269 research outputs found
From sparse to dense and from assortative to disassortative in online social networks
Inspired by the analysis of several empirical online social networks, we
propose a simple reaction-diffusion-like coevolving model, in which individuals
are activated to create links based on their states, influenced by local
dynamics and their own intention. It is shown that the model can reproduce the
remarkable properties observed in empirical online social networks; in
particular, the assortative coefficients are neutral or negative, and the power
law exponents are smaller than 2. Moreover, we demonstrate that, under
appropriate conditions, the model network naturally makes transition(s) from
assortative to disassortative, and from sparse to dense in their
characteristics. The model is useful in understanding the formation and
evolution of online social networks.Comment: 10 pages, 7 figures and 2 table
Optimal information diffusion in stochastic block models
We use the linear threshold model to study the diffusion of information on a
network generated by the stochastic block model. We focus our analysis on a two
community structure where the initial set of informed nodes lies only in one of
the two communities and we look for optimal network structures, i.e. those
maximizing the asymptotic extent of the diffusion. We find that, constraining
the mean degree and the fraction of initially informed nodes, the optimal
structure can be assortative (modular), core-periphery, or even disassortative.
We then look for minimal cost structures, i.e. those such that a minimal
fraction of initially informed nodes is needed to trigger a global cascade. We
find that the optimal networks are assortative but with a structure very close
to a core-periphery graph, i.e. a very dense community linked to a much more
sparsely connected periphery.Comment: 11 pages, 6 figure
Insights into failed lexical retrieval from network science
Previous network analyses of the phonological lexicon (Vitevitch, 2008) observed a web-like structure that exhibited assortative mixing by degree: words with dense phonological neighborhoods tend to have as neighbors words that also have dense phonological neighborhoods, and words with sparse phonological neighborhoods tend to have as neighbors words that also have sparse phonological neighborhoods. Given the role that assortative mixing by degree plays in network resilience, we examined instances of real and simulated lexical retrieval failures in computer simulations, analysis of a slips-of-the-ear corpus, and three psycholinguistic experiments for evidence of this network characteristic in human behavior. The results of the various analyses support the hypothesis that the structure of words in the mental lexicon influences lexical processing. The implications of network science for current models of spoken word recognition, language processing, and cognitive psychology more generally are discussed
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