Diferenças

Aqui você vê as diferenças entre duas revisões dessa página.

--- geopro:pedro:networks [2008/06/20 19:48] – pedro
+++ geopro:pedro:networks [2009/03/30 17:10] (atual) – pedro
@@ Linha 15: / Linha 15: @@
   * **social distance:** the shortest number of steps between 2 persons in a network.
   * **Fruchterman-Reingold algorithm**: Graph Drawing by Force-directed Placement (1991)
+  * **Boolean network:** a set of Boolean variables whose state is determined by other variables in the network. They are a particular case of discrete dynamical networks, where time and states are discrete, i.e. they have a bijection onto an integer series. Boolean and elementary cellular automata are particular cases of Boolean networks.
 =====Motivation=====
@@ Linha 54: / Linha 55: @@
-====The Architecture of Complexity====
-|A. Barabazi, 2007| IEEE Control Systems Magazine, 33:33-42| [[http://www.leg.ufpr.br/~pedro/papers/barabazi-architecture-of-complexity.pdf|pdf]]|
-{{  http://www.leg.ufpr.br/~pedro/figures/barabazi-networks.jpg }}
-====The Spatial Structure of Networks====
-|M. T. Gastner and M. E. J. Newmann, 2006| European Physical Journal B 49, 247-252|
-\\
-**Abstract:** We study **networks that connect points in geographic space**, such as transportation networks and the Internet. We find
-that **there are strong signatures in these networks of topography and use patterns, giving the networks shapes that are quite distinct from
-one another and from non-geographic networks**. We offer an explanation of these differences in terms of the costs and benefits of
-transportation and communication, and give a simple model based on Monte Carlo optimization of these costs and benefits that reproduces
-well the qualitative features of the networks studied.
-Internet and airline networks are not really two-dimensional at all, but the road network is.
-|                         ^  Internet    ^  airlines   ^  highway  |
-^vertices                 |  7049 computers  |  187 airports  |  935 intersections, termination points, and country borders  |
-^edges                    |  13831 links  |  825 scheduled flights  |  1337 highway stretches  |
-^diameter                 |  8  |   3  |  61  |
-^degree of the vertices   |  2138 (30%)  |  141 (76%)  |  4 (0,4%)  |
-^peaks on the distribution  |  two  |  two  |   one  |
-Why do not use cities as vertices of a highway instead of intersections, termination points and country borders?
-Future work: the effects of population distribution on the networks, and vice-versa.
-====Collective dynamics of `small-world' networks====
-|Watts, D J and Strogatz, S H, 1998| Nature  393(6684) 440-442|
-\\
-**Abstract:** Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays,, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks `rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them `small-world' networks, by analogy with the small-world phenomenon, (popularly known as six degrees of separation). The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
-\\
-====The Strength of Weak Ties====
-|M. S. Granovetter| Americal Journal of Sociology, 1973 78(6) 1360-1380| [[http://www.stanford.edu/dept/soc/people/mgranovetter/documents/granstrengthweakties.pdf|pdf]]|
-{{  http://www.leg.ufpr.br/~pedro/figures/bridges.jpg}}
-\\
-**Abstract:** Analysis of social networks is suggested as a tool for linking micro and macro levels of sociological theory. The procedure is illustrated by elaboration of the macro implications of one aspect of small-scale interaction: **the strength of dyadic ties. It is argued that the degree of overlap of two individuals' friendship networks varies directly with the strength of their tie to one another. The impact of this principle on diffusion of influence and information, mobility opportunity, and community organization is explored.** Stress is laid on the cohesive power of weak ties. Most network models deal, implicitly, with strong ties, thus confining their applicability to small, well-defined groups. Emphasis on weak ties lends itself to discussion of relations //between// groups and to analysis of segments of social structure not easily defined in terms of primary groups.
-\\
-**The strength of a tie is a (probabily linear) combination of the amount of time, the emotional intensity, the intimacy (multual confiding), and the reciprocal services which characterize the tie.** Ties are strong, weak, or absent. The stronger the tie between A and B, the larger the proportion of S to whom they will both be tied. The hypothesis is made plausible also by empirical evidence that the stronger the tie connecting two individuals, the more similar they are, in various ways.
-In the figure, A-B is a local bridge of degree 3 (above), and of degree 13 (below). As higher is the degree, stronger is the bridge. By the same logic used above, only weak ties may be local bridges.
-Tells about the problem of a participant observation to get information of a fairy restricted circle, and therefore do not take into account the weak ties.
