Understanding the structure of knowledge domains is one of the foundational challenges in science of science. Here, we propose a neural embedding technique that leverages the information contained in the citation network to obtain continuous vector representations of scientific periodicals. We demonstrate that our periodical embeddings encode nuanced relationships between periodicals as well as the complex disciplinary and interdisciplinary structure of science, allowing us to make cross-disciplinary analogies between periodicals. Furthermore, we show that the embeddings capture meaningful "axes" that encompass knowledge domains, such as an axis from "soft" to "hard" sciences or from "social" to "biological" sciences, which allow us to quantitatively ground periodicals on a given dimension. By offering novel quantification in science of science, our framework may in turn facilitate the study of how knowledge is created and organized.

cs.DL↗

arXiv2021

People, Places, and Ties: Landscape of social places and their social network structures

Due to their essential role as places for socialization, "third places" - social places where people casually visit and communicate with friends and neighbors - have been studied by a wide range of fields including network science, sociology, geography, urban planning, and regional studies. However, the lack of a large-scale census on third places kept researchers from systematic investigations. Here we provide a systematic nationwide investigation of third places and their social networks, by using Facebook pages. Our analysis reveals a large degree of geographic heterogeneity in the distribution of the types of third places, which is highly correlated with baseline demographics and county characteristics. Certain types of pages like "Places of Worship" demonstrate a large degree of clustering suggesting community preference or potential complementarities to concentration. We also found that the social networks of different types of social place differ in important ways: The social networks of 'Restaurants' and 'Indoor Recreation' pages are more likely to be tight-knit communities of pre-existing friendships whereas 'Places of Worship' and 'Community Amenities' page categories are more likely to bridge new friendship ties. We believe that this study can serve as an important milestone for future studies on the systematic comparative study of social spaces and their social relationships.

cs.SI↗

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↗

arXiv2020

The effectiveness of backward contact tracing in networks

Discovering and isolating infected individuals is a cornerstone of epidemic control. Because many infectious diseases spread through close contacts, contact tracing is a key tool for case discovery and control. However, although contact tracing has been performed widely, the mathematical understanding of contact tracing has not been fully established and it has not been clearly understood what determines the efficacy of contact tracing. Here, we reveal that, compared with "forward" tracing---tracing to whom disease spreads, "backward" tracing---tracing from whom disease spreads---is profoundly more effective. The effectiveness of backward tracing is due to simple but overlooked biases arising from the heterogeneity in contacts. Using simulations on both synthetic and high-resolution empirical contact datasets, we show that even at a small probability of detecting infected individuals, strategically executed contact tracing can prevent a significant fraction of further transmissions. We also show that---in terms of the number of prevented transmissions per isolation---case isolation combined with a small amount of contact tracing is more efficient than case isolation alone. By demonstrating that backward contact tracing is highly effective at discovering super-spreading events, we argue that the potential effectiveness of contact tracing has been underestimated. Therefore, there is a critical need for revisiting current contact tracing strategies so that they leverage all forms of biases. Our results also have important consequences for digital contact tracing because it will be crucial to incorporate the capability for backward and deep tracing while adhering to the privacy-preserving requirements of these new platforms.

q-bio.PE↗

arXiv2020

A Systematic Media Frame Analysis of 1.5 Million New York Times Articles from 2000 to 2017

Framing is an indispensable narrative device for news media because even the same facts may lead to conflicting understandings if deliberate framing is employed. Therefore, identifying media framing is a crucial step to understanding how news media influence the public. Framing is, however, difficult to operationalize and detect, and thus traditional media framing studies had to rely on manual annotation, which is challenging to scale up to massive news datasets. Here, by developing a media frame classifier that achieves state-of-the-art performance, we systematically analyze the media frames of 1.5 million New York Times articles published from 2000 to 2017. By examining the ebb and flow of media frames over almost two decades, we show that short-term frame abundance fluctuation closely corresponds to major events, while there also exist several long-term trends, such as the gradually increasing prevalence of the ``Cultural identity'' frame. By examining specific topics and sentiments, we identify characteristics and dynamics of each frame. Finally, as a case study, we delve into the framing of mass shootings, revealing three major framing patterns. Our scalable, computational approach to massive news datasets opens up new pathways for systematic media framing studies.

cs.CY↗

arXiv2020

Information Seeking Responses to News of Local COVID-19 Cases: Evidence from Internet Search Data

