Relevant bibliographies by topics / Citation’s network

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Citation’s network'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Citation’s network"

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Chakraborty, Manajit, Maksym Byshkin, and Fabio Crestani. "Patent citation network analysis: A perspective from descriptive statistics and ERGMs." PLOS ONE 15, no. 12 (2020): e0241797. http://dx.doi.org/10.1371/journal.pone.0241797.

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Patent Citation Analysis has been gaining considerable traction over the past few decades. In this paper, we collect extensive information on patents and citations and provide a perspective of citation network analysis of patents from a statistical viewpoint. We identify and analyze the most cited patents, the most innovative and the highly cited companies along with the structural properties of the network by providing in-depth descriptive analysis. Furthermore, we employ Exponential Random Graph Models (ERGMs) to analyze the citation networks. ERGMs enables understanding the social perspecti
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Vella, Supradeepa. "Predictions of Citations of a Scholarly Paper." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 1735–38. http://dx.doi.org/10.22214/ijraset.2021.37657.

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Abstract: Bibliometrics is a statistical analysis of written publications such as books or articles. A bibliographic citationis a reference to a book, article, web page, or other published item. Thus citations are useful for identifying the progress ofthe particular work and measuring the quality of the research article. The cited papers are downloaded using the crawler. Fromthe downloaded article, identify article relation by analyzing the citation context of the article. So first extract the citation context from the article. Citation context are classifies based on cue phrases of Simon tufe
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Bu, Yi, Yong Huang, and Wei Lu. "Loops in publication citation networks." Journal of Information Science 46, no. 6 (2019): 837–48. http://dx.doi.org/10.1177/0165551519871826.

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Traditionally, publication citation networks are regarded as acyclic, that is, no loops in the network as an earlier published article cannot cite a later published article. However, due to the accessibility of pre-print versions of articles, there might be some loops in a publication citation network. This article presents a descriptive statistic on loops in publication citation networks of computer science and physics by employing a network-based indicator, namely, strongly connected component (SCC). By employing computer science and physics disciplines publications from the Web of Science d
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Du, San Shan, and Yue Chun Wu. "Research Paper Influence Measurement and Applications: A Machine-Learning-Based Approach." Advanced Materials Research 1049-1050 (October 2014): 2073–78. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.2073.

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Measuring the influence of academic research publication is an meaningful work in academe. In this paper, the co-author and the citation networks are built to calculate the influence of a researcher and a paper in the way of networks separately with the discussion of further applications. At the beginning, the co-author network is built to determine the influence of co-authors. Then, based on the citations among the papers in the database, we build up the citation network with the help of graph theory. Thirdly, the method is implemented with the application of American Airline network analysis
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Wen, Fangfang. "Study on the research evolution of Nobel laureates 2018 based on self-citation network." Journal of Documentation 75, no. 6 (2019): 1416–31. http://dx.doi.org/10.1108/jd-02-2019-0027.

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Purpose Science is a continuum of experiences consisting of authors and their publications, and the authors’ experience is an integral part of their work that gets reflected through self-citations. Thus, self-citations can be employed in measuring the relevance between publications and tracking the evolution of research. The paper aims to discuss this issue. Design/methodology/approach Based on the bibliographic data obtained from Scopus, this study constructs and visualizes the self-citation networks of ten Nobel laureates 2018, in the fields of Physiology or Medicine, Physics, Chemistry and
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Souma, Wataru, Irena Vodenska, and Lou Chitkushev. "Classification of Paper Values Based on Citation Rank and PageRank." Journal of Data and Information Science 5, no. 3 (2020): 57–70. http://dx.doi.org/10.2478/jdis-2020-0031.

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AbstractPurposeThe number of citations has been widely used to measure the significance of a paper. However, there is a need in introducing another index to determine superiority or inferiority of papers with the same number of citations. We determine superiority or inferiority of papers by using the ranking based on the number of citations and PageRank.Design/methodology/approachWe show the positive linear correlation between Citation Rank (the ranking of the number of citation) and PageRank. On this basis, we identify high-quality, prestige, emerging, and popular papers.FindingsWe found that
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Pichardo-Corpus, Juan A., J. Guillermo Contreras, and José A. de la Peña. "Parametric definition of the influence of a paper in a citation network using communicability functions." Journal of Complex Networks 7, no. 4 (2019): 623–40. http://dx.doi.org/10.1093/comnet/cny037.

