arXiv subjects

Anurag Singh

Publications and source records attributed to Anurag Singh.

67 records · Page 4Linked to original sources

arXiv2018

Analysing the anisotropy in morphological evolution and readjustment effects in cluster-cluster aggregation of AuNPs using Shannon entropy

We have used information theory analogue of entropy, Shannon entropy, for estimating the variations during the isotropic and anisotropic AuNP fractal growth process. We have firstly applied the Shannon entropy on the simulated fractal aggregates obtained from DLA model with noise reduction scheme. In conventional noise reduction scheme used in past, the growth process of identical particles was performed and no effect of the evolving cluster on the incoming particle was considered, hence the noise is reduced in discrete amount and do not account for the noise fluctuations present during the morphological evolution of the fractals. The Shannon entropy is shown to capture the emergence of the anisotropic morphological evolution. The imaging tool was further found to be promising for capturing the readjustment effects during cluster-cluster aggregation.

physics.atm-clus↗

arXiv2018

Effect of correlation on the traffic capacity of Time Varying Communication Network

The network topology and the routing strategy are major factors to affect the traffic dynamics of the network. In this work, we aim to design an optimal time-varying network structure and an efficient route is allocated to each user in the network. The network topology is designed by considering addition, removal, and rewiring of links. At each time instants, a new node connects with an existing node based on the degree and correlation with its neighbor. Traffic congestion is handled by rewiring of some congested links along with the removal of the anti-preferential and correlated links. Centrality plays an important role to find the most important node in the network. The more a node is central, the more it can be used for the shortest route of the user pairs and it can be congested due to a large number of data coming from its neighborhood. Therefore, routes of the users are selected such that the sum of the centrality of the nodes appearing in the user's route is minimum. Thereafter, we analyze the network structure by using various network properties such as the clustering coefficient, centrality, average shortest path, rich club coefficient, average packet travel time and order parameter.

cs.NI↗

arXiv2018

Fair End to End Window Based Congestion Control in Time Varying Data Communication Networks

Communication networks are time-varying and hence, fair sharing of network resources among the users in such a dynamic environment is a challenging task. In this context, a time-varying network model is designed and the shortest user's route is found. In the designed network model, an end to end window-based congestion control scheme is developed with the help of internal nodes or router and the end user can get implicit feedback (RTT and throughput). This scheme is considered as fair if the allocation of resources among users minimizes overall congestion or backlog in the networks. Window update approach is based on a multi-class fluid model and is updated dynamically by considering delays (communication, propagation and queuing) and the backlog of packets in the user's routes. Convergence and stability of the window size are obtained using a Lyapunov function. A comparative study with other window-based methods is also provided.

cs.NI↗

arXiv2018

Effect of correlations on routing and modeling of Time Varying Communication Networks

Most of the real world networks are complex as well as evolving. Therefore, it is important to study the effect of network topology on the dynamics of traffic and congestion in the network. To account this problem, we have designed a time-varying network model where a new node will join a node in the existing network with probability proportional to its degree and disassortativity with its neighbors. Betweenness centrality (BC) plays an important role to find the influential node and user's shortest route in the network. As shortest route comprised of hub nodes and chances of congestion is more on these nodes. Hence, BC-BC correlation is used to find user's route. A connection between two hub nodes reduces the data forwarding capacity of connecting link with higher probability. If a node shows disassortativity with its neighbors then it may forward more packets and may be chosen for routing. Furthermore, user's optimal data sending rate as well as critical packet generation rate of the proposed model is calculated and shown improved results in comparison than the classical scale-free network model.

cs.NI↗

arXiv2018

Neighborhood Complexes of Kneser Graphs, $KG_{3,k}$

In this article, we prove that the neighborhood complex of the Kneser graph $KG_{3,k}$ is of the same homotopy type as that of a wedge of $\frac{(k+1)(k+3)(k+4)(k+6)}{4}+1$ spheres of dimension $k$. We construct a maximal subgraph $S_{3,k}$ of $KG_{3,k}$, whose neighborhood complex deformation retracts onto the neighborhood complex of $SG_{3,k}$.

