Abhijin Adiga

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The largest eigenvalue of the adjacency matrix of a network (referred to as the spectral radius) is an important metric in its own right. Further, for several models of epidemic spread on networks (e.g., the 'flu-like' SIS model), it has been shown that an epidemic dies out quickly if the spectral radius of the graph is below a certain threshold that(More)
Simple diffusion processes on networks have been used to model, analyze and predict diverse phenomena such as spread of diseases, information and memes. More often than not, the underlying network data is noisy and sampled. This prompts the following natural question: how sensitive are the diffusion dynamics and subsequent conclusions to uncertainty in the(More)
The k-core is commonly used as a measure of importance and well connectedness for nodes in diverse applications in social networks and bioinformatics. Since network data is commonly noisy and incomplete, a fundamental issue is to understand how robust the core decomposition is to noise. Further, in many settings, such as online social media networks,(More)
Let G(V, E) be a simple, undirected graph where V is the set of vertices and E is the set of edges. A b-dimensional cube is a Cartesian product I1 × I2 × · · · × I b , where each Ii is a closed interval of unit length on the real line. The cubicity of G, denoted by cub(G) is the minimum positive integer b such that the vertices in G can be mapped to axis(More)
An axis-parallel b-dimensional box is a Cartesian product R1 × R2 × · · · × R b where Ri is a closed interval of the form [ai, bi] on the real line. For a graph G, its boxicity box(G) is the minimum dimension b, such that G is representable as the intersection graph of boxes in b-dimensional space. Although boxicity was introduced in 1969 and studied(More)