Journal articles: 'Historical networks'

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Goldfarb, Doron, Max Arends, Josef Froschauer, and Dieter Merkl. "Comparing Art Historical Networks." Leonardo 46, no. 3 (June 2013): 279. http://dx.doi.org/10.1162/leon_a_00575.

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This work provides a comparison of link structures present in a common subset of art history related biographic person records/articles from the Getty Union List of Artist Names and English Wikipedia.

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Alcantara, Marlon C., Marco Braga, and Charles van den Heuvel. "Historical Networks in Science Education." Science & Education 29, no. 1 (January 9, 2020): 101–21. http://dx.doi.org/10.1007/s11191-019-00096-4.

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Ju, Harang, Dale Zhou, Ann S. Blevins, David M. Lydon-Staley, Judith Kaplan, Julio R. Tuma, and Dani S. Bassett. "Historical growth of concept networks in Wikipedia." Collective Intelligence 1, no. 2 (October 2022): 263391372211098. http://dx.doi.org/10.1177/26339137221109839.

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Philosophers of science have long questioned how collective scientific knowledge grows. Although disparate answers have been posited, empirical validation has been challenging due to limitations in collecting and systematizing large historical records. Here, we introduce new methods to analyze scientific knowledge formulated as a growing network of articles on Wikipedia and their hyperlinks. We demonstrate that in Wikipedia, concept networks in subdisciplines of science do not grow by expanding from their central core to reach an ancillary periphery. Instead, science concept networks in Wikipedia grow by creating and filling knowledge gaps. Notably, the process of gap formation and closure may be valued by the scientific community, as evidenced by the fact that it produces discoveries that are more frequently awarded Nobel prizes than other processes. To determine whether and how the gap process is interrupted by paradigm shifts, we operationalize a paradigm as a particular subdivision of scientific concepts into network modules. Hence, paradigm shifts are reconfigurations of those modules. The approach allows us to identify a temporal signature in structural stability across scientific subjects in Wikipedia. In a network formulation of scientific discovery, our findings suggest that data-driven conditions underlying scientific breakthroughs depend as much on exploring uncharted gaps as on exploiting existing disciplines and support policies that encourage new interdisciplinary research.

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Kaiser, Wolfram. "Bringing History Back In To the Study of Transnational Networks in European Integration." Journal of Public Policy 29, no. 2 (July 3, 2009): 223–39. http://dx.doi.org/10.1017/s0143814x09001032.

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AbstractThis article discusses the results of recent historical research on the governance of networks and their impact on policy-making in the formative period of the EU. It concludes that historically aware research on networks in EU governance has great potential. In particular, in can contribute to enhancing our knowledge about the formation and dynamics of networks; conceptualising the role of supranational institutions such as the Commission in instigating network formation and steering new networks; and improving our understanding of change over time in the governance of networks and their policy impact in the EU.

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Tambs, Lena, Michela De Bernardin, Marta Lorenzon, and Arianna Traviglia. "Bridging Historical, Archaeological and Criminal Networks." Journal of Computer Applications in Archaeology 7, no. 1 (2024): 1–7. http://dx.doi.org/10.5334/jcaa.141.

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Kuzmina, Anna V., and Sergey D. Borisov. "Social networks as a search tool for historical sources." SHS Web of Conferences 103 (2021): 02017. http://dx.doi.org/10.1051/shsconf/202110302017.

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This article is devoted to the use of social networks as a search tool for historical sources. The opportunity is analyzed to use social networks for surveying witnesses and participants in the considered events of regional history of the second half of the 20th century. This article also analyzes advantages of addressing social networks in the students’ projects to improve the oral history methods and development of professional skills of data search and processing. The importance of such joint activity of historians of various generations for patriotic upbringing is emphasized, the relevance of addressing online sources under the pandemic conditions is determined. The use of social networks as a means to replenish gaps in the set of sources is exemplified by the project devoted to social and economic history of Sevastopol. It is summarized that the most informative was the communication with Odnoklassniki network, since it is the leading platform in the Russian segment of Internet regarding the age group of concern: 46+. The methods of operation with social networks are not confined only by the oral history. A researcher is capable to study materials of profile groups and to detect unique photos. It has been concluded that it would be reasonable to perform historical research in social networks by interviewing respondents: bearers of historical memory.

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MacKay, Jon. "Networks of Canadian Business Elites: Historical Corporate Interlock Networks circa 1912." Connections 39, no. 1 (2019): 1–3. http://dx.doi.org/10.21307/connections-2019-001.

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Skeirik, Richard D. "Historical database training method for neural networks." Laboratory Automation & Information Management 33, no. 2 (December 1997): 148–49. http://dx.doi.org/10.1016/s1381-141x(97)80032-9.

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Suissa, Omri, Maayan Zhitomirsky-Geffet, and Avshalom Elmalech. "Toward a Period-specific Optimized Neural Network for OCR Error Correction of Historical Hebrew Texts." Journal on Computing and Cultural Heritage 15, no. 2 (June 30, 2022): 1–20. http://dx.doi.org/10.1145/3479159.

