A Survey on Centrality Metrics and Their Implications in Network Resilience

Centrality metrics have been used in various networks, such as communication, social, biological, geographic, or contact networks. In particular, they have been used in order to study and analyze targeted attack behaviors and investigated their effect on network resilience. Although a rich volume of centrality metrics has been developed for decades, a limited set of centrality metrics have been commonly in use. This paper aims to introduce various existing centrality metrics and discuss their applicabilities and performance based on the results obtained from extensive simulation experiments to encourage their use in solving various computing and engineering problems in networks.Comment: Main paper: 36 pages, 2 figures. Appendix 23 pages,45 figure

Exploring the Evolution of Node Neighborhoods in Dynamic Networks

Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of \textit{neighborhood event}, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes

Influence of fake news in Twitter during the 2016 US presidential election

The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated by www.opensources.co, we find that 25% of these tweets spread either fake or extremely biased news. We characterize the networks of information flow to find the most influential spreaders of fake and traditional news and use causal modeling to uncover how fake news influenced the presidential election. We find that, while top influencers spreading traditional center and left leaning news largely influence the activity of Clinton supporters, this causality is reversed for the fake news: the activity of Trump supporters influences the dynamics of the top fake news spreaders

Influence of fake news in Twitter during the 2016 US presidential election

The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated by www.opensources.co, we find that 25% of these tweets spread either fake or extremely biased news. We characterize the networks of information flow to find the most influential spreaders of fake and traditional news and use causal modeling to uncover how fake news influenced the presidential election. We find that, while top influencers spreading traditional center and left leaning news largely influence the activity of Clinton supporters, this causality is reversed for the fake news: the activity of Trump supporters influences the dynamics of the top fake news spreaders.Comment: Updated to latest revised versio

Force for ancient and recent life: viral and stem-loop RNA consortia promote life.

Lytic viruses were thought to kill the most numerous host (i.e., kill the winner). But persisting viruses/defectives can also protect against viruses, especially in a ubiquitous virosphere. In 1991, Yarmolinsky et al. discovered the addiction modules of P1 phage, in which opposing toxic and protective functions stabilize persistence. Subsequently, I proposed that lytic and persisting cryptic virus also provide addiction modules that promote group identity. In eukaryotes (and the RNA world), a distinct RNA virus-host relationship exists. Retrovirurses/retroposons are major contributors to eukaryotic genomes. Eukaryotic complexity appears to be mostly mediated by regulatory complexity involving noncoding retroposon-derived RNA. RNA viruses evolve via quasispecies, which contain cooperating, minority, and even opposing RNA types. Quasispecies can also demonstrate group preclusion (e.g., hepatitis C). Stem-loop RNA domains are found in long terminal repeats (and viral RNA) and mediate viral regulation/identity. Thus, stem-loop RNAs may be ancestral regulators. I consider the RNA (ribozyme) world scenario from the perspective of addiction modules and cooperating quasispecies (i.e., subfunctional agents that establish group identity). Such an RNA collective resembles a "gang" but requires the simultaneous emergence of endonuclease, ligase, cooperative catalysis, group identity, and history markers (RNA). I call such a collective a gangen (pathway to gang) needed for life to emerge

Consciousness, Sub-consciousness and Emotions, Soul and KDS

The report discusses some neurophysiologicai and other phenomena, interpretation of which persuasively supports, although indirectly, coceptual views developed by the authors, concerning memory organization in human brain and the processes that occur in it