1,720,973 research outputs found
Tomography of Large Adaptive Networks under the Dense Latent Regime
No full textThis work examines the problem of graph learning over a diffusion network when measurements can only be gathered from a limited fraction of agents (latent regime). Under this selling, most works in the literature rely on a degree of sparsity to provide guarantees of consistent graph recovery. This work moves away from this condition and shows that, even under dense connectivity, the Granger estimator ensures an identifiability gap that enables the discrimination between connected and disconnected nodes within the observable subnetwork
Tomography Of Adaptive Multi-Agent Networks Under Limited Observation
No full textThis work studies the problem of inferring from streaming data whether an agent is directly influenced by another agent over an adaptive network of interacting agents. Agent i influences agent j if they are connected, and if agent j uses the information from agent i to update its inference. The solution of this inference task is challenging for at least two reasons. First, only the output of the learning algorithm is available to the external observer and not the raw data. Second, only observations from a fraction of the network agents is available, with the total number of agents itself being also unknown. This work establishes, under reasonable conditions, that consistent tomography is possible, namely, that it is possible to reconstruct the interaction profile of the observable portion of the network, with negligible error as the network size increases. We characterize the decaying behavior of the error with the network size, and provide a set of numerical experiments to illustrate the results.AS
Graph Learning with Partial Observations: Role of Degree Concentration
No full textIn this work we consider the problem of learning an Erdos-Renyi graph over a diffusion network when: i) data from only a limited subset of nodes are available (partial observation); ii) and the inferential goal is to discover the graph of interconnections linking the accessible nodes (local structure learning). We propose three matrix estimators, namely, the Granger, the onelag correlation, and the residual estimators, which, when followed by a universal clustering algorithm, are shown to retrieve the true subgraph in the limit of large network sizes. Remarkably, it is seen that a fundamental role is played by the uniform concentration of node degrees, rather than by sparsity
DIVIDE-AND-CONQUER TOMOGRAPHY FOR LARGE-SCALE NETWORKS
No full textThis work considers the problem of reconstructing the topology of a network of interacting agents via observations of the state-evolution of the agents. Observations from only a subset of the nodes are collected, and the information is used to infer their local connectivity (local tomography). Recent results establish that, under suitable conditions on the network model, local tomography is achievable with high probability as the network size scales to infinity [1, 2]. Motivated by these results, we explore the possibility of reconstructing a larger network via repeated application of the local tomography algorithm to smaller network portions. A divide-and-conquer strategy is developed and tested numerically on some illustrative examples
Exponential Collapse of Social Beliefs over Weakly-connected Heterogeneous Networks
No full textWe consider a distributed social learning problem where a network of agents is interested in selecting one among a finite number of hypotheses. The data collected by the agents might be heterogeneous, meaning that different sub-networks might observe data generated by different hypotheses. For example, some sub-networks might be receiving (or even intentionally generating) data from a fake hypothesis and will bias the rest of the network via social influence. This work focuses on a two-step diffusion algorithm where each agent: i) first updates individually its belief function using its private data; ii) then computes a new belief function by exponentiating a linear combination of the log-beliefs of its neighbors. We obtain analytical formulas that reveal how the agents' detection capability and the network topology interplay to influence the asymptotic beliefs of the agents. Some interesting behaviors arise, such as the "mind-control" effect or the "truth-is-somewhere-in-between" effect
Consistent Tomography over Diffusion Networks under the Low-Observability Regime
No full textThis work considers a diffusion network responding to streaming data, and studies the problem of identifying the topology of a subnetwork of observable agents by tracking their output measurements. Topology inference from indirect and/or incomplete datasets (network tomography) is in general an ill-posed problem. Under an appropriate Erdos-Renyi random graph model for the unobserved part, the problem of network tomography is well-posed in the thermodynamic limit: when the number of network agents grows to infinity, any arbitrary subnetwork topology associated with the observed agents can be recovered with high probability.AS
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
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