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Over the past few decades, large archives of paper-based historical documents, such as books and newspapers, have been digitized using the Optical Character Recognition (OCR) technology. Unfortunately, this broadly used technology is error-prone, especially when an OCRed document was written hundreds of years ago. Neural networks have shown great success in solving various text processing tasks, including OCR post-correction. The main disadvantage of using neural networks for historical corpora is the lack of sufficiently large training datasets they require to learn from, especially for morphologically rich languages like Hebrew. Moreover, it is not clear what are the optimal structure and values of hyperparameters (predefined parameters) of neural networks for OCR error correction in Hebrew due to its unique features. Furthermore, languages change across genres and periods. These changes may affect the accuracy of OCR post-correction neural network models. To overcome these challenges, we developed a new multi-phase method for generating artificial training datasets with OCR errors and hyperparameters’ optimization for building an effective neural network for OCR post-correction in Hebrew. To evaluate the proposed approach, a series of experiments using several literary Hebrew corpora from various periods and genres were conducted. The obtained results demonstrate that (1) training a network on texts from a similar period dramatically improves the network's ability to fix OCR errors, (2) using the proposed error injection algorithm, based on character-level period-specific errors, minimizes the need for manually corrected data and improves the network accuracy by 9%, (3) the optimized network design improves the accuracy by 3% compared to the state-of-the-art network, and (4) the constructed optimized network outperforms neural machine translation models and industry-leading spellcheckers. The proposed methodology may have practical implications for digital humanities projects that aim to search and analyze OCRed documents in Hebrew and potentially other morphologically rich languages.

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Pan, Yi-Ru, Pang Hsiao, Chen-Yu Chang, Wen-Jong Ma, Hsiang Hsiao, Pei-Jung Lin, Shih-Chieh Wang, Hui-Jie Yang, Ting-Ting Chi, and Chin-Kun Hu. "Universality and scaling in complex networks from periods of Chinese history." Chaos: An Interdisciplinary Journal of Nonlinear Science 33, no. 1 (January 2023): 011101. http://dx.doi.org/10.1063/5.0134923.

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Critical physical systems with large numbers of molecules can show universal and scaling behaviors. It is of interest to know whether human societies with large numbers of people can show the same behaviors. Here, we use network theory to analyze Chinese history in periods 209 BCE–23 CE and 515–618 CE) related to the Western Han–Xin Dynasty and the late Northern Wei–Sui Dynasty, respectively. Two persons are connected when they appear in the same historical event. We find that the historical networks from two periods separated about 500 years have interesting universal and scaling behaviors, and they are small-world networks; their average cluster coefficients as a function of degree are similar to the network of movie stars. In the historical networks, the persons with larger degrees prefer to connect with persons with a small degree; however, in the network of movie stars, the persons with larger degrees prefer to connect with persons with large degrees. We also find an interesting similar mechanism for the decline or collapse of historical Chinese dynasties. The collapses of the Xin dynasty (9–23 CE) and the Sui dynasty (581–618 CE) were initiated from their arrogant attitude toward neighboring states.

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Heimerl, Lorene. "„Pietismus ist Kommunikation.“ Historische Netzwerkanalyse der Korrespondenz Johann Christoph Martinis (1722–1732)." historia.scribere, no. 12 (June 15, 2020): 139. http://dx.doi.org/10.15203/historia.scribere.12.626.

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“Pietism is Communication.” Historical network analysis of the correspondence of Johann Christoph Martini (1722–1732)The aim of the following seminar paper is to show how and to what extent the lesser-known Halle Pietists contributed and preserved the communication network between Halle and London, especially after the deaths of its main actors. The methodological approach as the main foundation of this paper is a historical network analysis of the correspondence of Johann Christoph Martini. It will be shown that wide networks, such as the network between Halle and London, can only flourish because of small ego-networks such as Martini’s.

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Zhong, Aini, Yukun Wu, Ke Nie, and Mengjun Kang. "Using Local Toponyms to Reconstruct the Historical River Networks in Hubei Province, China." ISPRS International Journal of Geo-Information 9, no. 5 (May 12, 2020): 318. http://dx.doi.org/10.3390/ijgi9050318.

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As an important data source for historical geography research, toponyms reflect the human activities and natural landscapes within a certain area and time period. In this paper, a novel quantitative method of reconstructing historical river networks using toponyms with the characteristics of water and direction is proposed. It is suitable for the study area which possesses rich water resources. To reconstruct the historical shape of the river network, (1) water-related toponyms and direction-related toponyms are extracted as two datasets based on the key words in each village toponym; (2) the feasibility of the river network reconstruction based on these toponyms is validated via a quantitative analysis, according to the spatial distributions of toponyms and rivers; (3) the reconstructed historical shape of the river network can be obtained via qualitative knowledge and geometrical analysis; and (4) the reconstructed rivers are visualized to display their general historical trends and shapes. The results of this paper demonstrate the global correlation and local differences between the toponyms and the river network. The historical river dynamics are revealed and can be proven by ancient maps and local chronicles. The proposed method provides a novel way to reconstruct historical river network shapes using toponym datasets.

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Zhao, Yao, Guangxia Wang, Jian Yang, Lantian Zhang, and Xiaofei Qi. "Building Block Extraction from Historical Maps Using Deep Object Attention Networks." ISPRS International Journal of Geo-Information 11, no. 11 (November 16, 2022): 572. http://dx.doi.org/10.3390/ijgi11110572.

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The geographical feature extraction of historical maps is an important foundation for realizing the transition from human map reading to machine map reading. The current methods for building block extraction from historical maps have many problems, such as low accuracy and poor scalability. Moreover, the high cost of annotating historical maps further limits its applications. In this study, a method for extracting building blocks from historical maps is proposed based on the deep object attention network. Based on the OCRNet framework, multiple attention mechanisms were used to improve the ability of the network to extract the contextual information of the target. Moreover, through the optimization of the feature extraction network structure, the impact of the down-sampling process on local information and boundary contours was reduced, in order to improve the network’s ability to capture boundary information. Subsequently, the transfer learning method was used to jointly train the network model on both remote sensing datasets and few-shot historical map datasets to further improve the feature learning ability of the network, which overcomes the constraints of small sample sizes. The experimental results show that the proposed method can effectively improve the extraction accuracy of building blocks from historical maps.

