arXiv subjects

Masaki Aida

Publications and source records attributed to Masaki Aida.

At least 19 recordsLinked to original sources

arXiv2026

Declining Modularity of Intellectual Bases During the Emergence of Research Areas

Understanding how research areas emerge can help identify nascent areas early and inform research strategy, yet how the intellectual base of a field restructures as an area takes shape remains unclear. We hypothesize that the emergence of a research area is accompanied by the integration of largely separate knowledge communities, observable as a decline in the modularity of its co-citation network, which represents its intellectual base. We propose a framework that tracks this modularity over time, evaluates the statistical robustness of its changes, and identifies the papers highly associated with the decline. We applied it to three areas with different modes of growth: higher-order network science, superstring theory, and graph representation learning. In all three, modularity declined in correspondence with each area's emergence or transformation, and in superstring theory, the decline aligns with an independently documented transition. Further analysis of higher-order network science shows that its decline reflects a cross-disciplinary integration. In graph representation learning, the gradual decline is followed by a rise, which we interpret as a re-differentiation after the emergence period. Our results suggest that a decline in the modularity of a co-citation network can serve as a structural signature that retrospectively characterizes this integrative mode of emergence.

physics.soc-ph↗

arXiv2025

Learning Multi-Order Block Structure in Higher-Order Networks

Higher-order networks, naturally described as hypergraphs, are essential for modeling real-world systems involving interactions among three or more entities. Stochastic block models offer a principled framework for characterizing mesoscale organization, yet their extension to hypergraphs involves a trade-off between expressive power and computational complexity. A recent simplification, a single-order model, mitigates this complexity by assuming a single affinity pattern governs interactions of all orders. This universal assumption, however, may overlook order-dependent structural details. Here, we propose a framework that relaxes this assumption by introducing a multi-order block structure, in which different affinity patterns govern distinct subsets of interaction orders. Our framework is based on a multi-order stochastic block model and searches for the optimal partition of the set of interaction orders that maximizes out-of-sample hyperlink prediction performance. Analyzing a diverse range of real-world networks, we find that multi-order block structures are prevalent. Accounting for them not only yields better predictive performance over the single-order model but also uncovers sharper, more interpretable mesoscale organization. Our findings reveal that order-dependent mechanisms are a key feature of the mesoscale organization of real-world higher-order networks.

cs.SI↗

arXiv2025

Network Prebunking Problem: Optimizing Prebunking Targets to Suppress the Spread of Misinformation in Social Networks

As a countermeasure against misinformation that undermines the healthy use of social media, a preventive intervention known as \textit{prebunking} has recently attracted attention in the field of psychology. Prebunking aims to strengthen individuals' cognitive resistance to misinformation by presenting weakened doses of misinformation or by teaching common manipulation techniques before they encounter actual misinformation. Despite the growing body of evidence supporting its effectiveness in reducing susceptibility to misinformation at the individual level, an important open question remains: how best to identify the optimal targets for prebunking interventions to mitigate the spread of misinformation in a social network. To address this issue, we formulate a combinatorial optimization problem, called the \textit{network prebunking problem}, which aims to select optimal prebunking targets that minimizes the spread of misinformation in a social network under limited intervention budgets. We show that the problem is NP-hard and that its objective function is monotone and submodular, which provides a theoretical foundation for approximation guarantees of greedy algorithms. However, since the greedy algorithm is computationally expensive and does not scale to large networks, we propose an efficient approximation algorithm, MIA-NPP, based on the Maximum Influence Arborescence (MIA) approach, which restricts influence propagation around each node to a local directed tree rooted at that node. Through numerical experiments using real-world social network datasets, we demonstrate that MIA-NPP effectively suppresses the spread of misinformation under both fully observed and uncertain model parameter settings.

