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- Réka Albert, Bhaskar DasGupta, Nasim Mobasheri
- Physical review. E, Statistical, nonlinear, and…
- 2014

Network measures that reflect the most salient properties of complex large-scale networks are in high demand in the network research community. In this paper we adapt a combinatorial measure of negative curvature (also called hyperbolicity) to parametrized finite networks, and show that a variety of biological and social networks are hyperbolic. This… (More)

- Réka Albert, Bhaskar DasGupta, Nasim Mobasheri
- Biomedical engineering and computational biology
- 2013

Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic… (More)

- Bhaskar Das Gupta, Marek Karpinski, Nasim Mobasheri, Farzane Yahyanejad
- Algorithmica
- 2017

$$\delta $$ δ -Hyperbolic graphs, originally conceived by Gromov (Essays in group theory. 1987), occur often in many network applications; for fixed $$\delta $$ δ , such graphs are simply called hyperbolic graphs and include non-trivial interesting classes of “non-expander” graphs. The main motivation of this paper is to investigate the effect of the… (More)

With the arrival of modern internet era, large public networks of various types have come to existence to benefit the society as a whole and several research areas such as sociology, economics and geography in particular. However, the societal and research benefits of these networks have also given rise to potentially significant privacy issues in the sense… (More)

- Bhaskar DasGupta, Nasim Mobasheri
- Discrete Applied Mathematics
- 2017

In this short note, we observe that the problem of computing the strong metric dimension of a graph can be reduced to the problem of computing a minimum node cover of a transformed graph within an additive logarithmic factor. This implies both a 2-approximation algorithm and a (2−ε)-inapproximability for the problem of computing the strong metric dimension… (More)

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