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Braden, L. E. A. "Networks Created Within Exhibition: The Curators’ Effect on Historical Recognition." American Behavioral Scientist 65, no. 1 (October 15, 2018): 25–43. http://dx.doi.org/10.1177/0002764218800145.

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This research examines artist networks created by shared museum exhibition. While previous research on artistic careers assesses self-cultivated networks, historical recognition may be further influenced by connections created by important others, such as museum curators and art historians. I argue when museum exhibitions show artists together, curators are creating symbolic associations between artists that signal the artist’s import and contextualization within his or her peer group. These exhibition-created associations, in turn, influence historians who must choose a small selection of artists to exemplify a historical cohort. The research tests this idea through a cohort of 125 artists’ exhibition networks in the Museum of Modern Art, New York, from 1929 to 1968 (996 exhibitions). Individual network variables, such as number and quality of connections, are examined for impact on an artist’s recognition in current art history textbooks (2012-2014). Results indicate certain connections created by exhibition have a positive effect on historical recognition, even when controlling for individual accomplishments of the artist (such as solo exhibitions). Artists connected with prestigious artists through “strong symbolic ties” (i.e., repeated exhibition) tend to garner the most historical recognition, suggesting robust associations with historical peers may signify an artist’s exemplary status within his or her cohort, and consequent “good fit” into the historical narrative.

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Al-Khafaji, Rashaa Malik Musa. "The Impact of Historical Nodes and Links of The City on Adopting Knowledge Networks Strategy." Journal of University of Babylon for Engineering Sciences 26, no. 5 (March 11, 2018): 115–34. http://dx.doi.org/10.29196/jub.v26i5.878.

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The city was formed historically as a node for human activities. Its formation depends on the resources and relations in areas of production and distribution; therefore, it was divided into special zones with different economic activities that depend on them (industrial, recreational, commercial, residential areas and other uses of land). The physical and functional features of the city reflected imposed social and digital systems. The Technological advances have expanded the role of knowledge in everyday life that established the concept of knowledge networks. This study deals with the concept of knowledge networks as an updated multidimensional strategy which imposes its ways to deal with the historic urban system as a part of the larger one, where the developed technology imposes its needs of the organizational vision of the city as part of the global system. This study hypothesis that the knowledge networks is a concept not separated from the historic space networks in cities. The study also hypothesis that these historic networks, which were formed from nodes and links impose the strategic vision to adopt and promote the knowledge networks into urban level. This study aims to clarify the concept of knowledge networks as an updated strategy for the urban growth, shows the role of nodes, and links which had grown historically in cities to adopt and promote strategies for the knowledge networks. This research adopted a method to achieve its aims, through studying the cognitive network mechanism and the role of urban characteristics as guidelines to strengthen these networks in a number of global examples, and then use the global experience to build a vision for network strategy in the Iraqi city to produce a new urban pattern. The main conclusion is that the historic urban ties act as a mutual force along with the global mechanism of knowledge network so that each city would show its special response to the network pattern.

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Dzihora, Kyryl. "Representation of historical memory in social network communities." Skhid 3, no. 1 (April 1, 2022): 15–24. http://dx.doi.org/10.21847/1728-9343.2022.3(1).254336.

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The article is devoted to the study of the representation of historical memory in social network communities. The purpose of this study is to identify current trends in some aspects of historical memory on social media. The study has been conducted on the three most popular networks in Ukraine, namely: Facebook, Instagram, YouTube. A comprehensive approach with the application of specialized software has been used for data collection. 468 communities from three social networks, the topics of which correspond to the search queries “Історія України”, “История Украины”, “History of Ukraine”, “Історія села”, “СCСР”, have been studied. The analysis of the obtained data has revealed that some groups formed a new category of educational groups. Further processing of the data has shown that groups often aim to break myths and promote “correct” history. Another category of groups is focused on the problem of preserving historical memory at the local level or the history of a particular industry. These trends demonstrate that social network communities are an indirect state of historical memory at the societal level, which, in turn, confirms Jameson’s thesis of the “New Historicism” of “installing historical attractions” and resisting theory.

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Wang, Xiaoni, Zhiwen Pan, Zhouxia Li, Wen Ji, and Feng Yang. "Adaptive information sharing approach for crowd networks based on two stage optimization." International Journal of Crowd Science 3, no. 3 (September 2, 2019): 284–302. http://dx.doi.org/10.1108/ijcs-09-2019-0020.