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Abstract Communicability functions quantify the flow of information between two nodes of a network. In this work, we use them to explore the concept of the influence of a paper in a citation network. These functions depend on a parameter. By varying the parameter in a continuous way we explore different definitions of influence. We study six citation networks, three from physics and three from computer science. As a benchmark, we compare our results against two frequently used measures: the number of citations of a paper and the PageRank algorithm. We show that the ranking of the articles in a
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de Vries, Y. A., A. M. Roest, M. Franzen, M. R. Munafò, and J. A. Bastiaansen. "Citation bias and selective focus on positive findings in the literature on the serotonin transporter gene (5-HTTLPR), life stress and depression." Psychological Medicine 46, no. 14 (2016): 2971–79. http://dx.doi.org/10.1017/s0033291716000805.

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BackgroundCaspi et al.'s 2003 report that 5-HTTLPR genotype moderates the influence of life stress on depression has been highly influential but remains contentious. We examined whether the evidence base for the 5-HTTLPR–stress interaction has been distorted by citation bias and a selective focus on positive findings.MethodA total of 73 primary studies were coded for study outcomes and focus on positive findings in the abstract. Citation rates were compared between studies with positive and negative results, both within this network of primary studies and in Web of Science. In addition, the im
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WANG, MINGYANG, GUANG YU, and DAREN YU. "THE PREFERENTIAL ATTACHMENT MECHANISM BASING ON WEIGHTED PAST CITATIONS." International Journal of Modern Physics B 25, no. 15 (2011): 2055–61. http://dx.doi.org/10.1142/s0217979211100424.

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In this paper, we investigate the influence of past citations ki on papers' attachment rate Π(k, t) and propose a method to consider these influences by assigning different weights to ki. The correlation r between Π(k, t) and ki decrease rapidly with time, which suggests the different influences of past citations on the preferential attachment in citation networks. According to the different influences of ki on Π(k, t), we put different weights to ki and find a good linear dependence between Π(k, t) and the weighted past citations in three actual citation networks.
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Zhao, Dangzhi, Alicia Cappello, and Lucinda Johnston. "Functions of Uni- and Multi-citations: Implications for Weighted Citation Analysis." Journal of Data and Information Science 2, no. 1 (2017): 51–69. http://dx.doi.org/10.1515/jdis-2017-0003.

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AbstractPurpose(1) To test basic assumptions underlying frequency-weighted citation analysis: (a) Uni-citations correspond to citations that are nonessential to the citing papers; (b) The influence of a cited paper on the citing paper increases with the frequency with which it is cited in the citing paper. (2) To explore the degree to which citation location may be used to help identify nonessential citations.Design/methodology/approachEach of the in-text citations in all research articles published in Issue 1 of the Journal of the Association for Information Science and Technology (JASIST) 20
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Dissertations / Theses on the topic "Citation’s network"

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Benkoussas, Chahinez. "Approches non supervisées pour la recommandation de lectures et la mise en relation automatique de contenus au sein d'une bibliothèque numérique." Thesis, Aix-Marseille, 2016. http://www.theses.fr/2016AIXM4379/document.

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Cette thèse s’inscrit dans le domaine de la recherche d’information (RI) et la recommandation de lecture. Elle a pour objets :— La création de nouvelles approches de recherche de documents utilisant des techniques de combinaison de résultats, d’agrégation de données sociales et de reformulation de requêtes ;— La création d’une approche de recommandation utilisant des méthodes de RI et les graphes entre les documents. Deux collections de documents ont été utilisées. Une collection qui provient de l’évaluation CLEF (tâche Social Book Search - SBS) et la deuxième issue du domaine des sciences hum
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Alshareef, Abdulrhman M. "Academic Recommendation System Based on the Similarity Learning of the Citation Network Using Citation Impact." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39111.

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In today's significant and rapidly increasing amount of scientific publications, exploring recent studies in a given research area and building an effective scientific collaboration has become more challenging than any time before. Scientific production growth has been increasing the difficulties for identifying the most relevant papers to cite or to find an appropriate conference or journal to submit a paper to publish. As a result, authors and publishers rely on different analytical approaches in order to measure the relationship among the citation network. Different parameters have been use
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Leifeld, Philip. "Policy networks a citation analysis of the quantitative literature /." [S.l. : s.n.], 2007. http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-26631.

