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Kimitaka Asatani
Graduate School of Engineering, The University of Tokyo, Tokyo, Japan

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Research
Published: 30 June 2021 in Applied Network Science
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Delayed recognition in which innovative discoveries are re-evaluated after a long period has significant implications for scientific progress. The quantitative method to detect delayed recognition is described as the pair of Sleeping Beauty (SB) and its Prince (PR), where SB refers to citation bursts and its PR triggers SB’s awakeness calculated based on their citation history. This research provides the methods to extract valid and large SB–PR pairs from a comprehensive Scopus dataset and analyses how PR discovers SB. We prove that the proposed method can extract long-sleep and large-scale SB and its PR best covers the previous multi-disciplinary pairs, which enables to observe delayed recognition. Besides, we show that the high-impact SB–PR pairs extracted by the proposed method are more likely to be located in the same field. This indicates that a hidden SB that your research can awaken may exist closer than you think. On the other hand, although SB–PR pairs are fat-tailed in Beauty Coefficient and more likely to integrate separate fields compared to ordinary citations, it is not possible to predict which citation leads to awake SB using the rarity of citation. There is no easy way to limit the areas where SB–PR pairs occur or detect it early, suggesting that researchers and administrators need to focus on a variety of areas. This research provides comprehensive knowledge about the development of scientific findings that will be evaluated over time.

ACS Style

Takahiro Miura; Kimitaka Asatani; Ichiro Sakata. Large-scale analysis of delayed recognition using sleeping beauty and the prince. Applied Network Science 2021, 6, 1 -28.

AMA Style

Takahiro Miura, Kimitaka Asatani, Ichiro Sakata. Large-scale analysis of delayed recognition using sleeping beauty and the prince. Applied Network Science. 2021; 6 (1):1-28.

Chicago/Turabian Style

Takahiro Miura; Kimitaka Asatani; Ichiro Sakata. 2021. "Large-scale analysis of delayed recognition using sleeping beauty and the prince." Applied Network Science 6, no. 1: 1-28.

Journal article
Published: 05 April 2021 in Scientific Reports
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Despite the intensive study of the viral spread of fake news in political echo chambers (ECs) on social networking services (SNSs), little is known regarding the underlying structure of the daily information spread in these ECs. Moreover, the effect of SNSs on opinion polarisation is still unclear in terms of pluralistic information access or selective exposure to opinions in an SNS. In this study, we confirmed the steady, highly independent nature of left- and right-leaning ECs, both of which are composed of approximately 250,000 users, from a year-long reply/retweet network of 42 million Japanese Twitter users. We found that both communities have similarly efficient information spreading networks with densely connected and core-periphery structures. Core nodes resonate in the early stages of information cascades, and unilaterally transmit information to peripheral nodes. Each EC has resonant core users who amplify and steadily spread information to a quarter of a million users. In addition, we confirmed the existence of extremely aggressive users of ECs who co-reply/retweet each other. The connection between these users and top influencers suggests that the extreme opinions of the former group affect the entire community through the top influencers.

ACS Style

Kimitaka Asatani; Hiroko Yamano; Takeshi Sakaki; Ichiro Sakata. Dense and influential core promotion of daily viral information spread in political echo chambers. Scientific Reports 2021, 11, 1 -10.

AMA Style

Kimitaka Asatani, Hiroko Yamano, Takeshi Sakaki, Ichiro Sakata. Dense and influential core promotion of daily viral information spread in political echo chambers. Scientific Reports. 2021; 11 (1):1-10.

Chicago/Turabian Style

Kimitaka Asatani; Hiroko Yamano; Takeshi Sakaki; Ichiro Sakata. 2021. "Dense and influential core promotion of daily viral information spread in political echo chambers." Scientific Reports 11, no. 1: 1-10.

Review
Published: 21 February 2020 in Energies
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Scientific research plays an important role in the achievement of a sustainable society. However, grasping the trends in sustainability research is difficult because studies are not devised and conducted in a top-down manner with Sustainable Development Goals (SDGs). To understand the bottom-up research activities, we analyzed over 300,000 publications concerned with sustainability by using citation network analysis and natural language processing. The results suggest that sustainability science’s diverse and dynamic changes have been occurring over the last few years; several new topics, such as nanocellulose and global health, have begun to attract widespread scientific attention. We further examined the relationship between sustainability research subjects and SDGs and found significant correspondence between the two. Moreover, we extracted SDG topics that were discussed following a convergent approach in academic studies, such as “inclusive society” and “early childhood development”, by observing the convergence of terms in the citation network. These results are valuable for government officials, private companies, and academic researchers, empowering them to understand current academic progress along with research attention devoted to SDGs.

ACS Style

Kimitaka Asatani; Haruo Takeda; Hiroko Yamano; Ichiro Sakata. Scientific Attention to Sustainability and SDGs: Meta-Analysis of Academic Papers. Energies 2020, 13, 975 .

AMA Style

Kimitaka Asatani, Haruo Takeda, Hiroko Yamano, Ichiro Sakata. Scientific Attention to Sustainability and SDGs: Meta-Analysis of Academic Papers. Energies. 2020; 13 (4):975.

