Detecting possible pairs of materials for composites using a material word co-occurrence network.

Composite materials are popular because of their high performance capabilities, but new material development is time-consuming. To accelerate this process, researchers studying material informatics, an academic discipline combining computational science and material science, have developed less time-consuming approaches for predicting possible material combinations. However, these processes remain problematic because some materials are not suited for them. The limitations of specific candidates for new composites may cause potential new material pairs to be overlooked. To solve this problem, we developed a new method to predict possible composite material pairs by considering more materials than previous techniques. We predicted possible material pairs by conducting link predictions of material word co-occurrence networks while assuming that co-occurring material word pairs in scientific papers on composites were reported as composite materials. As a result, we succeeded in predicting the co-occurrence of material words with high specificity. Nodes tended to link to many other words, generating new links in the created co-occurrence material word network; notably, the number of material words co-occurring with graphene increased rapidly. This phenomenon confirmed that graphene is an attractive composite component. We expect our method to contribute to the accelerated development of new composite materials.

Quantifying progress in research topics across nations.

A scientist's choice of research topic affects the impact of their work and future career. While the disparity between nations in scientific information, funding, and facilities has decreased, scientists on the cutting edge of their fields are not evenly distributed across nations. Here, we quantify relative progress in research topics of a nation from the time-series comparison of reference lists from papers, using 71 million published papers from Scopus. We discover a steady leading-following relationship in research topics between Western nations or Asian city-states and others. Furthermore, we find that a nation's share of information-rich scientists in co-authorship networks correlates highly with that nation's progress in research topics. These results indicate that scientists' relationships continue to dominate scientific evolution in the age of open access to information and explain the failure or success of nations' investments in science.

Obtaining interactions among science, technology, and research policy for developing an innovation strategy: A case study of supercapacitors.

Comprehensive observations of science, technology, and research policy transactions are important for developing an innovation strategy. We propose a new method that combines the academic landscape and matrix analysis to understand the relationships among activities of three aspects of the technological landscape: science, technology, and research policy. First, we divided academic research into 28 knowledge domains by clustering a citation network of scientific papers. Next, we developed a new matrix classifying them into three groups: "mature technology," "intermediate technology," and "emerging technology." The results showed that research domains in "emerging technology" showed a high rate of patent increase, indicating that they were commercializing rapidly. Finally, we identified the group that each country focused on, and this result reflected the countries' research policies. China and Singapore showed high rates, whereas Japan, France, and Germany had low values. This result reflects countries' research policies and implies that specialty research areas differed by country. As above, our research result implies that academia, industry, and government have paid attention to knowledge domains in "emerging technology" and these are important for creating innovation. A supercapacitor, also known as an electric double layer capacitor or ultracapacitor, was selected as an example in our method. This research could help academic researchers, industrial companies, and policymakers in developing innovation strategies.

Large-scale analysis of delayed recognition using sleeping beauty and the prince.

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.

Dense and influential core promotion of daily viral information spread in political echo chambers.

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.

Evaluating Nodes of Latent Mediators in Heterogeneous Communities.

Conventionally, the importance of nodes in a network has been debated from the viewpoint of the amount of information received by the nodes and its neighbors. While node evaluation based on the adjacency relationship mainly uses local proximity information, the community structure that characterizes the network has hardly been considered. In this study, we propose a new node index that contributes to the understanding of the inter-community structure of a network by combining the measures of link distribution and community relevance. The visualization of node rankings and rank correlations with respect to the attack tolerance of networks demonstrated that the proposed index shows the highest performance in comparison with five previously proposed indexes, suggesting a new way to detect latent mediators in heterogeneous networks.

Detecting trends in academic research from a citation network using network representation learning.

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.