Mechanisms for tuning clustering and degree-correlations in directed networks

With complex networks emerging as an effective tool to tackle multidisciplinary problems, models of network generation have gained an importance of their own. These models allow us to extensively analyze the data obtained from real-world networks, study their relevance and corroborate theoretical results. In this work, we introduce methods, based on degree preserving rewiring, that can be used to tune the clustering and degree-correlations in directed networks with random and scale-free topologies. They provide null-models to investigate the role of the mentioned properties along with their strengths and limitations. We find that in the case of clustering, structural relationships, that are independent of topology and rewiring schemes are revealed, while in the case of degree-correlations, the network topology is found to play an important role in the working of the mechanisms. We also study the effects of link-density on the efficiency of these rewiring mechanisms and find that in the case of clustering, the topology of the network plays an important role in determining how link-density affects the rewiring process, while in the case of degree-correlations, the link-density and topology, play no role for sufficiently large number of rewiring steps. Besides the intended purpose of tuning network properties, the proposed mechanisms can also be used as a tool to reveal structural relationships and topological constraints.Comment: 8 pages, 11 figures, submitted to Physical Review

Tracking filter and multi-sensor data fusion

In this paper factorization filtering, fusion filtering strategy and related algorithms are presented. Some results of implementation and validation using realistic data are given

Link deletion in directed complex networks

We present a systematic and detailed study of the robustness of directed networks under random and targeted removal of links. We work with a set of network models of random and scale free type, generated with specific features of clustering and assortativity. Various strategies like random deletion of links, or deletions based on betweenness centrality and degrees of source and target nodes, are used to breakdown the networks. The robustness of the networks to the sustained loss of links is studied in terms of the sizes of the connected components and the inverse path lengths. The effects of clustering and 2-node degree correlations, on the robustness to attack, are also explored. We provide specific illustrations of our study on three real-world networks constructed from protein-protein interactions and from transport data.Comment: 13 pages, 6 figures, submitted to Physica

Do Firms Smooth the Seasonal in Production in a Boom? Theory and Evidence

Using disaggregated production data we show that the size of seasonal cycles changes significantly over the course of the business cycle. In particular, during periods of high economy-wide activity, some industries smooth seasonal fluctuations while others exaggerate them. We interpret this finding using a simple analytical model that describes the conditions under which seasonal and cyclical fluctuations can be separated. Our model implies that seasonal fluctuations can safely be disentangled from cyclical fluctuations only when the marginal cost of production is linear, and the variation in demand and cost satisfy certain (restrictive) conditions. The model also suggests that inventory movements can be used to isolate the role of demand shifts in generating any interaction between seasonal cycles and business cycles. Thus, the empirical analysis involves studying the variation in seasonally unadjusted patterns of production and inventory accumulation over different phases of the business cycle. Our finding that seasonals shrink during booms and that firms carry more inventories into high sales seasons during a boom leads us to conclude that for several industries, marginal cost slopes up at an increasing rate. Conversely, in a couple of industries we find that seasonal swings in production are exaggerated during booms and that inventories are drawn down prior to high sales seasons, suggesting that marginal costs curves flatten as production increases. Overall, we find considerable evidence that there are non-linear interactions between business cycles and seasonal cycles.

Agricultural growth and structural changes in the Punjab economy: an input-output analysis

Agriculture Economic aspects India Punjab., Punjab (India) Economic conditions., Input-output analysis India Punjab.,