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Purpose This paper aims to optimize and evaluating the performance of the crowd networks through analyzing their information sharing patterns. That is, in a crowd network, the qualities of accomplishing tasks are highly dependent on the effective information sharing among intelligent subjects within the networks. Hence, proposing an adaptive information-sharing approach can help improve the performance of crowd networks on accomplishing tasks that are assigned to them. Design/methodology/approach This paper first introduces the factors that affect effectiveness of information-sharing pattern: the network topology, the resources owned by intelligent subjects and the degree of information demand. By analyzing the correlation between these factors and the performance of crowd networks, an Adaptive Information Sharing Approach for Crowd Networks (AISCN approach) is proposed. By referring to information needed for accomplishing the historical tasks that are assigned to a crowd network, the AISCN approach can explore the optimized information-sharing pattern based on the predefined composite objective function. The authors implement their approach on two crowd networks including bee colony and supply chain, to prove the effectiveness of the approach. Findings The shared information among intelligent subjects affects the efficiency of task completion in the crowd network. The factors that can be used to describe the effectiveness of information-sharing patterns include the network topology, the resources owned by intelligent subjects and the degree of information demand. The AISCN approach used heuristic algorithm to solve a composite objective function which takes all these factors into consideration, so that the optimized information-sharing pattern can be obtained. Originality/value This paper introduces a set of factors that can be used to describe the correlation between information-sharing pattern and performance of crowd network. By quantifying such correlation based on these factors, this paper proposes an adaptive information-sharing approach which can explore the optimized information-sharing pattern for a variety of crowd networks. As the approach is a data-driven approach that explores the information-sharing pattern based on the network’s performance on historical tasks and network’s characteristics, it is adaptive to the dynamic change (change of incoming tasks, change of network characteristics) of the target crowd network. To ensure the commonality of the information-sharing approach, the proposed approach is not designed for a specific optimization algorithm. In this way, during the implementation of the proposed approach, heuristic algorithms can be chosen according to the complexity of the target crowd network.

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Borodkin, Leonid, Arsenii Alanovich Gasanov, and Evgenii Vladimirovich Danilov. "Historical network analysis: to the fifth anniversary of the profile international journal." Историческая информатика, no. 2 (February 2022): 196–206. http://dx.doi.org/10.7256/2585-7797.2022.2.38447.

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This article is a review of materials published in the international journal Journal of Historical Network Research (JHNR), which has been in existence since 2017 and reflects the main approaches in the development of historical network research. This is a relatively new direction in historical science, reflecting the growing interest in the study of interactions, relationships in historical societies of various epochs and localizations. The methodological basis of historical network analysis is the methods of graph theory. A big role in these studies is given to the visualization of the results of the analysis of social networks with the help of special software actively used in the publications of the journal JHNR. Some issues of the journal are thematic – for example, reflecting the experience of using network analysis methods in the study of antiquity or the history of China. JHNR magazine is an online publication with open access, which creates a number of advantages for researchers of social networks. Thus, network illustrations are not subject to restrictions on the size or nature of the visualization. The article analyzes the experience of using network methods in the study of informal communications in the elites of Ancient Rome, relationships during coastal trade in Tudor England, network communications in the opposition community of the late GDR, as well as the network structure of the conspiracy of July 20, 1944.

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Bogomolova, Natalia. "Accuracy Appointment for Geodetic Measurements in Historical Object Reconstruction." Proceedings of Petersburg Transport University 19, no. 4 (December 20, 2022): 693–701. http://dx.doi.org/10.20295/1815-588x-2022-4-693-701.

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Purpose: To consider the issue of the assignment of marking work accuracy at reconstruction stage of a technically complex object. To show the need in accuracy preliminary calculation. After the assignment of geodetic measurement accuracy, to perform geodetic network designing in Credo.dat software package. To determine leveling class that satisfies formed requirements. Methods: Measurement error theory, least squares method, parametric adjustment method, modeling of geodetic networks using package of applied programs. Results: Requirements for the accuracy of marking works were established based on the ones of current regulatory documents. The sequence of the formation of requirements for definition of accuracy in geodetic staking base locations and requirements for the accuracy of used geodetic instruments is considered. Separately, the accuracy of planned and altitudinal geodetic networks is calculated. The possibility to apply class III leveling program when creating altitudinal network to meet accuracy established requirements is shown. Practical significance: The sequence of geodetic work accuracy assignment during historical object reconstruction is outlined on a particular example, the proposed methodology can be recommended for a practical use.

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O’Gorman, Eoin J. "Machine learning ecological networks." Science 377, no. 6609 (August 26, 2022): 918–19. http://dx.doi.org/10.1126/science.add7563.

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Lintunen, Tiina, and Kimmo Elo. "Networks of Revolutionary Workers: Socialist Red Women in Finland in 1918." International Review of Social History 64, no. 2 (July 10, 2019): 279–307. http://dx.doi.org/10.1017/s0020859019000336.

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AbstractRevolutions need people. How do these people connect with each other, and how can the revolutionary message pass from one person to another? This article aims to answer these questions by examining the revolutionaries who participated in the Finnish Civil War on the rebellious Red side in 1918. We have chosen Red women from a particular district in Finland in order to analyse their connections and the networks created by membership of the labour movement, place of residence, and kinship. In order to see the layers of those connections, we utilize historical social network analysis rooted in digital history. This allows us to observe the significance and impact of regional, social networks and improves our understanding of structural factors affecting the intra-group dynamics among these revolutionary women. Our results support the claim that historical network analysis is a suitable tool for exploring interaction patterns and social structures in the past, and to gain new insights into historical phenomena.

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Phillips, Jonathan D. "Networks of Historical Contingency in Earth Surface Systems." Journal of Geology 121, no. 1 (January 2013): 1–16. http://dx.doi.org/10.1086/668557.

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PRADO, S. D., S. R. DAHMEN, A. L. C. BAZZAN, M. MACCARRON, and J. HILLNER. "GENDERED NETWORKS AND COMMUNICABILITY IN MEDIEVAL HISTORICAL NARRATIVES." Advances in Complex Systems 23, no. 03 (May 2020): 2050006. http://dx.doi.org/10.1142/s021952592050006x.

