Citing for High Impact

The question of citation behavior has always intrigued scientists from various disciplines. While general citation patterns have been widely studied in the literature we develop the notion of citation projection graphs by investigating the citations among the publications that a given paper cites. We investigate how patterns of citations vary between various scientific disciplines and how such patterns reflect the scientific impact of the paper. We find that idiosyncratic citation patterns are characteristic for low impact papers; while narrow, discipline-focused citation patterns are common for medium impact papers. Our results show that crossing-community, or bridging citation patters are high risk and high reward since such patterns are characteristic for both low and high impact papers. Last, we observe that recently citation networks are trending toward more bridging and interdisciplinary forms.Comment: 10 pages, 6 figures, 1 tabl

Adoção, citação e difusão do artigo científico: o que é que se difunde?

Il est suggéré que la diffusion d'articles scientifiques peuve être étudié en termes de relations qui ont lieu entre une classe de comportements associés à la citation de l'article et les médias sociaux qui stimulent cette classe verbalement. Cette suggestion suppose que la diffusion ne concerne pas l'article, mais les relations entre la classe spécifique et de leurs antécédents et des conséquents verbales dans les médias sociaux pertinents à ceux qui font la citation. Le rôle des relations interpersonnelles dans le contrôle de la diffusion d'articles scientifiques par les règles et les réseaux d'information est mise en surbrillance. Il est soutenu que un cadre qui conceptualise la diffusion d'articles scientifiques dans les processus comportementaux de l'apprentissage social, l'induction et le contrôle du stimulus peut favoriser la recherche du thème non seulement par les psychologues du comportement et les spécialistes de l'information, mais aussi par les chercheurs et les professionnels d'autres secteurs. em frances.It is suggested that the diffusion of scientific articles can be studied in terms of relationships that take place between a class of behaviours associated with the article citation and the social media which stimulate this class verbally. This suggestion assumes that the diffusion does not concern the article, but the relationships between this specific class and their verbal antecedents and consequents in the social media pertinent to those making the citation. The role of interpersonal relationships in controlling the diffusion of scientific articles by rules and information networks is highlighted. It is argued that a framework which conceptualizes the diffusion of scientific papers as processes such as social learning, induction and stimulus control may favour the research of the theme not only by behavioural psychologists and information scientists, but also by researchers and professionals from other areas.Sugere-se neste texto que a difusão do artigo científico pode ser estudada em termos das relações que se processam entre uma classe comportamental associada à citação do artigo e os meios sociais que estimulam essa classe verbalmente. Tal sugestão presume que a difusão não concerne ao artigo, mas às relações entre a classe específica e os seus antecedentes e consequentes verbais, nos meios sociais pertinentes a quem faz a citação. É realçado o papel das relações interpessoais no controle da difusão de artigos científicos por meio de regras e redes de informação. Argumenta-se que o enquadramento conceitual da difusão do artigo científico nos processos comportamentais de aprendizagem social, indução e controle de estímulos pode favorecer o estudo do tema não só por psicólogos do comportamento e cientistas da informação, mas também por pesquisadores e profissionais de outras áreas.Se sugiere que la difusión de artículos científicos puede ser estudiada en términos de las relaciones que tienen lugar entre una clase de conductas asociadas a la citación del artículo y los medios de comunicación social que estimulan esta clase verbalmente. Esa propuesta supone que la difusión no se refiere al artículo, pero a las relaciones entre la clase específica y sus antecedentes y consecuentes verbales en los medios de comunicación social pertinentes a quien hace la citación. Se resalta el papel de las relaciones interpersonales en el control de la difusión de artículos científicos por las normas y redes de información. Se argumenta que un marco que concibe la difusión de artículos científicos como procesos comportamentales de aprendizaje social, inducción y control de estímulos puede favorecer la investigación del tema no sólo por los psicólogos del comportamiento y cientistas de la información, sino también por los investigadores y profesionales de otras áreas

Effects of Homophily on Citation Patterns in Scientific Communities

Indiviidid kipuvad looma sidemeid pigem nende isikutega, kellega neil on sarnaseid huvisid või muud ühist. Bibliomeetria kontekstis avaldub sama põhimõte viitamise mustrites - artiklites viidatakse muuhulgas ka autorite endi ning nende koostööpartnerite artiklitele. Eelneva valguses on käesoleva töö eesmärgiks projekteerida bibliomeetrilised indikaatorid, mis võimaldaks meil arvestada sarnasuse mõju teadlaste ning teadusasutuste hindamisel. Konkreetsemalt defineeritakse käesolevas töös sarnasuse alusel kärbitud ja kaalutud versioonid viidete arvu meetrikale. On ilmselge, et sarnasust arvestavad meetrikad annavad kõrgema hinnangu kogukondadele, kus on tava viidata eelkõige teiste kogukondade autorite artiklitele. Samuti kirjeldatakse ning analüüsitakse antud töös viitamise mustreid, mis tuvastati kirjeldatud meetrikate rakendamisel viidete võrgule kolme erineva teadlaste kogukonna puhul.Individuals tend to establish ties in higher rate with individuals that exhibit some kind of affinity than with dissimilar ones. This principle, referred to as homophily, has a important impact in the shape of the social interactions, i.e., topologies of the underlying social networks. In the context of bibliometrics, the effects of homophily can be observed in patterns of citation: references often include a non-negligeable number of self-citations and citations from close collaborators. In light of the above, we aim at designing bibliometric indicators that allow us to modulate the effect of homophily in the ranking induced by metrics. Clearly, homophily-aware metrics would favor communities where citations involve a broader participation. In this thesis, we present homophily-trimmed and homophily-weighted versions of the citation count and report on the patterns of citation uncovered by such metrics over the citation network for three different communities