math.CO↗

arXiv2018

Modelling Competitive marketing strategies in Social Networks

In a competitive marketing, there are a large number of players which produce the same product. Each firm aims to diffuse its product information widely so that it's product will become popular among potential buyers. The more popular is a product of a firm, the higher is the revenue for the firm. A model is developed in which two players compete to spread information in the large network. Players choose their initial seed nodes simultaneously and the information is diffused according to Independent Cascade model (ICM). The main aim of the player is to choose the seed nodes such that they will spread its information to as many nodes as possible in a social network. The rate of spreading of information also plays a very important role in information diffusion process. Any node in a social network will get influenced by none or one or more than one information. We also analyzed how much fraction of nodes in different compartment changes by changing the rate of spreading of information. Finally, a game theory model is developed to obtain the Nash equilibrium based on best response function of the players. This model is based on Hotelling's model of electoral competition.

cs.SI↗

arXiv2018

Homotopy type of Neighborhood Complexes of Kneser graphs, $KG_{2,k}$

Schrijver identified a family of vertex critical subgraphs of the Kneser graphs called the stable Kneser graphs $SG_{n,k}$. Björner and de Longueville proved that the neighborhood complex of the stable Kneser graph $SG_{n,k}$ is homotopy equivalent to a $k-$sphere. In this article, we prove that the homotopy type of the neighborhood complex of the Kneser graph $KG_{2,k}$ is a wedge of $(k+4)(k+1)+1$ spheres of dimension $k$. We construct a maximal subgraph $S_{2,k}$ of $KG_{2,k}$, whose neighborhood complex is homotopy equivalent to the neighborhood complex of $SG_{2,k}$. Further, we prove that the neighborhood complex of $S_{2,k}$ deformation retracts onto the neighborhood complex of $SG_{2,k}$.

math.CO↗

arXiv2017

Locating transition path region in the free energy landscape of protein folding

Protein folding processes are generally described statistically with the help of multidimensional free energy landscape, typically reduced to a 1-D free energy profile along good reaction co-ordinate. There are many physical parameters which are responsible for protein molecule to hop between the native and unfolded states. The transition path region across the barrier is the region corresponding to the minimum fluctuation. The extent to which this transition region can extend beyond the obvious 1/2 kBT region has been a question of interest for a long time. We propose a new method to locate this transition path region and to study its dependence on the asymmetry of the transition state for a given free energy landscape. We have performed Brownian dynamics simulations with Gaussian white noise and Monte-Carlo simulation by sampling ten thousand successful transitions across the barrier for three different energy landscape having fixed barrier height with asymmetry in their curvatures. It was found that the transition path region increases with the increase in the asymmetry of the energy landscape in the transition region. The rate limiting parameter diffusion constant over the diffusive barrier, rate constant at particular force and transition path time for different potentials were estimated directly from the landscape profile using Kramers theory for diffusive barrier crossing. It was also found that the average diffusion in the native state increases with the increase in the asymmetry of the transition state towards the non-native state.

physics.bio-ph↗

arXiv2017

Change in flexibility of DNA with binding ligands

The percentage and sequence of AT and GC base pairs and charges on the DNA backbone contribute significantly to the stiffness of DNA. This elastic property of DNA also changes with small interacting ligands. The single-molecule force spectroscopy technique shows different interaction modes by measuring the mechanical properties of DNA bound with small ligands. When a ds-DNA molecule is overstretched in the presence of ligands, it undergoes a co-operative structural transition based on the externally applied force, the mode of binding of the ligands, the binding constant of the ligands to the DNA, the concentration of the ligands and the ionic strength of the supporting medium. This leads to the changes in the regions- upto 60 pN, cooperative structural transition region and the overstretched region, compared to that of the FEC in the absence of any binding ligand. The cooperative structural transitions were studied by the extended and twistable worm-like chain model. Here we have depicted these changes in persistence length and the elastic modulus constant as a function of binding constant and the concentration of the bound ligands, which vary with time. Therefore, besides ionic strength, interacting proteins and content of AT and GC base pairs, the ligand binding or intercalation with the ligands is an important parameter which changes the stiffness of DNA.

cond-mat.soft↗

arXiv2017

Modeling and Performance Studies of Data Communication Networks using Dynamic Complex Networks

