Diffusion, Infection and Social (Information) Network Database

Research to analyze diffusive phenomena over large rich datasets has received considerable attention in recent years. Moreover, with the appearance and proliferation of online social network services, social (information) network analysis and mining techniques have become closely intertwined with the analysis of diffusive and infection phenomena. In this dissertation, we suggest various analysis and mining techniques to solve problems related to diffusive and infection phenomena over social (information) networks built from various datasets in diverse areas. This research makes five contributions. The first contribution is about influence analysis in social networks for which we suggest two new centrality measures, Diffusion Centrality and Covertness Centrality. Diffusion Centrality quantifies the influence of vertices in social networks with respect to a given diffusion model which explains how a diffusive property is spreading. Covertness Centrality quantifies how well a vertex can communicate (diffuse information) with (to) others and hide in networks as a common vertex w.r.t. a set of centrality measures. The second contribution is about network simplification problems to scale up analysis techniques for very large networks. For this topic, two techniques, CoarseNet and Coarsened Back and Forth (CBAF), are suggested in order to find a succinct representation of networks while preserving key characteristics for diffusion processes on that network. The third contribution is about social network databases. We propose a new network model, STUN (Spatio-Temporal Uncertain Networks), whose edges are characterized with uncertainty, space, and time, and develop a graph index structure to retrieve graph patterns over the network efficiently. The fourth contribution develops epidemic models and ensembles to predict the number of malware infections in countries using past detection history. In our fifth contribution, we also develop methods to predict financial crises of countries using financial connectedness among countries

Secrecy and absence in the residue of covert drone strikes

AbstractBy focusing on the materials and practices that prosecute drone warfare, critical scholarship has emphasised the internal state rationalisation of this violence, while positioning secrecy and absence as barriers to research. This neglects the public existence of covert U.S. drone strikes through the rumours and debris they leave behind, and the consequences for legitimisation. This article argues that by signifying the possible use of covertness, the public residue of unseen strikes materialises spaces of suspected secrecy. This secrecy frames seemingly arbitrary traces of violence as significant in having not been secreted by the state, and similarly highlights the absence in these spaces of clear markers of particular people and objects, including casualties. Drawing on colonial historiography, the article conceptualises this dynamic as producing implicit significations or intimations, unverifiable ideas from absences, which can undermine rationalisations of drone violence. The article examines the political consequences of these allusions through an historical affiliation with lynching practice. In both cases, traces of unseen violence represent the practice as distanced and confounding, prompting a focus on the struggle to comprehend. Intimations from spaces of residue position strikes as too ephemeral and materially insubstantial to understand. Unlike the operating procedures of drone warfare, then, these traces do not dehumanise targets. Rather, they narrow witnesses' ethical orientation towards these events and casualties, by prompting concern with intangibility rather than the infliction of violence itself. A political response to covert strikes must go beyond 'filling in' absences and address how absence gains meaning in implicit, inconspicuous ways

Key aspects of covert networks data collection:Problems, challenges, and opportunities

Data quality is considered to be among the greatest challenges in research on covert networks. This study identifies six aspects of network data collection, namely nodes, ties, attributes, levels, dynamics, and context. Addressing these aspects presents challenges, but also opens theoretical and methodological opportunities. Furthermore, specific issues arise in this research context, stemming from the use of secondary data and the problem of missing data. While each of the issues and challenges has some specific solution in the literature on organized crime and social networks, the main argument of this paper is to try and follow a more systematic and general solution to deal with these issues. To this end, three potentially synergistic and combinable techniques for data collection are proposed for each stage of data collection – biographies for data extraction, graph databases for data storage, and checklists for data reporting. The paper concludes with discussing the use of statistical models to analyse covert networks and the cultivation of relations within the research community and between researchers and practitioners