El problema del actor clave

Se describe un procedimiento para identificar actores clave en una red social. Un supuesto básico es que la selección optima depende de los fines de dicha selección. De acuerdo con ello, se articulan dos metas genéricas, referidas al problema del actor clave en términos positivos y negativos. Primero se propone un procedimiento para identificar actores clave con el objetivo de difundir algo de manera óptima en la red, valiéndose de los actores clave como semillas. El segundo procedimiento identifica actores clave con el objetivo de perturbar o fragmentar la red eliminando algunos de sus nodos. Los indicadores de centralidad habituales no son óptimos para este propósito, por lo que se proponen nuevos indicadores.A procedure is described for finding sets of key players in a social network. A key assumption is that the optimal selection of key players depends on what they are needed for. Accordingly, two generic goals are articulated, referring to key player problem in positive (KPP-1) and negative (KPP-2) terms. KPP-1 is defined as the identification of key players for the purpose of optimally diffusing something through the network by using the key players as seeds. KPP-2 is defined as the identification of key players for the purpose of disrupting or fragmenting the network by removing the key nodes. It is found that off-the-shelf centrality measures are not optimal for solving either generic problem, and therefore new measures are presented

Techniques: Dichotomizing a Network

This techniques guide provides a brief answer to the question: How to choose a dichotomization threshold? We propose a two step approach to selecting a dichotomization threshold. We illustrate the approaches using two datasets and provide instructions on how to perform these approaches in R and UCINET

Centrality in valued graphs: A measure of betweenness based on network flow

A new measure of centrality, C,, is introduced. It is based on the concept of network flows. While conceptually similar to Freeman’s original measure, Ca, the new measure differs from the original in two important ways. First, C, is defined for both valued and non-valued graphs. This makes C, applicable to a wider variety of network datasets. Second, the computation of C, is not based on geodesic paths as is C, but on all the independent paths between all pairs of points in the network

Characterizing Distances of Networks on the Tensor Manifold

At the core of understanding dynamical systems is the ability to maintain and control the systems behavior that includes notions of robustness, heterogeneity, or regime-shift detection. Recently, to explore such functional properties, a convenient representation has been to model such dynamical systems as a weighted graph consisting of a finite, but very large number of interacting agents. This said, there exists very limited relevant statistical theory that is able cope with real-life data, i.e., how does perform analysis and/or statistics over a family of networks as opposed to a specific network or network-to-network variation. Here, we are interested in the analysis of network families whereby each network represents a point on an underlying statistical manifold. To do so, we explore the Riemannian structure of the tensor manifold developed by Pennec previously applied to Diffusion Tensor Imaging (DTI) towards the problem of network analysis. In particular, while this note focuses on Pennec definition of geodesics amongst a family of networks, we show how it lays the foundation for future work for developing measures of network robustness for regime-shift detection. We conclude with experiments highlighting the proposed distance on synthetic networks and an application towards biological (stem-cell) systems.Comment: This paper is accepted at 8th International Conference on Complex Networks 201

Estimating mass-wasting processes in active earth slides – earth flows with time-series of High-Resolution DEMs from photogrammetry and airborne LiDAR

Abstract. This paper deals with the use of time-series of High-Resolution Digital Elevation Models (HR DEMs) obtained from photogrammetry and airborne LiDAR coupled with aerial photos, to analyse the magnitude of recently reactivated large scale earth slides – earth flows located in the northern Apennines of Italy. The landslides underwent complete reactivation between 2001 and 2006, causing civil protection emergencies. With the final aim to support hazard assessment and the planning of mitigation measures, high-resolution DEMs are used to identify, quantify and visualize depletion and accumulation in the slope resulting from the reactivation of the mass movements. This information allows to quantify mass wasting, i.e. the amount of landslide material that is wasted during reactivation events due to stream erosion along the slope and at its bottom, resulting in sediment discharge into the local fluvial system, and to assess the total volumetric magnitude of the events. By quantifying and visualising elevation changes at the slope scale, results are also a valuable support for the comprehension of geomorphological processes acting behind the evolution of the analysed landslides

Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings $\in \mathbb{R}^d$ and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex $v_1$ more central than vertex $v_2$ given centrality $c$?. We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves $89\%$ accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained ($n=128$). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.Comment: Published at ICANN2019. 10 pages, 3 Figure

A New Order of Things: Network Mechanisms of Field Evolution in the Aftermath of an Exogenous Shock

This study examines the role of a major environmental shock in triggering change in the social structure of an organizational field. Based on the longitudinal analysis of changing network configurations in the global airline industry, we explore how logics of attachment shift before, during and after an exogenous shock and how the rewiring of network ties in response to the shock may act as a countervailing force to the network dynamics that drive field stratification. Using the terrorist attacks of September 11, 2001 as a natural experiment, our work reveals how shocks may affect key mechanisms of network evolution thus altering tie distribution and access among members of the field. Overall this article contributes to emergent literature on field dynamics by exposing the evolution of interorganizational dynamics when external events produce unsettled times that render extant logics brittle and open prospects for change