6 research outputs found
Quantitative description and modeling of real networks
In this letter we present data analysis and modeling of two particular cases
of study in the field of growing networks. We analyze WWW data set and
authorship collaboration networks in order to check the presence of correlation
in the data. The results are reproduced with a pretty good agreement through a
suitable modification of the standard AB model of network growth. In
particular, intrinsic relevance of sites plays a role in determining the future
degree of the vertex.Comment: 4 pages, 3 figure
Cliques and duplication-divergence network growth
A population of complete subgraphs or cliques in a network evolving via
duplication-divergence is considered. We find that a number of cliques of each
size scales linearly with the size of the network. We also derive a clique
population distribution that is in perfect agreement with both the simulation
results and the clique statistic of the protein-protein binding network of the
fruit fly. In addition, we show that such features as fat-tail degree
distribution, various rates of average degree growth and non-averaging,
revealed recently for only the particular case of a completely asymmetric
divergence, are present in a general case of arbitrary divergence.Comment: 7 pages, 6 figure
Scale-Free networks from varying vertex intrinsic fitness
A new mechanism leading to scale-free networks is proposed in this Letter. It is shown that, in many cases of interest, the connectivity power-law behavior is neither related to dynamical properties nor to preferential attachment. Assigning a quenched fitness value xi to every vertex, and drawing links among vertices with a probability depending on the fitnesses of the two involved sites, gives rise to what we call a good-get-richer mechanism, in which sites with larger fitness are more likely to become hubs (i.e., to be highly connected)
Growing networks with local rules: preferential attachment, clustering hierarchy and degree correlations
The linear preferential attachment hypothesis has been shown to be quite
successful to explain the existence of networks with power-law degree
distributions. It is then quite important to determine if this mechanism is the
consequence of a general principle based on local rules. In this work it is
claimed that an effective linear preferential attachment is the natural outcome
of growing network models based on local rules. It is also shown that the local
models offer an explanation to other properties like the clustering hierarchy
and degree correlations recently observed in complex networks. These
conclusions are based on both analytical and numerical results of different
local rules, including some models already proposed in the literature.Comment: 17 pages, 14 figures (to appear in Phys. Rev E
Loops structure of the Internet at the autonomous system level
We present here a study of the clustering and loops in a graph of the Internet at the autonomous systems level. We show that, even if the whole structure is changing with time, the statistical distributions of loops of order 3, 4, and 5 remain stable during the evolution. Moreover, we will bring evidence that the Internet graphs show characteristic Markovian signatures, since the structure is very well described by two-point correlations between the degrees of the vertices. This indeed proves that the Internet belongs to a class of network in which the two-point correlation is sufficient to describe their whole local (and thus global) structure. Data are also compared to present Internet models. © 2005 The American Physical Society
Detecting communities in large networks
We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and link orientation. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable for the analysis of social and information networks. We test the algorithm on a large-scale data-set from a psychological experiment of word association. In this case, it proves to be successful both in clustering words, and in uncovering mental association patterns