@@ Linha 119: / Linha 69: @@
 \\
-====Geographic routing in social networks====
-{{ http://www.leg.ufpr.br/~pedro/figures/distance-and-friendship2.jpg}}
-|D Liben-Nowel, J Novak, R Kumar, P Raghavan, and A Tomkins, 2005|PNAS 102(33) 11623–11628| [[http://www.leg.ufpr.br/~pedro/papers/pnas/geographic-routing-social-networks.pdf|pdf]]|
-\\
-**Abstract:**We live in a ‘‘small world,’’ where two arbitrary people are likely connected by a short chain of intermediate friends. With scant
-information about a target individual, people can successively forward a message along such a chain. Experimental studies have
-verified this property in real social networks, and theoretical models have been advanced to explain it. However, existing
-theoretical models have not been shown to capture behavior in real-world social networks. Here, we introduce a richer model
-relating geography and social-network friendship, in which the probability of befriending a particular person is inversely proportional
-to the number of closer people. In a large social network, we show that one-third of the friendships are independent of geography
-and the remainder exhibit the proposed relationship. Further, we prove analytically that short chains can be discovered in
-every network exhibiting the relationship.
-\\
-at first blush, geographic location might have very little to do with the identity of a
-person’s online friends, but Fig. 3A verifies that geography remains crucial in online friendship.
-Although it has been suggested that the impact of distance is marginalized by communications technology
-(26), a large body of research shows that proximity remains a critical factor in effective collaboration and that the negative impacts of
-distance on productivity are only partially mitigated by technology (27). However, for distances larger than 1000 km, the
-curve approximately flattens to a constant probability of friendship between people, regardless of the geographic distance between them.
-====Social and Geographic Distance in HIV Risk====
-|R. Rothenberg and S. Q. Muth and S. Malone and J. J. Potterat and D. E. Woodhouse, 2005| Sexually Transmitted Diseases, 32(8)506–512| [[http://www.leg.ufpr.br/~pedro/papers/social-geographic-distance-hiv.pdf|pdf]]|
-{{  http://www.leg.ufpr.br/~pedro/figures/hiv-spatial-distance.jpg?400}}
-\\
-**Objective: **The objective of this study was to examine the relationship
-between social distance (measured as the geodesic, or shortest
-distance, between 2 people in a connected network) and geographic
-distance (measured as the actual distance between them in kilometers
-[km]).
-**Study:** We used data from a study of 595 persons at risk for HIV
-and their sexual and drug-using partners (total N = 8920 unique
-individuals) conducted in Colorado Springs, Colorado, from 1988 to
-—a longitudinal cohort study that ascertained sociodemographic,
-clinical, behavioral, and network information about participants. We
-used place of residence as the geographic marker and calculated
-distance between people grouped by various characteristics of interest.
-{{http://www.leg.ufpr.br/~pedro/figures/hiv-social-distance.jpg  }}
-\\
-\\
-\\
-\\
-\\
-\\
-\\
-\\
-\\
-\\
-**Results:** Fifty-two percent of all dyads were separated by a distance
-of 4 km or less. The closest pairs were persons who both shared needles
-and had sexual contact (mean ~ 3.2 km), and HIV-positive persons
-and their contacts (mean ~ 2.9). The most distant pairs were prostitutes
-and their paying partners (mean ~ 6.1 km). In a connected
-subset of 348 respondents, almost half the persons were between 3 and
-steps from each other in the social network and were separated by a
-distance of 2 to 8 km. Using block group centroids, the mean distance
-between all persons in Colorado Springs was 12.4 km compared with
-a mean distance of 5.4 km between all dyads in this study (P <0.0001).
-The subgroup of HIV-positive people and their contacts was drawn in
-real space on a map of Colorado Springs and revealed tight clustering
-of this group in the downtown area.
-**Conclusion: **The association of social and geographic distance in an
-urban group of people at risk for HIV provides demonstration of the
-importance of geographic clustering in the potential transmission of
-HIV. The proximity of persons connected within a network, but not
-necessarily known to each other, suggests that a high probability of
-partner selection from within the group may be an important factor in
-maintenance of HIV endemicity.
 ====Scale-free network of a dengue epidemic====
@@ Linha 284: / Linha 146: @@
 \\
@@ Linha 304: / Linha 167: @@
 \\
+FIXME Checar a veracidade: the behaviour of individuals cannot be explained except in terms of their
+interaction with other individuals known to them. Individuals being influenced by other individuals
+without slavishly imitating them.
 ====Capturing Social Embeddedness: a constructivist approach====