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The primary aim of the study aims to provide a solution for scheduling examinations for most of the universities and colleges across India which follow the Choice Based Credit System (CBCS) using a graph coloring algorithm. Nowadays, due to the flexibility of opting various subjects, and many students taking up different courses in their colleges and universities, it becomes difficult to schedule these examinations. Creating an examination schedule can be quite challenging and time-consuming for controlling the body of an examination. Our research work focuses on reducing the efforts for scheduling such examinations. With the knowledge of graph theory and graph traversing and coloring algorithms, our algorithm with the help of a few assumptions gives an efficient solution to the examination scheduling problem. A detailed correctness proof along with performance analysis has been done. The efficiency of our proposed algorithm is then compared to that of the coloring algorithm using backtracking. [ABSTRACT FROM AUTHOR]

Network-based research in the management field largely assumes one-mode (unipartite) networks, despite the widespread presence of two-mode (bipartite) networks. In empirical work, scholars usually project a bipartite network onto a unipartite network, ignoring issues related to the interdependence of ties and potential loss of information. Yet new advances in measures and methods related to bipartite networks in the fields of sociology, physics, and biology may make such tactics unnecessary. This article presents an overview of three research streams related to bipartite networks, namely, (a) refinements related to the projections of bipartite networks onto unipartite networks; (b) the extension of networks measures from unipartite networks to bipartite networks, with a focus on clustering coefficients; and (c) approaches unique to bipartite networks, such as nestedness. We apply these approaches and compare the findings of a traditional unipartite network analysis using both a simple example and a sample of 10,223 directors of 1,528 Indian firms in 2009. [ABSTRACT FROM AUTHOR]

Single Spectrum Bipartite Graph (SSBG) model is developed to forecast thunderstorms over Kolkata (22°32′N,88°20′E) during the premonsoon season (April-May). The statistical distribution of normal probability is observed for temperature, relative humidity, convective available potential energy (CAPE), and convective inhibition energy (CIN) to quantify the threshold values of the parameters for the prevalence of thunderstorms.Method of conditional probability is implemented to ascertain the possibilities of the occurrence of thunderstorms within the ranges of the threshold values. The single spectrum bipartite graph connectivitymodel developed in this study consists of two sets of vertices; one set includes two time vertices (00UTC, 12UTC) and the other includes four meteorological parameters: temperature, relative humidity, CAPE, and CIN. Three distinct ranges of maximal eigen values are obtained for the three categories of thunderstorms. Maximal eigenvalues for severe, ordinary, and no thunderstorm events are observed to be (2.6 ± 0.12), (1.88 ± 0.09), and (1.26 ± .03), respectively. The ranges of the threshold values obtained using ten year data (1997-2006) are considered as the reference range and the result is validated with the IMD (India Meteorological Department) observation, DopplerWeather Radar (DWR) Products, and satellite images of 2007. The result reveals that the model provides 12- to 6-hour forecast (nowcasting) of thunderstorms with 96% to 98% accuracy. [ABSTRACT FROM AUTHOR]