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Since the 1970s, scholars have begun to pay attention to the presentation of women in Bede’s Ecclesiastical History of the English People, the main source for the early history of Britain (from the first century BC to the eighth century AD). Vastly different conclusions have been drawn, ranging from positivist approaches which saw the period as a golden age for women to rather more negative assessments, which argue that Bede suppressed the role of women. By analyzing the concept of communicability and relevance of certain nodes in complex networks, we show how Bede’s Ecclesiastical History affords women complex and nuanced social roles. In particular, we can show the independent importance of certain abbesses, which is a significant result and challenges much of the existing scholarship on Bede’s attitude to female power.

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Håkansson, Peter, and Caroline Tovatt. "Networks and labor market entry – a historical perspective." Labor History 58, no. 1 (November 30, 2016): 67–90. http://dx.doi.org/10.1080/0023656x.2017.1250204.

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Saleh, Ahmed I., Amr Ali-Eldin, and Amr A. Mohamed. "Historical based location management strategies for PCS networks." Wireless Networks 23, no. 6 (April 16, 2016): 1967–92. http://dx.doi.org/10.1007/s11276-016-1268-1.

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Leahy, Thomas P., Francesc Pons Llopis, Matthew D. Palmer, and Niall H. Robinson. "Using Neural Networks to Correct Historical Climate Observations." Journal of Atmospheric and Oceanic Technology 35, no. 10 (October 2018): 2053–59. http://dx.doi.org/10.1175/jtech-d-18-0012.1.

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AbstractBiases in expendable bathythermograph (XBT) instruments have emerged as a leading uncertainty in reconstructions of historical ocean heat content change and therefore climate change. Corrections for these biases depend on the type of XBT used; however, this is unspecified for 52% of the historical XBT profiles in the World Ocean Database. Here, we use profiles of known XBT type to train a neural network that can classify probe type based on three covariates: profile date, maximum recorded depth, and country of origin. Whereas previous studies have shown an average classification skill of 77%, falling below 50% for some periods, our new algorithm maintains an average skill of 90%, with a minimum of 70%. Our study illustrates the potential for successfully applying machine learning approaches in a wide variety of instrument classification problems in order to promote more homogeneous climate data records.

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Deligiannakis, Antonios, Yannis Kotidis, and Nick Roussopoulos. "Dissemination of compressed historical information in sensor networks." VLDB Journal 16, no. 4 (July 28, 2006): 439–61. http://dx.doi.org/10.1007/s00778-005-0173-5.

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Langmead, Alison, Jessica M. Otis, Christopher N. Warren, Scott B. Weingart, and Lisa D. Zilinksi. "Towards Interoperable Network Ontologies for the Digital Humanities." International Journal of Humanities and Arts Computing 10, no. 1 (March 2016): 22–35. http://dx.doi.org/10.3366/ijhac.2016.0157.

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Scholars have long been interested in networks. Networks of scholarly exchange, trade, kinship, and patronage are some of the many such longstanding subjects of study. Recent and ongoing digital humanities projects are now considering networks with fresh approaches and increasingly complex datasets. At the heart of these digital projects are ‘network ontologies’ — functional data models for distilling the complicated, messy connections between historical people, objects, and places. Although scholars creating network ontologies necessarily focus on different types of content, if these networks are to form a coherent body of scholarship in the future, we must work towards the creation interoperable ontological structures, rather than yet another set of competing standards. Here we examine the methodological considerations behind designing such interoperable ontologies, focusing primarily on the example of Early Modern historical networks. We argue that it would be infeasible to adopt a single ontological standard for all possible digital humanities projects; flexibility is essential to accommodate all subjects and objects of humanistic enquiry, from the micro-level to the longue-durée. However, we believe it possible to establish shared practices to structure these network ontologies on an ongoing basis in order to ensure their long-term interoperability.

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Lowe, James, Miguel García-Sancho, Rhodri Leng, Mark Wong, Niki Vermeulen, and Gil Viry. "Across and within Networks." Historical Studies in the Natural Sciences 52, no. 3 (June 1, 2022): 443–75. http://dx.doi.org/10.1525/hsns.2022.52.3.443.

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In this essay, we reflect on how the findings of the preceding papers enabled us to thicken the history of genomics. We have expanded the number of dimensions across which our historical work operated beyond extending the dimension of time. Building on this, we argue that the history of genomics became synchronically entangled with a range of communities, target species, and research agendas—among them yeast biochemistry, pig and human immunology, systematics, medical genetics, and agricultural genetics. We make sense of these entanglements with analytic categories to characterize modes of organizing and conducting sequencing, and the relationships between the practices of sequencing and the objectives of those collaborating around it: horizontal and vertical, proximate and distal, directed and undirected, as well as intensive and extensive sequencing. Our categories emerged as we analyzed and qualitatively interpreted datasets and co-authorship networks. Throughout this special issue, we have characterized genomics as a set of tools that open up connections between actors, institutions, experimental organisms, and historically contingent forms of research. We contend that presenting genomics in this way emphasizes the agency of the communities that mobilized the sequence data and offers a fresh perspective for addressing the medical and agricultural translation of that data. We close by proposing how we can develop our mixed-methods approach through the establishment of a domain ontology that would allow information on sequence submissions and publications to be connected to other forms of data, thus expanding the range of evidence available for historical analysis. This essay is part of a special issue entitled The Sequences and the Sequencers: A New Approach to Investigating the Emergence of Yeast, Human, and Pig Genomics, edited by Michael García-Sancho and James Lowe.