Chicago/Turabian Style

Kimitaka Asatani; Haruo Takeda; Hiroko Yamano; Ichiro Sakata. 2020. "Scientific Attention to Sustainability and SDGs: Meta-Analysis of Academic Papers." Energies 13, no. 4: 975.

Conference paper
Published: 20 September 2018 in Transactions on Petri Nets and Other Models of Concurrency XV
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In this paper, we focus on unilateral preference for a group of specific kind of persons as a factor of network formation. Homophily and preferential attachment explain a large part of the formation of online social networks (OSN). Unilateral preference is also assumed to have important roles in OSNs, where high searchability exists with no geographical restriction. To observe unilateral preferences in a social network, we analyzed a user network constructed through interaction between those who make Japanese tweet(s) about “runaway” and those who react to them. In this case, a large proportion of the tweets are assumed to be made by young girls and most of the latter are adult men. By observing the user network, the network is found to have unsurprisingly bipartite structure composed of a thousand former users and several thousand latter users. In spite of a few friendship links among these users, about 19% of users in the latter group take one-to-many communication with users in the former group. Therefore, communications that assumed to be based on unilateral preference exist on a considerable scale. The proportion of reply message between users that regarded to have an intention of luring is surprisingly high (61%). Furthermore, we extract the core of communication by applying k-core network analysis. As a result, the proportion of luring in the core of the network is significantly higher than outside of the k-core network.

ACS Style

Kimitaka Asatani; Yasuko Kawahata; Fujio Toriumi; Ichiro Sakata. Communication Based on Unilateral Preference on Twitter: Internet Luring in Japan. Transactions on Petri Nets and Other Models of Concurrency XV 2018, 54 -66.

AMA Style

Kimitaka Asatani, Yasuko Kawahata, Fujio Toriumi, Ichiro Sakata. Communication Based on Unilateral Preference on Twitter: Internet Luring in Japan. Transactions on Petri Nets and Other Models of Concurrency XV. 2018; ():54-66.

Chicago/Turabian Style

Kimitaka Asatani; Yasuko Kawahata; Fujio Toriumi; Ichiro Sakata. 2018. "Communication Based on Unilateral Preference on Twitter: Internet Luring in Japan." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 54-66.

Research article
Published: 06 August 2018 in Journal of Computational Social Science
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With increases in the amount of human trajectory data, interest in explaining or predicting human mobility is growing. Owing to the difficulty of associating mobility data with interpersonal relationship data, previous studies on the link between interpersonal relationships and mobility are limited to the specific activities of particular users. In this paper, we propose a method for detecting interpersonal relationships from mobility data, while distinguishing these relationships from those of familiar strangers such as commuters. In the method, persons who take diverse variations within the same activities are recognized as a pair. From IC card data covering the daily mobility of six million people over three years, we detected millions of frequently co-located pairs. Under certain conditions, most of the detected pairs are confirmed as not being familiar strangers, but rather to have an interpersonal relationship. Next, we analyzed the detected pairs and found that the density of the relationships between groups was divided by gender and age and was found to be asymmetric by gender. For example, an elderly male person is not likely to take trips as a pair with a same-gender elderly person, and this result is data-based evidence for the isolation of retired men. In addition, group trips are confirmed to have an extraordinal character and sometimes converge spatiotemporally. These findings indicate that interpersonal relationship is a strong factor to determine their mobility and group observation is potentially useful for event detection.

ACS Style

Kimitaka Asatani; Fujio Toriumi; Junichiro Mori; Masanao Ochi; Ichiro Sakata. Detecting interpersonal relationships in large-scale railway trip data. Journal of Computational Social Science 2018, 1, 313 -326.

AMA Style

Kimitaka Asatani, Fujio Toriumi, Junichiro Mori, Masanao Ochi, Ichiro Sakata. Detecting interpersonal relationships in large-scale railway trip data. Journal of Computational Social Science. 2018; 1 (2):313-326.

Chicago/Turabian Style

Kimitaka Asatani; Fujio Toriumi; Junichiro Mori; Masanao Ochi; Ichiro Sakata. 2018. "Detecting interpersonal relationships in large-scale railway trip data." Journal of Computational Social Science 1, no. 2: 313-326.

Research article
Published: 21 May 2018 in PLOS ONE
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Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node’s degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth.

ACS Style

Kimitaka Asatani; Junichiro Mori; Masanao Ochi; Ichiro Sakata. Detecting trends in academic research from a citation network using network representation learning. PLOS ONE 2018, 13, e0197260 .

AMA Style

Kimitaka Asatani, Junichiro Mori, Masanao Ochi, Ichiro Sakata. Detecting trends in academic research from a citation network using network representation learning. PLOS ONE. 2018; 13 (5):e0197260.

Chicago/Turabian Style

Kimitaka Asatani; Junichiro Mori; Masanao Ochi; Ichiro Sakata. 2018. "Detecting trends in academic research from a citation network using network representation learning." PLOS ONE 13, no. 5: e0197260.