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Dunaiski, Marcel Paul. "Analysing ranking algorithms and publication trends on scholarly citation networks." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/96106.

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Thesis (MSc)--Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: Citation analysis is an important tool in the academic community. It can aid universities, funding bodies, and individual researchers to evaluate scientific work and direct resources appropriately. With the rapid growth of the scientific enterprise and the increase of online libraries that include citation analysis tools, the need for a systematic evaluation of these tools becomes more important. The research presented in this study deals with scientific research output, i.e., articles and citations, and how they can be us
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Alfraidi, Hanadi Humoud A. "Interactive System for Scientific Publication Visualization and Similarity Measurement based on Citation Network." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33135.

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Online scientific publications are becoming more and more popular. The number of publications we can access almost instantaneously is rapidly increasing. This makes it more challenging for researchers to pursue a topic, review literature, track research history or follow research trends. Using online resources such as search engines and digital libraries is helpful to find scientific publications, however most of the time the user ends up with an overwhelming amount of linear results to go through. This thesis proposes an alternative system, which takes advantage of citation/reference relation
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Maier, Gunther, Alexander Kaufmann, and Michael Vyborny. "Is regional science a scientific discipline? Answers from a citation based Social Network Analysis." Institut für Regional- und Umweltwirtschaft, WU Vienna University of Economics and Business, 2008. http://epub.wu.ac.at/1226/1/document.pdf.

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From its very beginnings, regional science has been open to intellectual exchange with many other scientific disciplines. This has led to cross-fertilization, but also to problems concerning the intellectual identity of regional science. After half a century of history of the field, it is time to ask the question, whether or not regional science has developed into a scientific discipline in these decades. In this paper we use cross-citation data between 464 journals in different disciplines to answer this question. With this data set we attempt to find out, how strongly regional science journa
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Glötzl, Florentin, and Ernest Aigner. "Orthodox Core-Heterodox Periphery? Contrasting Citation Networks of Economics Departments in Vienna." Taylor & Francis, 2018. http://epub.wu.ac.at/6631/1/09538259.2018.pdf.

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The notion of an "orthodox core-heterodox periphery" structure and the extent of interdisciplinary links have been widely discussed, and partially investigated bibliometrically, within economic discourse. We extend this research by applying tools from social network analysis to citation data of three economics departments located in Vienna, two mainstream and one non-mainstream, to assess their relative citation patterns. We show that both mainstream economics departments follow the asserted core-periphery pattern and have a mono-disciplinary research focus, while the citation network of the n
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Runelöv, Martin. "Finding seminal scientific publications with graph mining." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-172382.

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We investigate the applicability of network analysis to the problem of finding seminal publications in scientific publishing. In particular, we focus on the network measures betweenness centrality, the so-called backbone graph, and the burstiness of citations. The metrics are evaluated using precision-related scores with respect to gold standards based on fellow programmes and manual annotation. Citation counts, PageRank, and random selection are used as baselines. We find that the backbone graph provides us with a way to possibly discover seminal publications with low citation count, and comb
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Meiklejohn, Luke S. "How to attribute credit if you must." Master's thesis, Faculty of Commerce, 2021. http://hdl.handle.net/11427/33802.

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Data ownership is of fundamental importance in the digital economy of today. Commercializing academic research, whilst maintaining ownership of it, is a task that can now be accomplished due to the strengths of blockchain technology, which allows data to be registered, made unique, and traced to its origins. We propose a blockchain use-case for licencing academic research, based off an academic project named UniCoin. In this thesis, we discuss how to fairly attribute credit between all sources of knowledge that contribute to new pieces of academic research, using citation network analysis and
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Mead, Susan Virginia. "Identifying academic subcultures within higher education research : an examination of scholars' careers through author cocitation /." Diss., This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-10242005-124106/.

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Books on the topic "Citation’s network"

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Golosovsky, Michael. Citation Analysis and Dynamics of Citation Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28169-4.

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Harnack, Andrew. Online!: Citation styles. Bedford/St. Martin's, 2000.