All the existing real world networks are evolving, hence, study of traffic dynamics in these enlarged networks is a challenging task. The critical issue is to optimize the network structure to improve network capacity and avoid traffic congestion. We are interested in taking user's routes such that it is least congested with optimal network capacity. Network capacity may be improved either by optimizing network topology or enhancing in routing approach. In this context, we propose and design a model of the time varying data communication networks (TVCN) based on the dynamics of in-flowing links. Newly appeared node prefers to attach with most influential node present in the network. In this paper, influence is termed as \textit{reputation} and is applied for computing overall congestion at any node. User path with least betweenness centrality and most reputation is preferred for routing. Kelly's optimization formulation for a rate allocation problem is used for obtaining optimal rates of distinct users at different time instants and it is found that the user's path with lowest betweenness centrality and highest reputation will always give maximum rate at stable point.

cs.NI↗

arXiv2016

Centrality Measures for Networks with Community Structure

Understanding the network structure, and finding out the influential nodes is a challenging issue in the large networks. Identifying the most influential nodes in the network can be useful in many applications like immunization of nodes in case of epidemic spreading, during intentional attacks on complex networks. A lot of research is done to devise centrality measures which could efficiently identify the most influential nodes in the network. There are two major approaches to the problem: On one hand, deterministic strategies that exploit knowledge about the overall network topology in order to find the influential nodes, while on the other end, random strategies are completely agnostic about the network structure. Centrality measures that can deal with a limited knowledge of the network structure are required. Indeed, in practice, information about the global structure of the overall network is rarely available or hard to acquire. Even if available, the structure of the network might be too large that it is too much computationally expensive to calculate global centrality measures. To that end, a centrality measure is proposed that requires information only at the community level to identify the influential nodes in the network. Indeed, most of the real-world networks exhibit a community structure that can be exploited efficiently to discover the influential nodes. We performed a comparative evaluation of prominent global deterministic strategies together with stochastic strategies with an available and the proposed deterministic community-based strategy. Effectiveness of the proposed method is evaluated by performing experiments on synthetic and real-world networks with community structure in the case of immunization of nodes for epidemic control.

cs.SI↗

arXiv2014

Community-based Immunization Strategies for Epidemic Control

Understanding the epidemic dynamics, and finding out efficient techniques to control it, is a challenging issue. A lot of research has been done on targeted immunization strategies, exploiting various global network topological properties. However, in practice, information about the global structure of the contact network may not be available. Therefore, immunization strategies that can deal with a limited knowledge of the network structure are required. In this paper, we propose targeted immunization strategies that require information only at the community level. Results of our investigations on the SIR epidemiological model, using a realistic synthetic benchmark with controlled community structure, show that the community structure plays an important role in the epidemic dynamics. An extensive comparative evaluation demonstrates that the proposed strategies are as efficient as the most influential global centrality based immunization strategies, despite the fact that they use a limited amount of information. Furthermore, they outperform alternative local strategies, which are agnostic about the network structure, and make decisions based on random walks.

cs.SI↗

arXiv2012

Nonlinear spread of rumor and inoculation strategies in the nodes with degree dependent tie strength in complex networks

In earlier rumor spreading models, at each time step nodes contact all of their neighbors. In more realistic scenario it is possible that a node may contact only some of its neighbors to spread the rumor. Therefore it is must in real world complex networks, the classic rumor spreading model need to be modified to consider the dependence of rumor spread rate on the degree of the spreader and the informed nodes. We have given a modified rumor spreading model to accommodate these facts. This new model, has been studied for rumor spreading in complex networks in this work. Nonlinear rumor spread exponent $α$ and degree dependent tie strength exponent $β$ in any complex network gives rumor threshold as some finite value. In the present work, the modified rumor spreading model has been studied in scale free networks. It is also found that if $ α$ and $ β$ parameters are tuned to appropriate value, the rumor threshold becomes independent of network size. In any social network, rumors can spread may have undesirable effect. One of the possible solutions to control rumor spread, is to inoculate a certain fraction of nodes against rumors. The inoculation can be done randomly or in a targeted fashion. We have used modified rumor spreading model over scale free networks to investigate the efficacy of inoculation. Random and targeted inoculation schemes have been applied. It has been observed that rumor threshold in random inoculation scheme is greater than the rumor threshold in the model without any inoculation scheme. But random inoculation is not that much effective. The rumor threshold in targeted inoculation is very high than the rumor threshold in the random inoculation in suppressing the rumor.

cs.SI↗