cs.SI↗

arXiv2025

Sampling nodes and hyperedges via random walks on large hypergraphs

Hypergraphs provide a fundamental framework for representing complex systems involving interactions among three or more entities. As empirical hypergraphs grow in size, characterizing their structural properties becomes increasingly challenging due to computational complexity and, in some cases, restricted access to complete data, requiring efficient sampling methods. Random walks offer a practical approach to hypergraph sampling, as they rely solely on local neighborhood information from nodes and hyperedges. In this study, we investigate methods for simultaneously sampling nodes and hyperedges via random walks on large hypergraphs. First, we compare three existing random walks in the context of hypergraph sampling and identify an advantage of the so-called higher-order random walk. Second, by extending an established technique for graphs to the case of hypergraphs, we present a non-backtracking variant of the higher-order random walk. We derive theoretical results on estimators based on the non-backtracking higher-order random walk and validate them through numerical simulations on large empirical hypergraphs. Third, we apply the non-backtracking higher-order random walk to a large hypergraph of co-authorships indexed in the OpenAlex database, where full access to the data is not readily available. Despite the relatively small sample size, our estimates largely align with previous findings on author productivity, team size, and the prevalence of open-access publications. Our findings contribute to the development of analysis methods for large hypergraphs, offering insights into sampling strategies and estimation techniques applicable to real-world complex systems.

cs.SI↗

arXiv2022

Interpreting Graph-based Sybil Detection Methods as Low-Pass Filtering

Online social networks (OSNs) are threatened by Sybil attacks, which create fake accounts (also called Sybils) on OSNs and use them for various malicious activities. Therefore, Sybil detection is a fundamental task for OSN security. Most existing Sybil detection methods are based on the graph structure of OSNs, and various methods have been proposed recently. However, although almost all methods have been compared experimentally in terms of detection performance and noise robustness, theoretical understanding of them is still lacking. In this study, we show that existing graph-based Sybil detection methods can be interpreted in a unified framework of low-pass filtering. This framework enables us to theoretically compare and analyze each method from two perspectives: filter kernel properties and the spectrum of shift matrices. Our analysis reveals that the detection performance of each method depends on how well low-pass filtering can extract low frequency components and remove noisy high frequency components. Furthermore, on the basis of the analysis, we propose a novel Sybil detection method called SybilHeat. Numerical experiments on synthetic graphs and real social networks demonstrate that SybilHeat performs consistently well on graphs with various structural properties. This study lays a theoretical foundation for graph-based Sybil detection and leads to a better understanding of Sybil detection methods.

cs.CR↗

arXiv2021

Perturbative expansion of the fundamental equation of online user dynamics for describing changes in eigenfrequencies

The oscillation model has been proposed as a theoretical framework for describing user dynamics in online social networks. This model can model the user dynamics generated by a particular network structure and allow its causal relationships to be explicitly described. In this paper, by applying perturbation theory to the fundamental equation of the oscillation model, we confirm that we can explicitly trace, at least in principle, the changes in user dynamics associated with changes in the network structure. Specifically, we formulate perturbative expansions up to infinite order, by drawing on inferences from regularities found in perturbative expansions; the accuracy of perturbative expansions of finite order is evaluated by numerical experiments.

cs.SI↗

arXiv2021

Derivation and Characteristics of Closed-Form Solutions of the Fundamental Equations for Online User Dynamics

The oscillation model, based on the wave equation on networks, can describe user dynamics in online social networks. The fundamental equation of user dynamics can be introduced into the oscillation model to explicitly describe the causal relation of user dynamics yielded by certain specific network structures. Moreover, by considering the sparseness of the link structure of online social networks, a novel fundamental equation of different forms has been devised. In this paper, we derive a closed-form solution of the new fundamental equation. Also, we show that the closed-form solution of the new fundamental equation can generate the general solution of the original wave equation and investigate the characteristics of the derived general solution.

cs.SI↗

arXiv2021

Increase of Low-Frequency Modes of User Dynamics in Online Social Networks During Overheating of Discussions