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Wasserkrug, Segev, Radu Marinescu, Sergey Zeltyn, Evgeny Shindin, and Yishai A. Feldman. "Learning the Parameters of Bayesian Networks from Uncertain Data." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 12190–97. http://dx.doi.org/10.1609/aaai.v35i13.17447.

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The creation of Bayesian networks often requires the specification of a large number of parameters, making it highly desirable to be able to learn these parameters from historical data. In many cases, such data has uncertainty associated with it, including cases in which this data comes from unstructured analysis or from sensors. When creating diagnosis networks, for example, unstructured analysis algorithms can be run on the historical text descriptions or images of previous cases so as to extract data for learning Bayesian network parameters, but such derived data has inherent uncertainty associated with it due to the nature of such algorithms. Because of the inability of current Bayesian network parameter learning algorithms to incorporate such uncertainty, common approaches either ignore this uncertainty, thus reducing the resulting accuracy, or completely disregard such data. We present an approach for learning Bayesian network parameters that explicitly incorporates such uncertainty, and which is a natural extension of the Bayesian network formalism. We present a generalization of the Expectation Maximization parameter learning algorithm that enables it to handle any historical data with likelihood-evidence-based uncertainty, as well as an empirical validation demonstrating the improved accuracy and convergence enabled by our approach. We also prove that our extended algorithm maintains the convergence and correctness properties of the original EM algorithm, while explicitly incorporating data uncertainty in the learning process.

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Postles, David. "Reviewing Social Networks: using Ucinet." History and Computing 6, no. 1 (March 1994): 1–11. http://dx.doi.org/10.3366/hac.1994.6.1.1.

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Social relationships have become an increasingly important area of historical research. The very precise (perhaps, in the context of historical data, too rigorous) manipulation performed by exponents of formal SocialNetworkAnalysis, who nowmainlycomprise sociologists, has not been received into the historian's methodology. Two ofthe possible reasons maybe that: there is a strongbeliefthathistorical data are both too complex and too incomplete to support such manipulation; and there has not been easily available and user-friendlysoftware. The most recent release of Ucinet is reviewed here to assess how far it might address the latter problem.

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NEVALAINEN, TERTTU. "Social networks and language change in Tudor and Stuart London – only connect?" English Language and Linguistics 19, no. 2 (July 2015): 269–92. http://dx.doi.org/10.1017/s136067431500009x.

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Place is an integral part of social network analysis, which reconstructs network structures and documents the network members’ linguistic practices in a community. Historical network analysis presents particular challenges in both respects. This article first discusses the kinds of data, official documents, personal letters and diaries that historians have used in reconstructing social networks and communities. These analyses could be enriched by including linguistic data and, vice versa, historical sociolinguistic findings may often be interpreted in terms of social networks.Focusing on Early Modern London, I present two case studies, the first one investigating a sixteenth-century merchant family exchange network and the second discussing the seventeenth-century naval administrator Samuel Pepys, whose role as a community broker between the City and Westminster is assessed in linguistic terms. My results show how identifying the leaders and laggers of linguistic change can add to our understanding of the varied ways in which linguistic innovations spread to and from Tudor and Stuart London both within and across social networks.

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Wang, Lili, Chenghan Huang, Ying Lu, Weicheng Ma, Ruibo Liu, and Soroush Vosoughi. "Dynamic Structural Role Node Embedding for User Modeling in Evolving Networks." ACM Transactions on Information Systems 40, no. 3 (July 31, 2022): 1–21. http://dx.doi.org/10.1145/3472955.

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Complex user behavior, especially in settings such as social media, can be organized as time-evolving networks. Through network embedding, we can extract general-purpose vector representations of these dynamic networks which allow us to analyze them without extensive feature engineering. Prior work has shown how to generate network embeddings while preserving the structural role proximity of nodes. These methods, however, cannot capture the temporal evolution of the structural identity of the nodes in dynamic networks. Other works, on the other hand, have focused on learning microscopic dynamic embeddings. Though these methods can learn node representations over dynamic networks, these representations capture the local context of nodes and do not learn the structural roles of nodes. In this article, we propose a novel method for learning structural node embeddings in discrete-time dynamic networks. Our method, called HR2vec , tracks historical topology information in dynamic networks to learn dynamic structural role embeddings. Through experiments on synthetic and real-world temporal datasets, we show that our method outperforms other well-known methods in tasks where structural equivalence and historical information both play important roles. HR2vec can be used to model dynamic user behavior in any networked setting where users can be represented as nodes. Additionally, we propose a novel method (called network fingerprinting) that uses HR2vec embeddings for modeling whole (or partial) time-evolving networks. We showcase our network fingerprinting method on synthetic and real-world networks. Specifically, we demonstrate how our method can be used for detecting foreign-backed information operations on Twitter.

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Xu, Gang, Xinyue Wang, Na Zhang, Zhifei Wang, Lin Yu, and Liqiang He. "A Routing Algorithm for the Sparse Opportunistic Networks Based on Node Intimacy." Wireless Communications and Mobile Computing 2021 (March 1, 2021): 1–9. http://dx.doi.org/10.1155/2021/6666211.

