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Yuichi Ikeda

Publications and source records attributed to Yuichi Ikeda.

At least 19 recordsLinked to original sources

Tracing Stablecoin Contagion during the USDC Depeg after the Silicon Valley Bank Collapse

The March 2023 collapse of Silicon Valley Bank (SVB) disrupted the core premise of stablecoins, which are digital tokens designed to maintain a fixed value against the U.S. dollar and serve as on-chain substitutes for dollar liquidity. The event triggered a sharp depeg of USDC, creating a rare exogenous shock to the stablecoin ecosystem. While price deviations during this crisis are well documented, the underlying behavioral reorganization of on-chain activity remains less understood. Here, we analyze high-granularity transaction data to measure the shock's effects on network activities, volumes, and prices, reconstructing the contagion pathway from market-wide synchronization down to account-level reallocation. By extracting phase dynamics, we first show that transaction activity across major stablecoins became strongly synchronized during the crisis window, indicating a collective market-level response. We then uncover a bifurcated contagion pathway. While USDT, WBTC, and WETH reacted primarily as liquidity absorption channels with larger trade volumes, only USDC-related assets exhibited immediate price responses alongside surging transaction counts. This reflects the dominant role of USDC-related assets in this incident and their immediate behavioral connection to user panic, driving a mass reallocation from single-coin to multi-coin portfolios. Finally, governed by persistent intraday time-zone rhythms and balance-size heterogeneity, these findings provide a comprehensive empirical framework for understanding systemic risk and flight-to-quality mechanisms in fractional-reserve digital asset networks.

cs.CE

Anomaly prediction in XRP price with topological features

The aim of this research is to study XRP cryptoasset price dynamics, with a particular focus on forecasting atypical price movements. Recent studies suggest that topological properties of transaction graphs are highly informative for understanding cryptocurrency price behavior. In this work, we show that specific topological properties of the XRP transaction graphs provide important information about extreme XRP price surges, and can be used for more competitive prediction of anomalous price dynamics.

q-fin.ST

Arbitrage impact on the relationship between XRP price and correlation tensor spectra of transaction networks

The increasing use of cryptoassets for international remittances has proven to be faster and more cost-effective, particularly for migrants without access to traditional banking. However, the inherent volatility of cryptoasset prices, independent of blockchain-based remittance mechanisms, introduces potential risks during periods of high volatility. This study investigates the intricate dynamics between XRP price fluctuations across diverse crypto exchanges and the correlation of the largest singular values of the correlation tensor of XRP transaction networks. Particularly, we show the impact of arbitrage opportunities across different crypto exchanges on the relationship between XRP price and correlation tensor spectra of transaction networks. Distinct periods, non-bubble and bubble, showcase different characteristics in XRP price fluctuations. Establishing a connection between XRP price and transaction networks, we compute correlation tensors and singular values, emphasizing the significance of the largest singular value. Comparisons with reshuffled and Gaussian random correlation tensors validate the uniqueness of the empirical tensor. A set of simulated weekly XRP prices, resembling arbitrage opportunities across various crypto exchanges, further confirms the robustness of our findings. It reveals a pronounced anti-correlation during bubble periods and a non-significant correlation during non-bubble periods with the largest singular value, irrespective of price fluctuations across different crypto exchanges.

physics.soc-ph

Exploring language endangerment: historical, geographical, and economic insights from multilayer language-country bipartite network analysis

Language endangerment is a phenomenon in which approximately 40% of languages spoken worldwide are predicted to disappear within the next few decades, resulting in the loss of cultures associated with these languages. To take effective measures against language endangerment, it is essential to quantitatively understand its characteristics because it is a phenomenon in which historical, geographical, and economic factors are intricately intertwined. In this study, multilayer language-country bipartite networks are constructed using information about which countries each language is spoken in and two types of linguistic features, namely the existence of a writing system and the function within a country. In addition, percolation simulations are conducted to measure how language and country networks break down according to the extinction of languages and to identify vulnerable connections in them. In the language network of officially used languages with their writing system, the community analysis indicated that there were communities composed of languages spoken over geographically separated distances. The strength of languages revealed that the official languages in the former colonial nations, namely English, French, Spanish, Dutch, Portuguese, and Russian, still played significant roles in the formation of these communities. In the language and country networks of unofficially used languages without their writing system, the percolation simulation revealed that languages were likely to severely disappear in the Americas, and that linguistic diversity was vulnerable in affluent countries. The findings show that the analysis of multilayer language-country bipartite networks has enabled a quantitative understanding of the language endangerment occurring worldwide from historical, geographical, and economic perspectives.

physics.soc-ph

Dynamic relationship between XRP price and correlation tensor spectra of the transaction network