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Hacker, Diana. Research and documentation online. Bedford/St. Martin's, 1998.

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Kyle, Noeline. Citing historical sources: A manual for family historians. Unlock the Past, 2013.

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Brian, Kelly. iSearch: Psychology. Allyn and Bacon, 2003.

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La recherche en fiscalité canadienne. 3rd ed. Carswell, 2011.

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B, Barnes Susan, and Barr Linda R, eds. Web research: Selecting, evaluating, and citing. Pearson/Allyn & Bacon, 2006.

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Radford, Marie L. Web research: Selecting, evaluating, and citing. Allyn and Bacon, 2002.

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Newman, Mark. Networks of information. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0003.

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A discussion of information networks and their measurement. The world wide web is discussed at length, including HTML, HTTP, and the use of crawlers to measure network structure. Citation networks are also discussed in some detail, including their history, structure, and statistics, and the use of databases of citation records to construct networks. Other networks discussed include peer-to-peer networks, recommender networks, and keyword indexes.
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Golosovsky, Michael. Citation Analysis and Dynamics of Citation Networks. Springer, 2019.

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Book chapters on the topic "Citation’s network"

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Radicchi, Filippo, Santo Fortunato, and Alessandro Vespignani. "Citation Networks." In Understanding Complex Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23068-4_7.

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Divakarmurthy, Pramod, and Ronaldo Menezes. "The Effect of Citations to Collaboration Networks." In Complex Networks. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30287-9_19.

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Csárdi, Gábor. "Dynamics of Citation Networks." In Artificial Neural Networks – ICANN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840817_73.

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Peroni, Silvio, David Shotton, and Fabio Vitali. "Building Citation Networks with SPACIN." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58694-6_23.

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Renoust, Benjamin, Vivek Claver, and Jean-François Baffier. "Flows of Knowledge in Citation Networks." In Studies in Computational Intelligence. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50901-3_13.

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Kralj, Jan, Anita Valmarska, Marko Robnik-Šikonja, and Nada Lavrač. "Mining Text Enriched Heterogeneous Citation Networks." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18038-0_52.

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Beltz, Hayley, Timothy Rutledge, Raoul R. Wadhwa, et al. "Ranking Algorithms: Application for Patent Citation Network." In Information Fusion and Data Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-03643-0_21.

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Lee, Moosung, and Tom Friedrich. "Citation Network Analysis of Comparative Education Texts." In Beyond the Comparative. SensePublishers, 2011. http://dx.doi.org/10.1007/978-94-6091-722-6_7.

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Mathur, Sandeep, and Loveleen Gaur. "Predictability, Power and Procedures of Citation Analysis." In Lecture Notes in Networks and Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9689-6_6.

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Fushimi, Takayasu, Tetsuji Satoh, and Noriko Kando. "Dynamic Visualization of Citation Networks and Detection of Influential Node Addition." In Complex Networks IX. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73198-8_25.

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Conference papers on the topic "Citation’s network"

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Ji, Taoran, Zhiqian Chen, Nathan Self, Kaiqun Fu, Chang-Tien Lu, and Naren Ramakrishnan. "Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/364.

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Modeling and forecasting forward citations to a patent is a central task for the discovery of emerging technologies and for measuring the pulse of inventive progress. Conventional methods for forecasting these forward citations cast the problem as analysis of temporal point processes which rely on the conditional intensity of previously received citations. Recent approaches model the conditional intensity as a chain of recurrent neural networks to capture memory dependency in hopes of reducing the restrictions of the parametric form of the intensity function. For the problem of patent citation
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Soares, Rafael Henrique Santos, Jorge H. C. Fernandes, and Ricardo Sampaio. "Formal Information Flows Among Top Authorities of the Brazilian Federal Government based on Co-word Analysis of Data Published in the Official Gazette." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/brasnam.2016.6450.

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This paper describes a methodology for modeling social networks of formal communications among top authorities of the Brazilian Federal Government grounded on data available in the government official gazette (Diário Oficial da União). The text of a large number of official publications such as presidential decrees, ministerial orders and authority nominations was analyzed for identification of citations to organizations and persons. The co-occurrence of names of persons in such publications created a network of relations among such persons. An ego-network was built around the president Dilma
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Guo, Tinghao, Jiarui Xu, Yue Sun, Yilin Dong, Neal E. Davis, and James T. Allison. "Network Analysis of Design Automation Literature." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67361.