The novel coronavirus (COVID-19) outbreak is a global pandemic with community circulation in many countries, including the U.S. where every state is reporting confirmed cases. The course of this pandemic will be largely shaped by how governments enact timely policies, disseminate the information, and most importantly, how the public reacts to them. Here, we examine informationseeking responses to the first COVID-19 case public announcement in a state. By using an eventstudy framework, we show that such news increases collective attention to the crisis right away, but the elevated level of attention is short-lived, even though the initial announcements were followed by increasingly strong measures. We find that people respond to the first report of COVID-19 in their state by immediately seeking information about COVID-19, as measured by searches for coronavirus, coronavirus symptoms and hand sanitizer. On the other hand, searches for information regarding community level policies (e.g., quarantine, school closures, testing), or personal health strategies (e.g., masks, grocery delivery, over-the-counter medications) do not appear to be immediately triggered by first reports. These results are encouraging given our study period is relatively early in the epidemic and more elaborate policy responses were not yet part of the public discourse. Further analysis will track evolving patterns of responses to subsequent flows of public information.

cs.SI↗

arXiv2020

Extracting hierarchical backbones from bipartite networks

We propose a method for extracting hierarchical backbones from a bipartite network. Our method leverages the observation that a hierarchical relationship between two nodes in a bipartite network is often manifested as an asymmetry in the conditional probability of observing the connections to them from the other node set. Our method estimates both the importance and direction of the hierarchical relationship between a pair of nodes, thereby providing a flexible way to identify the essential part of the networks. Using semi-synthetic benchmarks, we show that our method outperforms existing methods at identifying planted hierarchy while offering more flexibility. Application of our method to empirical datasets---a bipartite network of skills and individuals as well as the network between gene products and Gene Ontology (GO) terms---demonstrates the possibility of automatically extracting or augmenting ontology from data.

cs.SI↗

arXiv2019

Co-contributorship Network and Division of Labor in Individual Scientific Collaborations

Collaborations are pervasive in current science. Collaborations have been studied and encouraged in many disciplines. However, little is known how a team really functions from the detailed division of labor within. In this research, we investigate the patterns of scientific collaboration and division of labor within individual scholarly articles by analyzing their co-contributorship networks. Co-contributorship networks are constructed by performing the one-mode projection of the author-task bipartite networks obtained from 138,787 papers published in PLoS journals. Given a paper, we define three types of contributors: Specialists, Team-players, and Versatiles. Specialists are those who contribute to all their tasks alone; team-players are those who contribute to every task with other collaborators; and versatiles are those who do both. We find that team-players are the majority and they tend to contribute to the five most common tasks as expected, such as "data analysis" and "performing experiments". The specialists and versatiles are more prevalent than expected by a random-graph null model. Versatiles tend to be senior authors associated with funding and supervisions. Specialists are associated with two contrasting roles: the supervising role as team leaders or marginal and specialized contributions.

cs.SI↗

arXiv2019

Massive Multi-Agent Data-Driven Simulations of the GitHub Ecosystem

Simulating and predicting planetary-scale techno-social systems poses heavy computational and modeling challenges. The DARPA SocialSim program set the challenge to model the evolution of GitHub, a large collaborative software-development ecosystem, using massive multi-agent simulations. We describe our best performing models and our agent-based simulation framework, which we are currently extending to allow simulating other planetary-scale techno-social systems. The challenge problem measured participant's ability, given 30 months of meta-data on user activity on GitHub, to predict the next months' activity as measured by a broad range of metrics applied to ground truth, using agent-based simulation. The challenge required scaling to a simulation of roughly 3 million agents producing a combined 30 million actions, acting on 6 million repositories with commodity hardware. It was also important to use the data optimally to predict the agent's next moves. We describe the agent framework and the data analysis employed by one of the winning teams in the challenge. Six different agent models were tested based on a variety of machine learning and statistical methods. While no single method proved the most accurate on every metric, the broadly most successful sampled from a stationary probability distribution of actions and repositories for each agent. Two reasons for the success of these agents were their use of a distinct characterization of each agent, and that GitHub users change their behavior relatively slowly.

cs.MA↗

arXiv2019

Element-centric clustering comparison unifies overlaps and hierarchy

Clustering is one of the most universal approaches for understanding complex data. A pivotal aspect of clustering analysis is quantitatively comparing clusterings; clustering comparison is the basis for many tasks such as clustering evaluation, consensus clustering, and tracking the temporal evolution of clusters. In particular, the extrinsic evaluation of clustering methods requires comparing the uncovered clusterings to planted clusterings or known metadata. Yet, as we demonstrate, existing clustering comparison measures have critical biases which undermine their usefulness, and no measure accommodates both overlapping and hierarchical clusterings. Here we unify the comparison of disjoint, overlapping, and hierarchically structured clusterings by proposing a new element-centric framework: elements are compared based on the relationships induced by the cluster structure, as opposed to the traditional cluster-centric philosophy. We demonstrate that, in contrast to standard clustering similarity measures, our framework does not suffer from critical biases and naturally provides unique insights into how the clusterings differ. We illustrate the strengths of our framework by revealing new insights into the organization of clusters in two applications: the improved classification of schizophrenia based on the overlapping and hierarchical community structure of fMRI brain networks, and the disentanglement of various social homophily factors in Facebook social networks. The universality of clustering suggests far-reaching impact of our framework throughout all areas of science.