The emergence of cryptoassets has sparked a paradigm shift in the world of finance and investment, ushering in a new era of digital assets with profound implications for the future of currency and asset management. A recent study showed that during the bubble period around the year, 2018, the price of cryptoasset, XRP has a strong anti correlation with the largest singular values of the correlation tensors obtained from the weekly XRP transaction networks. In this study, we provide a detailed analysis of the method of correlation tensor spectra for XRP transaction networks. We calculate and compare the distribution of the largest singular values of the correlation tensor using the random matrix theory with the largest singular values of the empirical correlation tensor. We investigate the correlation between the XRP price and the largest singular values for a period spanning two years. We also uncover the distinct dependence between XRP price and the singular values for bubble and non-bubble periods. The significance of time evolution of singular values is shown by comparison with the evolution of singular values of the reshuffled correlation tensor. Furthermore, we identify a set of driver nodes in the transaction networks that drives the market during the bubble period using the singular vectors.

q-fin.ST

Embedding and correlation tensor for XRP transaction networks

Cryptoassets are growing rapidly worldwide. One of the large cap cryptoassets is XRP. In this article, we focus on analyzing transaction data for the 2017-2018 period that consist one of the significant XRP market price bursts. We construct weekly weighted directed networks of XRP transactions. These weekly networks are embedded on continuous vector space using a network embedding technique that encodes structural regularities present in the network structure in terms of node vectors. Using a suitable time window we calculate a correlation tensor. A double singular value decomposition of the correlation tensor provides key insights about the system. The significance of the correlation tensor is captured using a randomized correlation tensor. We present a detailed dependence of correlation tensor on model parameters.

physics.soc-ph

Projecting XRP price burst by correlation tensor spectra of transaction networks

Cryptoassets are becoming essential in the digital economy era. XRP is one of the large market cap cryptoassets. Here, we develop a novel method of correlation tensor spectra for the dynamical XRP networks, which can provide an early indication for XRP price. A weighed directed weekly transaction network among XRP wallets is constructed by aggregating all transactions for a week. A vector for each node is then obtained by embedding the weekly network in continuous vector space. From a set of weekly snapshots of node vectors, we construct a correlation tensor. A double singular value decomposition of the correlation tensors gives its singular values. The significance of the singular values is shown by comparing with its randomize counterpart. The evolution of singular values shows a distinctive behavior. The largest singular value shows a significant negative correlation with XRP/USD price. We observe the minimum of the largest singular values at the XRP/USD price peak during the first week of January 2018. The minimum of the largest singular value during January 2018 is explained by decomposing the correlation tensor in the signal and noise components and also by evolution of community structure.

physics.soc-ph

Community Structure and Its Stability on a Face-to-Face Interaction Network in Kyoto City

As social behavior plays an essential role in people's lives, the features of face-to-face interaction networks must be examined to understand people's social behavior. In this study, we focused on the stable community structure of a face-to-face interaction network because it explains the persistent communities caused by the stationary communication patterns of citizens and visitors in a city. We regarded citizens and visitors as two kinds of particles and the community as a phase and theorized the stability of the community structure using the equilibrium conditions among communities. We formulated the chemical potentials of the communities and examined whether they were in equilibrium under the assumption of a canonical ensemble. We estimated the chemical potentials of persistent communities and found that these values matched within approximately 10% error for each day. This result indicates that the cause of persistent communities is the stability of community structure.

physics.soc-ph

First Demonstration Experiment for Energy Trading System EDISON-X Using the XRP Ledger

We develop an energy trading system, EDISON-X, that uses blockchain technology to manage the buying and selling of electricity usage rights, i.e., tokens. UPX and SPX tokens purchase electricity from the utility company's distribution lines and the photovoltaic panels. On July 1, 17 students in our school dormitory participated in an experiment to confirm the operation of the EDISON-X system. Based on the results of this experiment, we discuss the energy trading system using blockchain technology for the effective usage of renewable energy. We develop topology and network science methodologies to understand the characteristics of energy trading. We test the hypothesis that market transactions become less active when ``cavities'' appear using persistent homology. The preliminary result implies that the hypothesis could be adopted. We, however, need more data samples.

cs.CR

Hodge Decomposition of the Remittance Network on the XRP ledger in the Price Hike of January 2018

This study analyzes the remittance transaction recorded on the XRP ledger for ETH and USD from July 2017 to Jun 2018, including the bubble period in early 2018. Using the Hodge decomposition, we estimate the ``loop flow'' in the international remittance of cryptoassets during the bubble period. We found characteristic differences between those fiat currencies and cryptoassets during the bubble period. For ETH, there was a significant increase in the loop flow during the cryptoasset price peak. This might be related to money laundering or arbitrage transaction. There was a slight increase in the loop flow for USD during the cryptoasset price peak.