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In this paper we present a study of citation and co-authorship networks for articles from the ASME Design Automation Conference (DAC) during the years 2002–2015. We identify key authors, show that the co-authorship network exhibits the small world network property, and reveal other insights from network structure. Results from two topic modeling methods are presented. A frequency-based model was developed to explore DAC topic distribution and evolution. Citation analysis was also conducted for each core topic. A correlation matrix and association rule mining were used to discover topic relatio
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Zhang, Zhen, Hongxia Yang, Jiajun Bu, et al. "ANRL: Attributed Network Representation Learning via Deep Neural Networks." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/438.

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Network representation learning (RL) aims to transform the nodes in a network into low-dimensional vector spaces while preserving the inherent properties of the network. Though network RL has been intensively studied, most existing works focus on either network structure or node attribute information. In this paper, we propose a novel framework, named ANRL, to incorporate both the network structure and node attribute information in a principled way. Specifically, we propose a neighbor enhancement autoencoder to model the node attribute information, which reconstructs its target neighbors inste
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Zhang, Daokun, Jie Yin, Xingquan Zhu, and Chengqi Zhang. "User Profile Preserving Social Network Embedding." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/472.

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This paper addresses social network embedding, which aims to embed social network nodes, including user profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore user-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, user profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preservin
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Wu, Hao, and Kristina Lerman. "Deep Context: A Neural Language Model for Large-scale Networked Documents." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/431.

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We propose a scalable neural language model that leverages the links between documents to learn the deep context of documents. Our model, Deep Context Vector, takes advantage of distributed representations to exploit the word order in document sentences, as well as the semantic connections among linked documents in a document network. We evaluate our model on large-scale data collections that include Wikipedia pages, and scientific and legal citations networks. We demonstrate its effectiveness and efficiency on document classification and link prediction tasks.
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Cheng, Dawei, Yi Tu, Zhenwei Ma, Zhibin Niu, and Liqing Zhang. "Risk Assessment for Networked-guarantee Loans Using High-order Graph Attention Representation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/807.

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Assessing and predicting the default risk of networked-guarantee loans is critical for the commercial banks and financial regulatory authorities. The guarantee relationships between the loan companies are usually modeled as directed networks. Learning the informative low-dimensional representation of the networks is important for the default risk prediction of loan companies, even for the assessment of systematic financial risk level. In this paper, we propose a high-order graph attention representation method (HGAR) to learn the embedding of guarantee networks. Because this financial network
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"Citation information." In Networks (DSN). IEEE, 2010. http://dx.doi.org/10.1109/dsn.2010.5544401.

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"Citation information." In Networks (DSN). IEEE, 2011. http://dx.doi.org/10.1109/dsn.2011.5958199.

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Jiang, Xiaodong, Pengsheng Ji, and Sheng Li. "CensNet: Convolution with Edge-Node Switching in Graph Neural Networks." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/369.

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In this paper, we present CensNet, Convolution with Edge-Node Switching graph neural network, for semi-supervised classification and regression in graph-structured data with both node and edge features. CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space. By using line graph of the original undirected graph, the role of nodes and edges are switched, and two novel graph convolution operations are proposed for feature propagation. Experimental results on real-world academic citation networks and quantum chemistry graphs show that our approa
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Reports on the topic "Citation’s network"

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McLane, V. Citation guidelines for nuclear data retrieved from databases resident at the Nuclear Data Centers Network. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/380333.

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Hilbrecht, Margo, David Baxter, Alexander V. Graham, and Maha Sohail. Research Expertise and the Framework of Harms: Social Network Analysis, Phase One. GREO, 2020. http://dx.doi.org/10.33684/2020.006.

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In 2019, the Gambling Commission announced a National Strategy to Reduce Gambling Harms. Underlying the strategy is the Framework of Harms, outlined in Measuring gambling-related harms: A framework for action. "The Framework" adopts a public health approach to address gambling-related harm in Great Britain across multiple levels of measurement. It comprises three primary factors and nine related subfactors. To advance the National Strategy, all componentsneed to be supported by a strong evidence base. This report examines existing research expertise relevant to the Framework amongacademics bas
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