Math algorithm tracks crime, rumours, epidemics to source
(Phys.org) -- A team of EPFL scientists has developed an algorithm that can identify the source of an epidemic or information circulating within a network, a method that could also be used to help with criminal investigations. Investigators are well aware of how difficult it is to trace an unlawful act to its source. The job was arguably easier with old, Mafia-style criminal organizations, as their hierarchical structures more or less resembled predictable family trees. In the Internet age, however, the networks used by organized criminals have changed. "Using our method, we can find the source of all kinds of things circulating in a network just by 'listening' to a limited number of members of that network," explains Pinto. Out in the real world, the algorithm can be employed to find the primary source of an infectious disease, such as cholera. The validity of this method thus has been proven a posteriori. Explore further: Pseudo-mathematics and financial charlatanism
Clustering coefficient
In graph theory, a clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. Evidence suggests that in most real-world networks, and in particular social networks, nodes tend to create tightly knit groups characterised by a relatively high density of ties; this likelihood tends to be greater than the average probability of a tie randomly established between two nodes (Holland and Leinhardt, 1971;[1] Watts and Strogatz, 1998[2]). Two versions of this measure exist: the global and the local. Global clustering coefficient[edit] The global clustering coefficient is based on triplets of nodes. Watts and Strogatz defined the clustering coefficient as follows, "Suppose that a vertex has neighbours; then at most edges can exist between them (this occurs when every neighbour of is connected to every other neighbor of ). denote the fraction of these allowable edges that actually exist. as the average of over all Transitivity Ratio[edit] A graph and a set of edges
Cours de cartes conceptuelles
Une carte conceptuelle (ou schéma conceptuel, concept map en anglais), dont les variantes sont la mind map, carte des idées ou carte heuristique, est un diagramme qui représente les liens entre différents concepts. Elle peut être également appelée schéma de pensée, carte mentale, arbre à idées ou topogramme. La différence entre une carte heuristique et une carte conceptuelle est que cette dernière relie un ensemble de concepts entre eux par des lignes orientées et qualifiées (est un composant de.., favorise). De plus, elle prend la forme d' un graphe alors que la carte heuristique est un arbre. Ces cours en diapositives animées, vidéo, ppt, pdf et ces cartes conceptuelles sont distribués sous licence Creative Commons : à condition de me citer et de mettre un lien vers cette page, vous pouvez les réutiliser ou les modifier dans un cadre non-commercial, mais vous devez ensuite les publier aux mêmes conditions. - Cours de mind mapping en vidéo - En pdf et pptx En analyse fonctionnelle :
How to Burst the "Filter Bubble" that Protects Us from Opposing Views
The term “filter bubble” entered the public domain back in 2011when the internet activist Eli Pariser coined it to refer to the way recommendation engines shield people from certain aspects of the real world. Pariser used the example of two people who googled the term “BP”. One received links to investment news about BP while the other received links to the Deepwater Horizon oil spill, presumably as a result of some recommendation algorithm. This is an insidious problem. Much social research shows that people prefer to receive information that they agree with instead of information that challenges their beliefs. This problem is compounded when social networks recommend content based on what users already like and on what people similar to them also like. This is the filter bubble—being surrounded only by people you like and content that you agree with. And the danger is that it can polarise populations creating potentially harmful divisions in society. It’s certainly a start.
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Power law
An example power-law graph, being used to demonstrate ranking of popularity. To the right is the long tail, and to the left are the few that dominate (also known as the 80–20 rule). In statistics, a power law is a functional relationship between two quantities, where a relative change in one quantity results in a proportional relative change in the other quantity, independent of the initial size of those quantities: one quantity varies as a power of another. For instance, considering the area of a square in terms of the length of its side, if the length is doubled, the area is multiplied by a factor of four.[1] Empirical examples of power laws[edit] Properties of power laws[edit] Scale invariance[edit] One attribute of power laws is their scale invariance. , scaling the argument by a constant factor causes only a proportionate scaling of the function itself. That is, scaling by a constant simply multiplies the original power-law relation by the constant . and A power-law only if Universality[edit]
Installation d'un réseau : les étapes à respecter
1. Conseil de déploiement et audit du réseau Avant de démarrer une installation ou une modernisation du réseau informatique, l'entreprise doit évaluer ses besoins et connaître le périmètre fonctionnel de son réseau. Un prestataire rédige alors un cahier des charges précis. la nature et la superficie des locaux à équiper,les contraintes techniques de ces locaux,le nombre de serveurs requis,le nombre de personnes et de postes de travail à relier,les besoins en bande passante et en débit. 2. Cette étape vise à équiper les locaux de l'entreprise d'un réseau de câbles pour relier les serveurs et les PC entre eux. si le bâtiment est ancien, il sera difficile de percer les murs. 3. Il convient de vérifier la connectique des serveurs. 4. Les commutateurs réseaux sont des équipements électroniques intelligents qui permettent de connecter les différents serveurs et les postes de travail. 5. Cette étape plutôt classique vise à paramétrer les serveurs Windows et/ou Linux. 6. 7.
Network science
{{Scienc[1] e}} Network science is an interdisciplinary academic field which studies complex networks such as telecommunication networks, computer networks, biological networks, cognitive and semantic networks, and social networks. The field draws on theories and methods including graph theory from mathematics, statistical mechanics from physics, data mining and information visualization from computer science, inferential modeling from statistics, and social structure from sociology. The United States National Research Council defines network science as "the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena."[2] Background and history[edit] The study of networks has emerged in diverse disciplines as a means of analyzing complex relational data. In the 1930s Jacob Moreno, a psychologist in the Gestalt tradition, arrived in the United States. Department of Defense Initiatives[edit] In 2006, the U.S. Density[edit] .