cs.CR

Regional medical inter-institutional cooperation in medical provider network constructed using patient claims data from Japan

The aging world population requires a sustainable and high-quality healthcare system. To examine the efficiency of medical cooperation, medical provider and physician networks were constructed using patient claims data. Previous studies have shown that these networks contain information on medical cooperation. However, the usage patterns of multiple medical providers in a series of medical services have not been considered. In addition, these studies used only general network features to represent medical cooperation, but their expressive ability was low. To overcome these limitations, we analyzed the medical provider network to examine its overall contribution to the quality of healthcare provided by cooperation between medical providers in a series of medical services. This study focused on: i) the method of feature extraction from the network, ii) incorporation of the usage pattern of medical providers, and iii) expressive ability of the statistical model. Femoral neck fractures were selected as the target disease. To build the medical provider networks, we analyzed the patient claims data from a single prefecture in Japan between January 1, 2014 and December 31, 2019. We considered four types of models: a model using node strength and linear regression to a model using feature representation by node2vec and regression tree ensemble, which is a machine learning method. The results showed that a stronger medical provider reduces the duration of hospital stay. The overall contribution of the medical cooperation to the duration of hospital stay extracted from the medical provider network using node2vec is approximately 20%, which is approximately 20 times higher than the model using strength.

physics.soc-ph

Reconstruction of Interbank Network using Ridge Entropy Maximization Model

We develop a network reconstruction model based on entropy maximization considering the sparsity of networks. We reconstruct the interbank network in Japan from financial data in individual banks' balance sheets using the developed reconstruction model from 2000 to 2016. The observed sparsity of the interbank network is successfully reproduced. We examine the characteristics of the reconstructed interbank network by calculating important network attributes. We obtain the following characteristics, which are consistent with the previously known stylized facts. Although we do not introduce the mechanism to generate the core and peripheral structure, we impose the constraints to consider the sparsity that is no transactions within the same bank category except for major commercial banks, the core and peripheral structure has spontaneously emerged. We identify major nodes in each community using the value of PageRank and degree to examine the changing role of each bank category. The observed changing role of banks is considered a result of the quantitative and qualitative monetary easing policy started by the Bank of Japan in April 2013.

econ.GN

Optimizing travel routes using temporal networks constructed from GPS data

Because of the complexity of urban transportation networks and the temporal changes in traffic conditions, it is difficult to assess real-time traffic situations. However, the development of information terminals has made it easier to obtain personal mobility information. In this study, we propose methods for evaluating the mobility of people in a city using global positioning system data. There are two main methods for evaluating movement. One is to create a temporal network from real data and check the change in travel time according to time zones or seasons. Temporal networks are difficult to evaluate because of their time complexity, and in this study, we proposed an evaluation method using the probability density function of travel time. The other method is to define a time-dependent traveling salesman problem and find an efficient traveling route by finding the shortest path. By creating a time-dependent traveling salesman problem in an existing city and solving it, a traveler can choose an efficient route by considering traffic conditions at different times of the day. We used 2 months of data from Kyoto City to conduct a traffic evaluation as a case study.

cs.SI

Regional economic integration via detection of circular flow in international value-added network

Global value chains (GVCs) are formed through value-added trade, and some regions promote economic integration by concluding regional trade agreements to promote these chains. However, there is no way to quantitatively assess the scope and extent of economic integration involving various sectors in multiple countries. In this study, we used the World Input--Output Database to create a cross-border sector-wise trade in value-added network (international value-added network (IVAN)) covering the period of 2000--2014 and evaluated them using network science methods. By applying Infomap to the IVAN, we confirmed for the first time the existence of two regional communities: Europe and the Pacific Rim. Helmholtz--Hodge decomposition was used to decompose the value flows within the region into potential and circular flows, and the annual evolution of the potential and circular relationships between countries and sectors was clarified. The circular flow component of the decomposition was used to define an economic integration index, and findings confirmed that the degree of economic integration in Europe declined sharply after the economic crisis in 2009 to a level lower than that in the Pacific Rim. The European economic integration index recovered in 2011 but again fell below that of the Pacific Rim in 2013. Moreover, sectoral analysis showed that the economic integration index captured the effect of Russian mineral resources, free movement of labor in Europe, and international division of labor in the Pacific Rim, especially in GVCs for the manufacture of motor vehicles and high-tech products.

