Decremental All-Pairs ALL Shortest Paths and Betweenness Centrality

We consider the all pairs all shortest paths (APASP) problem, which maintains the shortest path dag rooted at every vertex in a directed graph G=(V,E) with positive edge weights. For this problem we present a decremental algorithm (that supports the deletion of a vertex, or weight increases on edges incident to a vertex). Our algorithm runs in amortized O(\vstar^2 \cdot \log n) time per update, where n=|V|, and \vstar bounds the number of edges that lie on shortest paths through any given vertex. Our APASP algorithm can be used for the decremental computation of betweenness centrality (BC), a graph parameter that is widely used in the analysis of large complex networks. No nontrivial decremental algorithm for either problem was known prior to our work. Our method is a generalization of the decremental algorithm of Demetrescu and Italiano [DI04] for unique shortest paths, and for graphs with \vstar =O(n), we match the bound in [DI04]. Thus for graphs with a constant number of shortest paths between any pair of vertices, our algorithm maintains APASP and BC scores in amortized time O(n^2 \log n) under decremental updates, regardless of the number of edges in the graph.Comment: An extended abstract of this paper will appear in Proc. ISAAC 201

Detection the Coordinators by using of Social Network Analysis: Case study: in an Emergency Management

The adverse consequences of emergencies obviouslysupport the need for integrated preparednessof operational teams at the emergencyresponse management to do their best to response mostreasonably and effectively. The main obstacle to this preparednessis the lack of coordination across the variousgroups which obstruct attempts for preparation. Achievementof proper coordination requires effective relationshipamong all team members. One way to improve coordinationis to identify effective individuals that play vitalroles as coordinators in the network. This survey has beendone among 142 responders of the emergency management,as a case study. To identify members who occupythe central positions as coordinators, this study utilizedcentrality indicators including degree, closeness, and betweennessof social network analysis at the individual level.The data were collected through structured interviews andwere analyzed into social network analysis program, UCINET6.0. This study, also, performed an attempt to investigatethe relationship between demographic factors and centralityindicators. Statistical analysis was performed with SPSS16 and GraphPad. According to the findings, individualswith high mutual selections in both directions (in and outdegrees) had more chance to be accepted as powerful andinfluential members by other members (P<0.0000). Findingsof this study indicated that there is no significant relationshipbetween demographic factors (age, material statues, educationlevel) and centrality indicators (P>0.05).Also, there wasa significant relationship between demographic variablessuch as work experience, type of unit and the number ofthe experience in the emergency situation and the centralityindicators (P<0.05). Consequently, the present research studiedthe relationships among the team members of the emergencymanagement to select coordinators. The findings ofthis study may allow the planners and decision makers to beaware of hidden relationships in their network

Scalable Online Betweenness Centrality in Evolving Graphs

Betweenness centrality is a classic measure that quantifies the importance of a graph element (vertex or edge) according to the fraction of shortest paths passing through it. This measure is notoriously expensive to compute, and the best known algorithm runs in O(nm) time. The problems of efficiency and scalability are exacerbated in a dynamic setting, where the input is an evolving graph seen edge by edge, and the goal is to keep the betweenness centrality up to date. In this paper we propose the first truly scalable algorithm for online computation of betweenness centrality of both vertices and edges in an evolving graph where new edges are added and existing edges are removed. Our algorithm is carefully engineered with out-of-core techniques and tailored for modern parallel stream processing engines that run on clusters of shared-nothing commodity hardware. Hence, it is amenable to real-world deployment. We experiment on graphs that are two orders of magnitude larger than previous studies. Our method is able to keep the betweenness centrality measures up to date online, i.e., the time to update the measures is smaller than the inter-arrival time between two consecutive updates.Comment: 15 pages, 9 Figures, accepted for publication in IEEE Transactions on Knowledge and Data Engineerin

Article 13 on social media and news media: disintermediation and reintermediation on the modern media landscape

The former Article 13 (now Article 17) of the European directive on copyright and the internet (Directive EC2019/790) has been under negotiations since 2016 and was finally approved in 2019. In Portugal, however, the issue was mostly absent from public scrutiny and debate until November 2018. In that month, the issue arose to a prominent level, both in news media and in social media, following a wave of alerts issued by various young youtubers, incentivized by YouTube management. In this paper, we engage in the discussion concerning disintermediation, studying the way in which such alerts spread both in news media and social media, and understanding the role played by the users of social media platforms in modelling the social relevance and the social discourse of the issue of copyright and the internet. To do so, we used digital methods, collecting and analysing data from Twitter, YouTube and from online news media, mapping Article 13 discussions and identifying key actors in each field, as well as the connections between them. The results show that the ease of access provided by platforms such as Twitter or YouTube converts some users to prominent influencers and that, in some cases, those influencers are able to shift and model the public discourse about relevant collective issues

Interactions between tick and transmitted pathogens evolved to minimise competition through nested and coherent networks

Natural foci of ticks, pathogens, and vertebrate reservoirs display complex relationships that are key to the circulation of pathogens and infection dynamics through the landscape. However, knowledge of the interaction networks involved in transmission of tick-borne pathogens are limited because empirical studies are commonly incomplete or performed at small spatial scales. Here, we applied the methodology of ecological networks to quantify >14, 000 interactions among ticks, vertebrates, and pathogens in the western Palearctic. These natural networks are highly structured, modular, coherent, and nested to some degree. We found that the large number of vertebrates in the network contributes to its robustness and persistence. Its structure reduces interspecific competition and allows ample but modular circulation of transmitted pathogens among vertebrates. Accounting for domesticated hosts collapses the network'' s modular structure, linking groups of hosts that were previously unconnected and increasing the circulation of pathogens. This framework indicates that ticks and vertebrates interact along the shared environmental gradient, while pathogens are linked to groups of phylogenetically close reservoirs