#### Filter Results:

#### Publication Year

2013

2016

#### Publication Type

#### Co-author

#### Key Phrase

#### Publication Venue

Learn More

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)

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)

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)

Gromov-hyperbolic graphs (or, hyperbolic graphs for short) are a non-trivial interesting classes of " non-expander " graphs. Originally conceived by Gromov in 1987 in a different context while studying fundamental groups of a Riemann surface, the hyperbolicity measure for graphs has recently been a quite popular measure in the network science community in… (More)

δ-hyperbolic graphs, originally conceived by Gromov in 1987, include non-trivial interesting classes of " non-expander " graphs; for fixed δ, such graphs are simply called hyperbolic graphs. Our goal in this paper is to study the effect of the hyperbolicity measure δ on expansion and cut-size bounds on graphs (here δ need not be a constant, i.e., the graph… (More)

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… (More)

- ‹
- 1
- ›