cs.SI

Network Analysis of Attitudes towards Immigrants in Asia

This study models cross-national attitudes towards immigrants in East and Southeast Asia as a signed and weighted bipartite network of countries and evaluative reactions to a variety of political issues, or determinants. This network is then projected into two one-mode networks, one of countries and one of determinants, and community detection methods are applied. The paper aims to fill two deficiencies in the current research on attitudes towards immigrants: 1) the lack of cross-national studies in Asia, a region where migration is growing, and 2) the tendency of researchers to treat determinants as uncorrelated, despite the interdependent nature of evaluative reactions. The results show that the nine countries in the sample are a cohesive clique, showing greater similarities than differences in the determinants of their attitudes. A blockmodeling approach was employed to identify eight determinants in attitudes towards immigrants, namely views on independence and social dependencies, group identities, absolute or relative moral orientation, attitudes towards democracy, science and technology, prejudice and stigma, and two determinants related to religion. However, the findings of this survey yielded some surprising results when compared with the literature review. First, education was not found to be a significant determinants of attitudes towards immigrants, despite its strong and consistent predictive power in European models. Second, prejudice appears to be mediated in part by religion, especially in religious identification and belief in God. Group identity and prejudice also appear to be related, though only weakly. Finally, anxiety appears in clusters related to social norms, suggesting that fears regarding immigrants relates closely to expectations of others' behavior.

physics.soc-ph

Multilayer Network Analysis of the Drug Pipeline in the Global Pharmaceutical Industry

Generally, open innovation is a lucrative research topic within industries relying on innovation, such as the pharmaceutical industry, which are also known as knowledge-intensive industries. However, the dynamics of drug pipelines within a small-medium enterprise level in the global economy remains concerning. To reveal the actual situation of pharmaceutical innovation, we investigate the feature of knowledge flows between the licensor and licensee in the drug pipeline based on a multilayer network constructed with the drug pipeline, global supply chain, and ownership data. Thus, our results demonstrate proven similarities between the knowledge flows in the drug pipeline among the supply chains, which generally agrees with the situation of pharmaceutical innovation collaborated with other industries, such as the artificial intelligence industry.

physics.soc-ph

Bow-tie structure and community identification of global supply chain network

We study on topological properties of global supply chain network in terms of degree distribution, hierarchical structure, and degree-degree correlation in the global supply chain network. The global supply chain data is constructed by collecting various company data from the web site of Standard & Poor's Capital IQ platform in 2018. The in- and out-degree distributions are characterized by a power law with in-degree exponent = 2.42 and out-degree exponent = 2.11. The clustering coefficient decays as power law with an exponent = 0.46. The nodal degree-degree correlation indicates the absence of assortativity. The Bow-tie structure of GWCC reveals that the OUT component is the largest and it consists 41.1% of total firms. The GSCC component comprises 16.4% of total firms. We observe that the firms in the upstream or downstream sides are mostly located a few steps away from the GSCC. Furthermore, we uncover the community structure of the network and characterize them according to their location and industry classification. We observe that the largest community consists of consumer discretionary sector mainly based in the US. These firms belong to the OUT component in the bow-tie structure of the global supply chain network. Finally, we confirm the validity for propositions S1 (short path length), S2 (power-law degree distribution), S3 (high clustering coefficient), S4 ("fit-gets-richer" growth mechanism), S5 (truncation of power-law degree distribution), and S7 (community structure with overlapping boundaries) in the global supply chain network.

physics.soc-ph

An Interacting Agent Model of Economic Crisis

Most national economies are linked by international trade. Consequently, economic globalization forms a massive and complex economic network with strong links, that is, interactions arising from increasing trade. Various interesting collective motions are expected to emerge from strong economic interactions in a global economy under trade liberalization. Among the various economic collective motions, economic crises are our most intriguing problem. In our previous studies, we have revealed that the Kuramoto's coupled limit-cycle oscillator model and the Ising-like spin model on networks are invaluable tools for characterizing the economic crises. In this study, we develop a mathematical theory to describe an interacting agent model that derives the Kuramoto model and the Ising-like spin model by using appropriate approximations. Our interacting agent model suggests phase synchronization and spin ordering during economic crises. We confirm the emergence of the phase synchronization and spin ordering during economic crises by analyzing various economic time series data. We also develop a network reconstruction model based on entropy maximization that considers the sparsity of the network. Here network reconstruction means estimating a network's adjacency matrix from a node's local information. The interbank network is reconstructed using the developed model, and a comparison is made of the reconstructed network with the actual data. We successfully reproduce the interbank network and the known stylized facts. In addition, the exogenous shock acting on an industry community in a supply chain network and financial sector are estimated. Estimation of exogenous shocks acting on communities of in the real economy in the supply chain network provide evidence of the channels of distress propagating from the financial sector to the real economy through the supply chain network.

physics.soc-ph