Biomedical subjects

Sameet Sreenivasan

Publications and source records attributed to Sameet Sreenivasan.

4 recordsLinked to original sources

PubMed2006

Scale-free networks emerging from weighted random graphs.

We study Erdös-Rényi random graphs with random weights associated with each link. We generate a "supernode network" by merging all nodes connected by links having weights below the percolation threshold (percolation clusters) into a single node. We show that this network is scale-free, i.e., the degree distribution is P(k) approximately k(-lambda) with lambda=2.5. Our results imply that the minimum spanning tree in random graphs is composed of percolation clusters, which are interconnected by a set of links that create a scale-free tree with lambda=2.5. We suggest that optimization causes the percolation threshold to emerge spontaneously, thus creating naturally a scale-free supernode network. We discuss the possibility that this phenomenon is related to the evolution of several real world scale-free networks.

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PubMed2005

Resilience of complex networks to random breakdown.

Using Monte Carlo simulations we calculate fc, the fraction of nodes that are randomly removed before global connectivity is lost, for networks with scale-free and bimodal degree distributions. Our results differ from the results predicted by an equation for fc proposed by Cohen We discuss the reasons for this disagreement and clarify the domain for which the proposed equation is valid.

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PubMed2004

Effect of disorder strength on optimal paths in complex networks.

We study the transition between the strong and weak disorder regimes in the scaling properties of the average optimal path l(opt) in a disordered Erdos-Rényi (ER) random network and scale-free (SF) network. Each link i is associated with a weight tau(i) identical withexp (a r(i) ) , where r(i) is a random number taken from a uniform distribution between 0 and 1 and the parameter a controls the strength of the disorder. We find that for any finite a , there is a crossover network size N* (a) at which the transition occurs. For N< /=4 , and l(opt) approximately N (lambda-3) (/ (lambda-1) ) for SF networks with 3 >N* (a) the scaling behavior is in the weak disorder regime, with l(opt) approximately ln N for ER networks and SF networks with lambda>3 . In order to study the transition we propose a measure which indicates how close or far the disordered network is from the limit of strong disorder. We propose a scaling ansatz for this measure and demonstrate its validity. We proceed to derive the scaling relation between N* (a) and a . We find that N* (a) approximately a(3) for ER networks and for SF networks with lambda>/=4 , and N* (a) approximately a (lambda-1) (/ (lambda-3) ) for SF networks with 3<lambda<4 .

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PubMed2002

Continuum percolation threshold for interpenetrating squares and cubes.

Monte Carlo simulations are performed to determine the critical percolation threshold for interpenetrating square objects in two dimensions and cubic objects in three dimensions. Simulations are performed for two cases: (i) objects whose edges are aligned parallel to one another and (ii) randomly oriented objects. For squares whose edges are aligned, the critical area fraction at the percolation threshold phi(c)=0.6666+/-0.0004, while for randomly oriented squares phi(c)=0.6254+/-0.0002, 6% smaller. For cubes whose edges are aligned, the critical volume fraction at the percolation threshold phi(c)=0.2773+/-0.0002, while for randomly oriented cubes phi(c)=0.2168+/-0.0002, 22% smaller.

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