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Opportunistic networks are becoming more and more important in the Internet of Things. The opportunistic network routing algorithm is a very important algorithm, especially based on the historical encounters of the nodes. Such an algorithm can improve message delivery quality in scenarios where nodes meet regularly. At present, many kinds of opportunistic network routing algorithms based on historical message have been provided. According to the encounter information of the nodes in the last time slice, the routing algorithms predict probability that nodes will meet in the subsequent time slice. However, if opportunistic network is constructed in remote rural and pastoral areas with few nodes, there are few encounters in the network. Then, due to the inability to obtain sufficient encounter information, the existing routing algorithms cannot accurately predict whether there are encounters between nodes in subsequent time slices. For the purpose of improving the accuracy in the environment of sparse opportunistic networks, a prediction model based on nodes intimacy is proposed. And opportunistic network routing algorithm is designed. The experimental results show that the ONBTM model effectively improves the delivery quality of messages in sparse opportunistic networks and reduces network resources consumed during message delivery.

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VUKELIC, M. A., and E. N. MIRANDA. "NEURAL NETWORKS IN PETROLEUM ENGINEERING: A CASE STUDY." International Journal of Neural Systems 07, no. 02 (May 1996): 187–94. http://dx.doi.org/10.1142/s0129065796000154.

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A multilayer neural network has been used for deciding which oil reservoir layer has to be perforated. Many network architectures were tested until we found those with the best generalization capability. The network performs better than human experts and its achievements are higher than the historical average in the test area. As in other applications of neural networks, the learning capability improves with more hidden neurons but the generalization does not.

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Brosens, Koenraad, Klara Alen, Astrid Slegten, and Fred Truyen. "MapTap and Cornelia Slow Digital Art History and Formal Art Historical Social Network Research." Zeitschrift für Kunstgeschichte 79, no. 3 (December 30, 2016): 315–30. http://dx.doi.org/10.1515/zkg-2016-0025.

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Abstract The essay introduces MapTap, a research project that zooms in on the ever-changing social networks underpinning Flemish tapestry (1620 – 1720). MapTap develops the young and still slightly amorphous field of Formal Art Historical Social Network Research (FAHSNR) and is fueled by Cornelia, a custom-made database. Cornelia’s unique data model allows researchers to organize attribution and relational data from a wide array of sources in such a way that the complex multiplex and multimode networks emerging from the data can be transformed into partial unimode networks that enable proper FAHSNR. A case study revealing the key roles played by women in the tapestry landscape shows how this kind of slow digital art history can further our understanding of early modern creative communities and industries.

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CHROBOK, ROLAND, ANDREAS POTTMEIER, SIGURDUR F. HAFSTEIN, and MICHAEL SCHRECKENBERG. "TRAFFIC FORECAST IN LARGE SCALE FREEWAY NETWORKS." International Journal of Bifurcation and Chaos 14, no. 06 (June 2004): 1995–2004. http://dx.doi.org/10.1142/s0218127404010412.

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Traffic flow in large and complex freeway networks is a highly nonlinear phenomenon, which makes traffic forecast a difficult task. In this article an approach to traffic forecast is presented, which uses a micro-simulator for traffic flow combined with current and historical traffic data. The micro-simulator and locally measured current traffic data are used to reconstruct the current network-wide traffic state. Then, this state is combined with historical traffic data to forecast the traffic development. This framework is applied to the freeway network of the German state North Rhine-Westphalia. The micro-simulator uses an advanced cellular-automaton model for traffic flow, and the data are supplied from more than 4,000 locally installed loop-detectors, which deliver information on the (local) traffic state online minute by minute.

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Meeks, Elijah. "Modeling Transportation in the Roman World: Implications for World Systems." Leonardo 46, no. 3 (June 2013): 278. http://dx.doi.org/10.1162/leon_a_00574.

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ORBIS is a geospatial transportation network model of the Roman world, simulating historical travel patterns by modeling the major roads, rivers, and sea routes active during the Roman Empire. With such a model, historians can more accurately examine not only individual route patterns, but also emergent structures of the network as a whole. By defining traditional world systems networks as a particular movement profile for application on a geospatial transportation network, we can begin to see regions of the network using community analysis and analyze those regions for historical patterns.

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Hellwig, Oliver. "Dating Sanskrit texts using linguistic features and neural networks." Indogermanische Forschungen 124, no. 1 (September 18, 2019): 1–46. http://dx.doi.org/10.1515/if-2019-0001.

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Abstract Deriving historical dates or datable stratifications for texts in Classical Sanskrit, such as the epics Mahābhārata and Rāmāyaṇa, is a considerable challenge for text-historical research. This paper provides empirical evidence for subtle but noticeable diachronic changes in the fundamental linguistic structures of Classical Sanskrit, and argues that Classical Sanskrit shows enough diachronic variation for dating texts on the basis of linguistic developments. Building on this evidence, it evaluates machine learning algorithms that predict approximate dates of composition for Sanskrit texts. The paper introduces the required background, discusses the relevance of linguistic features for temporal classification, and presents a text-historical evaluation of Book 6 of the Mahābhārata, whose historical stratification is disputed in Indological research.

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Ferro, S., M. Pelillo, and A. Traviglia. "AI-ASSISTED DIGITALISATION OF HISTORICAL DOCUMENTS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-2-2023 (June 24, 2023): 557–62. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-2-2023-557-2023.