Characteristics of Knowledge Cooperation Network in a Design-driven Domain: A Social Network Analysis

The research is financed by: Social Science Funding Project of Jilin Province (No. 2017B141), China Postdoctoral Science Foundation (No. 2016M590251), China Postdoctoral Science Special Fund (No.2018T110242). Abstract The field of scientific research is currently moving from an individual and single-discipline to a more cooperative discipline that combines various researchers and their capabilities. This study uses network analysis to explore the current situation and development trend characteristics of knowledge cooperation in the design field of Decoration. We construct large-scale networks using empirical data of sampled coauthored papers from 2008 to 2016. The main aims of this paper are: (a) to disclose different patterns of networking relationships among coauthored research works in the journal of Decoration and (b), to understand the mutual interaction of knowledge cooperation across regions and units in China’s field of design. The study found that the depth of knowledge cooperation in the field of design in China needed to be improved, the knowledge cooperation network also had a small-world effect, and the network community gradually emerged. In addition, the Chinese design field had made major advances in international cooperation, cross-regional cooperation, and diversification in the forms of research works. These findings could be used to recognize interdisciplinary and intra-disciplinary networks where research collaboration is supported and encouraged. However, there were still problems such as imbalanced levels of knowledge output among the groups. Keywords: design field; knowledge cooperation; social network analysis; decoration; small-world effec

Social network analysis of scientific articles published by food policy

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Unsupervised Graph-Based Similarity Learning Using Heterogeneous Features.

Relational data refers to data that contains explicit relations among objects. Nowadays, relational data are universal and have a broad appeal in many different application domains. The problem of estimating similarity between objects is a core requirement for many standard Machine Learning (ML), Natural Language Processing (NLP) and Information Retrieval (IR) problems such as clustering, classiffication, word sense disambiguation, etc. Traditional machine learning approaches represent the data using simple, concise representations such as feature vectors. While this works very well for homogeneous data, i.e, data with a single feature type such as text, it does not exploit the availability of dfferent feature types fully. For example, scientic publications have text, citations, authorship information, venue information. Each of the features can be used for estimating similarity. Representing such objects has been a key issue in efficient mining (Getoor and Taskar, 2007). In this thesis, we propose natural representations for relational data using multiple, connected layers of graphs; one for each feature type. Also, we propose novel algorithms for estimating similarity using multiple heterogeneous features. Also, we present novel algorithms for tasks like topic detection and music recommendation using the estimated similarity measure. We demonstrate superior performance of the proposed algorithms (root mean squared error of 24.81 on the Yahoo! KDD Music recommendation data set and classiffication accuracy of 88% on the ACL Anthology Network data set) over many of the state of the art algorithms, such as Latent Semantic Analysis (LSA), Multiple Kernel Learning (MKL) and spectral clustering and baselines on large, standard data sets.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89824/1/mpradeep_1.pd

Dynamics of Information Diffusion

Real diffusion networks are complex and dynamic, since underlying social structures are not only far-reaching beyond a single homogeneous system but also frequently changing with the context of diffusion. Thus, studying topic-related diffusion across multiple social systems is important for a better understanding of such realistic situations. Accordingly, this thesis focuses on uncovering topic-related diffusion dynamics across heterogeneous social networks in both model-driven and model-free ways. We first conduct empirical studies for analyzing diffusion phenomena in real world systems, such as new diffusion in social media and knowledge transfer in academic publications. We observe that large diffusion is more likely attributed to interactions between heterogeneous social networks as if they were in the same networks. Thus, external influences from out-of-the-network sources, as observed in previous work, need to be explained with the context of interactions between heterogeneous social networks. This observation motivates our new conceptual framework for cross-population diffusion, which extends the traditional diffusion mechanism to a more flexible and general one. Second, we propose both model-driven and model-free approaches to estimate global trends of information diffusion. Based on our conceptual framework, we propose a model-driven approach which allows internal influence to reach heterogeneous populations in a probabilistic way. This approach extends a simple and robust mass action diffusion model by incorporating the structural connectivity and heterogeneity of real-world networks. We then propose a model-free approach using informationtheoretic measures with the consideration of both time-delay and memory effects on diffusion. In contrast to the model-driven approach, this model-free approach does not require any assumptions on dynamic social interactions in the real world, providing the benefits of quantifying nonlinear dynamics of complex systems. Finally, we compare our model-driven and model-free approaches in accordance with different context of diffusion. This helps us to obtain a more comprehensive understanding of topic-related diffusion patterns. Both approaches provide a coherent macroscopic view of global diffusion in terms of the strength and directionality of influences among heterogeneous social networks. We find that the two approaches provide similar results but with different perspectives, which in conjunction can help better explain diffusion than either approach alone. They also suggest alternative options as either or both of the approaches can be used appropriate to the real situations of different application domains. We expect that our proposed approaches provide ways to quantify and understand cross-population diffusion trends at a macro level. Also, they can be applied to a wide range of research areas such as social science, marketing, and even neuroscience, for estimating dynamic influences among target regions or systems