1,721,021 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
No full text“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
IDENTIFYING INFLUENTIAL SPREADERS IN COMPLEX NETWORKS
No full textInfluence maximization is the problem of identifying the set of nodes that maximize the size of the outbreak of a spreading process occurring on the network. This problem is important for strategic decisions in marketing and political campaigns. Typically, the problem consists of finding small sets of initial spreaders in large static networks. Due to its computational complexity, the problem can not be solved exactly. Many methods have been proposed to approximate solutions to the influence maximization problem. Here, we first study the effectiveness of proposed methods on a large corpus of real-world networks. We show that simple heuristic methods with low computational complexity can provide comparable solutions to optimization algorithms with high computational burden. Furthermore, we propose a machine learning based approach that combines heuristic methods to increase the performance of provided solutions. Next, we tackle the problem of noise in network structure and dynamics data. We analyze both the individual and combined effects of structural and dynamical noise on the quality of solutions. We show that implementing artificial noise can improve the performance of optimization algorithms to identify influential spreaders. We further analyze the influence maximization problem on temporal networks. We show that losing the information on the ordering or the timing of the interactions significantly decreases the ability to identify influential spreaders. Furthermore, information of the network structure during the first phases of the spreading dynamics is important in order to successfully find influential spreaders, especially when the recovery probability is high
NODE INFLUENCE IN NETWORK-BASED DISCRETE DYNAMICAL SYSTEMS
No full textThesis (Ph.D.) - Indiana University, School of Informatics, Computing, and Engineering, 2023Many complex systems can be modeled as network-based discrete dynamical systems, where individual nodes (representing some variable of interest) are connected by edges that describe their interactions. Each node can take on different states relevant to the network under investigation (for example, a gene may be turned ON or OFF in a genetic regulatory network, or a person may be Infected or Susceptible to a disease in an epidemiological network). These networks are frequently studied by analyzing global properties such as fixed points, robustness (sensitivity to perturbation), and functional organization (modularity). A fundamental problem in complex systems science is to understand how interactions between individual components of a system give rise to such properties; in other words, how can the influence of a node, or set of nodes, be measured and how does it affect the large-scale dynamics of the system? Understanding this influence is crucial to characterize, predict, and control complex systems. Traditional lines of inquiry often analyze only the network structure or assume that the entire network configuration is known (i.e., the state value of all nodes in the network). In practice, however, a network’s structure may not be a good predictor of its dynamics and furthermore, it is reasonable to assume that some nodes may not be measureable or controllable. Some recent approaches, such as causal inference methods, do not make such assumptions; still, calculating influence is in general a NP-hard problem and thus there is a need to further develop feasible approximate methods that work well in practice. This dissertation focuses on methods to calculate node influence and uncover dynamical properties of a network (modular organization, attractor control sets, size of perturbation cascades) that depend only on limited (partial) knowledge of the network configuration. I begin by reviewing the literature on node influence and related problems (influence maximization, control, and modularity), with special focus on methods that utilize either causal inference or approximations of dynamics. I then expand upon these methods with my own contributions to the field. First, I utilize the the existing concept of pathway modules on a dynamical map to define complex (synergistic) modules and use these to describe a network’s dynamics by its underlying causal mechanisms (by calculating direct node influence) and measure its dynamical modularity. Next I use a mean-field approximation of a node’s state based on iterative update of the states of its inputs (the IBMFA) to estimate the influence of that node on long-term configurations and attractors of a network, finding that the approximation performs well in comparison to actual simulations of the system. Finally, I define a thresholded representation of the dynamics (a generalized threshold network) to study different structural representations of the node update functions. I use these different representations and the IBMFA to calculate node influence and find that the choice of which method to use depends on the connectivity of the graph and the precision required. These methods are applied to various networks including random Boolean networks and biological signaling and regulatory networks, with examples given of additional use cases (such as linear threshold models and game-theoretic networks). Taken together, they help to elucidate the role of individual components within complex systems, with applications to dynamical modularity, influence maximization, and attractor/target control. Throughout I try to bridge the gap between literature on dynamical networks (e.g., logical models, Boolean networks) and dynamical processes on networks (e.g., epidemic and information spreading)
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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