User dynamics in online social networks have a significant impact on not only the online community but also real-world activities. As examples, we can mention explosive user dynamics triggered by social polarization, echo chamber phenomena, fake news, etc. Explosive user dynamics are frequently called online flaming. The wave equation-based model for online social networks (called the oscillation model) is a theoretical model proposed to describe user dynamics in online social networks. This model can be used to understand the relationship between explosive user dynamics and the structure of social networks. However, since the oscillation model was introduced as a purely theoretical model of social networks, it is necessary to confirm whether the model describes real phenomena correctly or not. In this paper, we first show a prediction from the oscillation model; the low-frequency oscillation mode of user dynamics will be dominant when the structure of online social networks changes so that user activity is activated. To verify the predictions with actual data, we show spectral analyses of both the log data of posts on an electronic bulletin board site and the frequency data of word search from Google Trends. The results support the predictions from the theoretical model.

cs.SI↗

arXiv2021

Evaluation of User Dynamics Created by Weak Ties among Divided Communities

Flaming phenomena represent the divergence in the strength of user dynamics as created by user interactions in online social networks (OSNs). Although it has been known that flaming phenomena occur when the Laplacian matrix of the OSN has non-real eigenvalues, it was recently shown that flaming phenomena may occur even if all the eigenvalues are real numbers. This effect appears only in the situation that some eigenvalues are degenerate, and a special unitary transformation is applied to the equations representing user dynamics; whether actual OSNs satisfy this condition has not been fully discussed. In this paper, we clarify that the user dynamics caused by the degeneration of eigenvalue 0 is one specific example of the above condition. We also investigate the mechanism and characteristics of flaming phenomena generated by degenerated eigenvalues. Furthermore, we demonstrate through numerical simulations that the degeneration of eigenvalues can cause divergence.

cs.SI↗

arXiv2020

Independence of the Fundamental Equation of the Oscillation Model on Algebraic Representations: Social Media Echo Chamber Effect

In the oscillation model that describes the user dynamics of online social networks, it is known that the fundamental equation can explicitly describe the causal relationship between the network structure and user dynamics. The fundamental equation uses algebra that satisfies the anti-commutation relation, and its matrix representation is not unique. However, even if the matrix representations are different, the same results should be derived from different representations of the fundamental equation if they are describing the same phenomenon. In this paper, we confirm, using the echo-chamber effect as an example, that the fundamental equations of different matrix representations lead to the same result.

cs.SI↗

arXiv2020

Modeling of Online Echo-Chamber Effect Based on the Concept of Spontaneous Symmetry Breaking

The online echo-chamber effect is a phenomenon in which beliefs that are far from common sense are strengthened within relatively small communities formed within online social networks. Since it is significantly degrading social activities in the real world, we should understand how the echo-chamber effect arises in an engineering framework to realize countermeasure technologies. This paper proposes a model of the online echo-chamber effect by introducing the concept of spontaneous symmetry breaking to the oscillation model framework used for describing online user dynamics.

cs.SI↗

arXiv2020

Technology to Counter Online Flaming Based on the Frequency-Dependent Damping Coefficient in the Oscillation Model

Online social networks, which are remarkably active, often experience explosive user dynamics such as online flaming, which can significantly impact the real world. However, countermeasures based on social analyses of the individuals causing flaming are too slow to be effective because of the rapidity with which the influence of online user dynamics propagates. A countermeasure technology for the flaming phenomena based on the oscillation model, which describes online user dynamics, has been proposed; it is an immediate solution as it does not depend on social analyses of individuals. Conventional countermeasures based on the oscillation model assume that the damping coefficient is a constant regardless of the eigenfrequency. This assumption is, however, problematic as the damping coefficients are, in general, inherently frequency-dependent; the theory underlying the dependence is being elucidated. This paper discusses a design method that uses the damping coefficient to prevent flaming under general conditions considering the frequency-dependence of the damping coefficient and proposes a countermeasure technology for the flaming phenomena.

cs.SI↗

arXiv2020

A Model of Polarization on Social Media Caused by Empathy and Repulsion

In recent years, the ease with which social media can be accessed has led to the unexpected problem of a shrinkage in information sources. This phenomenon is caused by a system that facilitates the connection of people with similar ideas and recommendation systems. Bias in the selection of information sources promotes polarization that divides people into multiple groups with opposing views and creates conflicts between opposing groups. This paper elucidates the mechanism of polarization by proposing a model of opinion formation in social media that considers users' reactions of empathy and repulsion. Based on the idea that opinion neutrality is only relative, this model offers a novel technology for dealing with polarization.