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Abstract. Preserving historical archival heritage involves not only physical measures to safeguard these valuable texts but also providing for their digital preservation. However, merely digitising manuscripts and codexes is not enough. A further step is needed: the digitalisation of their content, i.e. the verbatim transcription of scanned texts. This process enables the accurate preservation of their textual content, making it easier to search for information and conduct further analyses. With the help of artificial intelligence, particularly Deep Neural Networks (DNNs), automatic handwriting recognition can be performed. In this study, we employed a Convolutional Recurrent Neural Network (CRNN), an established type of DNN, to determine the minimum amount of labelled data required to automatically transcribe five different historical datasets that vary in language and time period. The results show that a Character Error Rate (CER) lower than 10% can be achieved with just a few hundred labelled text lines in almost all cases.

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Koistinen, David, and Kenneth Lipartito. "Offshoring, outsourcing and global production networks in historical context." Entreprises et histoire 94, no. 1 (2019): 62. http://dx.doi.org/10.3917/eh.094.0062.

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Furfaro, Filippo, Giuseppe M. Mazzeo, and Andrea Pugliese. "Managing Multidimensional Historical Aggregate Data in Unstructured P2P Networks." IEEE Transactions on Knowledge and Data Engineering 22, no. 9 (September 2010): 1313–30. http://dx.doi.org/10.1109/tkde.2009.160.

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Gavin, Michael. "Historical Text Networks: The Sociology of Early English Criticism." Eighteenth-Century Studies 50, no. 1 (2016): 53–80. http://dx.doi.org/10.1353/ecs.2016.0041.

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Shi, Chong-Xiao, and Guang-Hong Yang. "Distributed Learning Over Networks: Effect of Using Historical Observations." IEEE Transactions on Automatic Control 65, no. 12 (December 2020): 5503–9. http://dx.doi.org/10.1109/tac.2020.2973583.

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Büchler, Marco, Gregory Crane, and Gerhard Heyer. "Historical Relevance Feedback Detection by Text Re-use Networks." Leonardo 46, no. 3 (June 2013): 276. http://dx.doi.org/10.1162/leon_a_00572.

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Text re-use has been in the humanist's interest for centuries. Collecting parallel texts implies giving a certain information, e.g. a moral statement or report on wars and conflicts, a kind of witness. The more independent parallel texts are collected, the more feasible the information is. The contribution reported here is on automatic detection of text re-use and the usage of a text re-use network to derive a Cultural Heritage Aware PageRank technique given ancient text re-uses like quotations, paraphrases, and allusions.

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Schlote, K. H. "Symmetrie Gruppe Dualität. Science Networks – Historical Studies Bd. 1." Zeitschrift für Kristallographie 194, no. 1-2 (January 1991): 154–56. http://dx.doi.org/10.1524/zkri.1991.194.1-2.154.

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Bowern, Claire. "Historical linguistics in Australia: trees, networks and their implications." Philosophical Transactions of the Royal Society B: Biological Sciences 365, no. 1559 (December 12, 2010): 3845–54. http://dx.doi.org/10.1098/rstb.2010.0013.

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This paper presents an overview of the current state of historical linguistics in Australian languages. Australian languages have been important in theoretical debates about the nature of language change and the possibilities for reconstruction and classification in areas of intensive diffusion. Here are summarized the most important outstanding questions for Australian linguistic prehistory; I also present a case study of the Karnic subgroup of Pama–Nyungan, which illustrates the problems for classification in Australian languages and potential approaches using phylogenetic methods.

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Malm, Annika, Olle Ljunggren, Olof Bergstedt, Thomas J. R. Pettersson, and Gregory M. Morrison. "Replacement predictions for drinking water networks through historical data." Water Research 46, no. 7 (May 2012): 2149–58. http://dx.doi.org/10.1016/j.watres.2012.01.036.

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Gao, Huiji, Jiliang Tang, and Huan Liu. "Exploring Social-Historical Ties on Location-Based Social Networks." Proceedings of the International AAAI Conference on Web and Social Media 6, no. 1 (August 3, 2021): 114–21. http://dx.doi.org/10.1609/icwsm.v6i1.14240.

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Location-based social networks (LBSNs) have become a popular form of social media in recent years. They provide location related services that allow users to "check-in'' at geographical locations and share such experiences with their friends. Millions of "check-in'' records in LBSNs contain rich information of social and geographical context and provide a unique opportunity for researchers to study user's social behavior from a spatial-temporal aspect, which in turn enables a variety of services including place advertisement, traffic forecasting, and disaster relief. In this paper, we propose a social-historical model to explore user's check-in behavior on LBSNs. Our model integrates the social and historical effects and assesses the role of social correlation in user's check-in behavior. In particular, our model captures the property of user's check-in history in forms of power-law distribution and short-term effect, and helps in explaining user's check-in behavior. The experimental results on a real world LBSN demonstrate that our approach properly models user's check-ins and shows how social and historical ties can help location prediction.

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Beecher, Janice A. "Naruc as Network: A Perspective on the U.S. Regulatory Policy Community." Competition and Regulation in Network Industries 13, no. 4 (December 2012): 362–88. http://dx.doi.org/10.1177/178359171201300403.

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While much has been written about network industries, somewhat less attention has been paid to the policy networks among regulators themselves. Networks serve the collective and individual interests of members through professional socialization, capacity development, and institutionalization. Regulatory networks are particularly valuable if they help address information asymmetry, an inherent strategic advantage of regulated firms. Given potential tradeoffs associated with networking, regulatory networks must be vigilant about protecting institutional integrity. The history and culture of U.S. regulation are inextricably tied to the National Association of Regulatory Utility Commissioners (NARUC). This paper presents an historical and cultural account of ‘NARUC as network’ and offers a perspective for thinking about regulatory policy networks generally.

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Journal articles on the topic 'Historical networks'

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