Prateek Kapadia

Social network analysis of the Short Message Service

In this paper, we analyze patterns in the Short Message Service (SMS) behavior of customers in a large telecom service provider network. Toward this, we construct SMS Graphs, which are graphs induced by people exchanging SMSs, from the SMS Call Detail Records of the concerned service provider. These patterns are modeled by a weighted graph G(V, E, W), in which the vertices represent the customers, and the edges and weights characterize the SMS transactions. We analyze properties of this graph, such as the distribution of component sizes, cliques, and vertex degrees. It is our belief that this study should enable the telecom operators to utilize the social behavior of their customers to design better service plans, and generate optimum incentives.

Interference-constrained coverage algorithms in the protocol and SINR models

AbstractA wireless network’s design must include the optimization of the area of coverage of its wireless transmitters—mobile and base stations in cellular networks, wireless access points in WLANs, or nodes on a transmit schedule in a wireless ad-hoc network. Furthermore, with increasing densities of wireless network deployments, paucity of spectrum, and new developments like whitespace devices and cognitive networks, there is a need to study the computational efficiency of managing interference and optimizing coverage. This work presents new algorithms for computing and optimizing interference-limited coverage of wireless networks under protocol and Signal-to-Interference-and-Noise Ratio (SINR) models. For the protocol model we demonstrate lower bounds on computation of the coverage area for an

Method for facilitating and analyzing social interactions and context for targeted recommendations in a network of a telecom service provider

n

Interference-constrained wireless coverage in a protocol model

n transmitter topology. We first show that any offline computation has a run-time of

Real World Applications of Machine Learning Techniques over Large Mobile Subscriber Datasets

\varOmega (n\log {n})

METHODS AND SYSTEMS FOR PROVIDING MULTIVARIATE TIME SERIES CLUSTERING FOR CUSTOMER SEGMENTATION

Ω(nlogn), and any dynamic update takes