cs.SI↗

arXiv2020

Closed-Form Solutions of the Fundamental Equation That Describes User Dynamics in Online Social Networks

The oscillation model, based on the wave equation on networks, can describe user dynamics in online social networks. The fundamental equation of user dynamics can be introduced into the oscillation model to explicitly describe the causal relation of user dynamics yielded by certain specific network structures. Moreover, by considering the sparseness of online social networks, a novel fundamental equation of different form has been devised. In this paper, we derive a closed-form solution of the new fundamental equation. Also, we find the closed-form solution of the new fundamental equation can generate the general solution of the original wave equation.

cs.SI↗

arXiv2020

Polarization Model of Online Social Networks Based on the Concept of Spontaneous Symmetry Breaking

The spread of information networks has not only made it easier for people to access a variety of information sources but also greatly enhanced the ability of individuals to disseminate information. Unfortunately, however, the problem of slander in online social networks shows that the evolving information network environment does not necessarily support mutual understanding in society. Since information with particular bias is distributed only to those communities that prefer it, the division of society into various opposing groups is strengthened. This phenomenon is called polarization. It is necessary to understand the mechanism of polarization to establish technologies that can counter polarization. This paper introduces a fundamental model for understanding polarization that is based on the concept of spontaneous symmetry breaking; our starting point is the oscillation model that describes user dynamics in online social networks.

cs.SI↗

arXiv2020

A New Model of Flaming Phenomena in Online Social Networks that Considers Resonance Driven by External Stimuli

The explosive user dynamics represented by flaming phenomena in online social networks can sometimes negatively influence lives in the real world. To take measures against online flaming phenomena promptly, it is necessary to model its defining characteristics. Based on the oscillation model that describes user dynamics on networks, previous work has revealed that online flaming arises when some eigenvalues of the matrix expressing network structure are non-real numbers. This paper considers the network resonance driven by periodic external stimuli and proposes a flaming model that posits flaming even if all the matrix's eigenvalues are real numbers. Also, we describe a theoretical framework for observing the omen of online flaming to trigger preventive measures.

cs.SI↗

arXiv2020

The Wigner's Semicircle Law of Weighted Random Networks

The spectral graph theory provides an algebraical approach to investigate the characteristics of weighted networks using the eigenvalues and eigenvectors of a matrix (e.g., normalized Laplacian matrix) that represents the structure of the network. However, it is difficult for large-scale and complex networks (e.g., social network) to represent their structure as a matrix correctly. If there is a universality that the eigenvalues are independent of the detailed structure in large-scale and complex network, we can avoid the difficulty. In this paper, we clarify the Wigner's Semicircle Law for weighted networks as such a universality. The law indicates that the eigenvalues of the normalized Laplacian matrix for weighted networks can be calculated from the a few network statistics (the average degree, the average link weight, and the square average link weight) when the weighted networks satisfy the sufficient condition of the node degrees and the link weights.

physics.soc-ph↗

arXiv2020

On the fundamental equation of user dynamics and the structure of online social networks

Online social networks suffer from explosive user dynamics such as flaming that can seriously affect social activities in the real world because the dynamics have growth rates that can overwhelm our rational decision making faculties. Therefore, a deeper understanding of user dynamics in online social networks is a fundamental problem in computer and information science. One of the effective user dynamics models is the networked oscillation model; it uses a second-order differential equation with Laplacian matrix. Although our previous study indicates that the oscillation model provides us with a minimal but effective model of user interactions, there still remains the open problem as to the existence of a first-order fundamental differential equation that respects the structure of the original network. This paper fills in this gap and shows that, by doubling the dimension of the state space, we can explicitly but naturally construct a fundamental equation that fully respects the structure of the original network.

cs.SI↗