stat.ML↗

arXiv2019

Optimal modularity and memory capacity of neural reservoirs

The neural network is a powerful computing framework that has been exploited by biological evolution and by humans for solving diverse problems. Although the computational capabilities of neural networks are determined by their structure, the current understanding of the relationships between a neural network's architecture and function is still primitive. Here we reveal that neural network's modular architecture plays a vital role in determining the neural dynamics and memory performance of the network of threshold neurons. In particular, we demonstrate that there exists an optimal modularity for memory performance, where a balance between local cohesion and global connectivity is established, allowing optimally modular networks to remember longer. Our results suggest that insights from dynamical analysis of neural networks and information spreading processes can be leveraged to better design neural networks and may shed light on the brain's modular organization.

cs.NE↗

arXiv2019

Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters in the world economy

Groups of firms often achieve a competitive advantage through the formation of geo-industrial clusters. Although many exemplary clusters, such as Hollywood or Silicon Valley, have been frequently studied, systematic approaches to identify and analyze the hierarchical structure of the geo-industrial clusters at the global scale are rare. In this work, we use LinkedIn's employment histories of more than 500 million users over 25 years to construct a labor flow network of over 4 million firms across the world and apply a recursive network community detection algorithm to reveal the hierarchical structure of geo-industrial clusters. We show that the resulting geo-industrial clusters exhibit a stronger association between the influx of educated-workers and financial performance, compared to existing aggregation units. Furthermore, our additional analysis of the skill sets of educated-workers supplements the relationship between the labor flow of educated-workers and productivity growth. We argue that geo-industrial clusters defined by labor flow provide better insights into the growth and the decline of the economy than other common economic units.

cs.SI↗

arXiv2018

SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment

Because word semantics can substantially change across communities and contexts, capturing domain-specific word semantics is an important challenge. Here, we propose SEMAXIS, a simple yet powerful framework to characterize word semantics using many semantic axes in word- vector spaces beyond sentiment. We demonstrate that SEMAXIS can capture nuanced semantic representations in multiple online communities. We also show that, when the sentiment axis is examined, SEMAXIS outperforms the state-of-the-art approaches in building domain-specific sentiment lexicons.

cs.CL↗

arXiv2018

Optimal modularity in complex contagion

In this chapter, we apply the theoretical framework introduced in the previous chapter to study how the modular structure of the social network affects the spreading of complex contagion. In particular, we focus on the notion of optimal modularity, that predicts the occurrence of global cascades when the network exhibits just the right amount of modularity. Here we generalize the findings by assuming the presence of multiple communities and an uniform distribution of seeds across the network. Finally, we offer some insights into the temporal evolution of cascades in the regime of the optimal modularity.

physics.soc-ph↗

arXiv2018

Spreading in Social Systems: Reflections

In this final chapter, we consider the state-of-the-art for spreading in social systems and discuss the future of the field. As part of this reflection, we identify a set of key challenges ahead. The challenges include the following questions: how can we improve the quality, quantity, extent, and accessibility of datasets? How can we extract more information from limited datasets? How can we take individual cognition and decision making processes into account? How can we incorporate other complexity of the real contagion processes? Finally, how can we translate research into positive real-world impact? In the following, we provide more context for each of these open questions.

physics.soc-ph↗

arXiv2017

The Impact of Random Models on Clustering Similarity

Clustering is a central approach for unsupervised learning. After clustering is applied, the most fundamental analysis is to quantitatively compare clusterings. Such comparisons are crucial for the evaluation of clustering methods as well as other tasks such as consensus clustering. It is often argued that, in order to establish a baseline, clustering similarity should be assessed in the context of a random ensemble of clusterings. The prevailing assumption for the random clustering ensemble is the permutation model in which the number and sizes of clusters are fixed. However, this assumption does not necessarily hold in practice; for example, multiple runs of K-means clustering returns clusterings with a fixed number of clusters, while the cluster size distribution varies greatly. Here, we derive corrected variants of two clustering similarity measures (the Rand index and Mutual Information) in the context of two random clustering ensembles in which the number and sizes of clusters vary. In addition, we study the impact of one-sided comparisons in the scenario with a reference clustering. The consequences of different random models are illustrated using synthetic examples, handwriting recognition, and gene expression data. We demonstrate that the choice of random model can have a drastic impact on the ranking of similar clustering pairs, and the evaluation of a clustering method with respect to a random baseline; thus, the choice of random clustering model should be carefully justified.

stat.ML↗