1,720,958 research outputs found
Subset Selection and Detection Problems in Opinion Dynamics Models
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
FJ-MM: Friedkin-Johnsen opinion dynamics model with memory and higher-order neighbors
Full text linkThe Friedkin-Johnsen (FJ) model has been extensively validated across social science, systems and control, game theory, and algorithmic research. We introduce an advanced generalization - termed the FJ-MM model - that incorporates memory effects and multi-hop influence. This extension allows agents to naturally integrate both current and past opinions at each iteration. We analyze the stability and equilibrium properties of the FJ-MM model, demonstrating that they can be derived from those of a standard FJ model with an appropriately modified influence matrix. We examine the convergence rate of the FJ-MM model and demonstrate that, as can be expected, the time lags introduced by memory and higher-order neighbor influences result in slower convergence. Numerical results illustrate that memory and multi-hop influence reshape the final opinion landscape, e.g., by reducing polarization
Optimal selection of the most informative nodes in Opinion Dynamics on Networks
Full text linkFinding the optimal subset to observe in a network system is a fundamental problem in science and engineering, with a wide range of applications like monitoring spatial phenomena, control of epidemic spread, feature selection in machine learning, or active surveying in social studies. The goal of this paper is to address the subset selection problem on an Opinion Dynamics model where the variable of interest Y is the average opinion of the community. We consider the opinion vector X to be updated according to a Friedkin-Johnsen opinion dynamics model where every agent i is equipped with an original unknown belief ui, which is assumed to be normally distributed, and a parameter.i describing its openness to interactions. The objective function of the optimization problem is the variance reduction from the observation of the steady-state opinions of a subset K. V of agents. We show how this functional can be rewritten in terms of the Bonacich centrality and the cycle centrality of the agents in social network when the subset selection is of cardinality 1, providing particular graph-theoretic interpretations related to the network itself. In addition, first exploratory simulations highlight a behaviour which deviates from the one of known centrality measures depending on the choice of model parameters. Finally, we show that the submodularity of the functional is not guaranteed in our case and thus results taken from known literature are non-enforceable. This paves the way for further analysis
Detecting Stubborn Behaviors in Influence Networks: A Model-Based Approach for Resilient Analysis
Full text linkThe wide spread of on-line social networks poses new challenges in information environment and cybersecurity. A key issue is detecting stubborn behaviors to identify leaders and influencers for marketing purposes, or extremists and automatic bots as potential threats. Existing literature typically relies on known network topology and extensive centrality measures computation. However, the size of social networks and their often unknown structure could make social influence computation impractical. We propose a new approach based on opinion dynamics to estimate stubborn agents from data. We consider a DeGroot model in which regular agents adjust their opinions as a linear combination of their neighbors’ opinions, whereas stubborn agents keep their opinions constant over time. We formulate the stubborn nodes identification and their influence estimation problems as a low-rank approximation problem. We then propose an interpolative decomposition algorithm for their solution. We determine sufficient conditions on the model parameters to ensure the algorithm’s resilience to noisy observations. Finally, we corroborate our theoretical analysis through numerical results
On Controlling a Coevolutionary Model of Actions and Opinions
Full text linkWe deal with a control problem for a complex social network in which each agent has an action and an opinion, evolving according to a coevolutionary model. In particular, we consider a scenario in which a committed minority -a set of stubborn nodes- aims to steer a population, initially at a consensus, to a different consensus state. Our study focuses on determining the conditions under which such a goal is reached, and ultimately, how to optimally define a minimal committed minority. First, we derive a general monotone convergence result for the controlled coevolutionary model, under mild and general assumptions on the agents revision sequence. Then, we build on our theoretical result to propose a systematic approach to investigate the research problem
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Full text linkWe 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
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