arXiv subjects

Sarika Jalan

Publications and source records attributed to Sarika Jalan.

At least 91 records · Page 5Linked to original sources

arXiv2016

Social patterns revealed through random matrix theory

Despite the tremendous advancements in the field of network theory, very few studies have taken weights in the interactions into consideration that emerge naturally in all real world systems. Using random matrix analysis of a weighted social network, we demonstrate the profound impact of weights in interactions on emerging structural properties. The analysis reveals that randomness existing in particular time frame affects the decisions of individuals rendering them more freedom of choice in situations of financial security. While the structural organization of networks remain same throughout all datasets, random matrix theory provides insight into interaction pattern of individual of the society in situations of crisis. It has also been contemplated that individual accountability in terms of weighted interactions remains as a key to success unless segregation of tasks comes into play.

physics.soc-ph↗

arXiv2016

Multilayer network decoding versatility and trust

In the recent years, the multilayer networks have increasingly been realized as a more realistic framework to understand emergent physical phenomena in complex real world systems. We analyze a massive time-varying social data drawn from the largest film industry of the world under multilayer network framework. The framework enables us to evaluate the versatility of actors, which turns out to be an intrinsic property of lead actors. Versatility in dimers suggests that working with different types of nodes are more beneficial than with similar ones. However, the triangles yield a different relation between type of co-actor and the success of lead nodes indicating the importance of higher order motifs in understanding the properties of the underlying system. Furthermore, despite the degree-degree correlations of entire networks being neutral, multilayering picks up different values of correlation indicating positive connotations like trust, in the recent years. Analysis of weak ties of the industry uncovers nodes from lower degree regime being important in linking Bollywood clusters. The framework and the tools used herein may be used for unraveling the complexity of other real world systems.

physics.soc-ph↗

arXiv2016

Dissortativity and duplications in Oral cancer

More than 300,000 new cases worldwide are being diagnosed with oral cancer annually. Complexity of oral cancer renders designing drug targets very difficult. We analyse protein-protein interaction network for the normal and oral cancer tissue and detect crucial changes in the structural properties of the networks in terms of the interactions of the hub proteins and the degree-degree correlations. Further analysis of the spectra of both the networks, while exhibiting universal statistical behavior, manifest distinction in terms of the zero degeneracy, providing insight to the complexity of the underlying system.

q-bio.MN↗

arXiv2015

Emergence of (bi)multi-partiteness in networks having inhibitory and excitatory couplings

(Bi)multi-partite interaction patterns are commonly observed in real world systems which have inhibitory and excitatory couplings. We hypothesize these structural interaction pattern to be stable and naturally arising in the course of evolution. We demonstrate that a random structure evolves to the (bi)multi-partite structure by imposing stability criterion through minimization of the largest eigenvalue in the genetic algorithm devised on the interacting units having inhibitory and excitatory couplings. The evolved interaction patterns are robust against changes in the initial network architecture as well as fluctuations in the interaction weights.

physics.soc-ph↗

arXiv2015

Impact of leader on cluster synchronization

We study the mechanisms of frequency synchronized cluster formation in coupled non-identical oscillators and investigate the impact of presence of a leader on the cluster synchronization. We find that the introduction of a leader, node having large parameter mismatch, induces a profound change in the cluster pattern as well as in the mechanism of the cluster formation. The emergence of a leader generates a transition from the driven to the mixed cluster state. The frequency mismatch turns out to be responsible for this transition. Additionally, for a chaotic evolution, the driven mechanism stands as a primary mechanism for the cluster formation, whereas for a periodic evolution the self-organization mechanism becomes equally responsible.

nlin.CD↗

arXiv2015

Assortative and disassortative mixing investigated using the spectra of graphs

We investigate the impact of degree-degree correlations on the spectra of networks. Even though density distributions exhibit drastic changes depending on the (dis)assortative mixing and the network architecture, the short range correlations in eigenvalues exhibit universal RMT predictions. The long range correlations turn out to be a measure of randomness in (dis)assortative networks. The analysis further provides insight in to the origin of high degeneracy at the zero eigenvalue displayed by majority of the biological networks.

physics.soc-ph↗

arXiv2015

Origin and implications of zero degeneracy in networks spectra

Spectra of real world networks exhibit properties which are different from the random networks. One such property is the existence of a very high degeneracy at zero eigenvalues. In this work, we provide possible reasons behind occurrence of the zero degeneracy in various networks spectra. Comparison of zero degeneracy in protein-protein interaction networks of six different species and in their corresponding model networks sheds light in understanding the evolution of complex biological systems.

cond-mat.dis-nn↗

arXiv2014

Cluster Synchronization in Multiplex Networks

We study the impact of interaction of nodes in a layer of a multiplex network on the dynamical behavior and cluster synchronization of these nodes in other layers. We find that nodes interactions in one layer affects the cluster synchronizability of another layer in many different ways. While multiplexing with a sparse network enhances the synchronizability multiplexing with a dense network suppresses the cluster synchronizability with the network architecture deciding the impact of the enhancement and suppression. Additionally, at weak couplings the enhancement in the cluster synchronizability due to multiplexing remains of the driven type, while for strong couplings the multiplexing may lead to a transition to the self-organized mechanism.

nlin.CD↗

arXiv2014

Interaction patterns in diabetes mellitus II network: An RMT relation

Diabetes mellitus type II affects around 8 percent of the total adult population in the world. It is the fifth leading cause of death in high income countries and an epidemic in developing countries. We analyze protein-protein interaction data of the pancreatic cells for normal and disease states. The analysis exhibits overall structural similarities in the normal and disease networks. The important differences are revealed through specific interaction patterns and eigenvector analyses. The top contributing nodes from localized eigenvectors as well as those being part of specific interaction patterns turn out to be significant for the occurrence of the disease. The analysis provides a direction for further development of novel drugs and therapies in curing the disease by targeting specific patterns instead of a single node.

q-bio.MN↗

arXiv2014

Randomness and preserved patterns in cancer network

Breast cancer has been reported to account for the maximum cases among all female cancers till date. In order to gain a deeper insight into the complexities of the disease, we analyze the breast cancer network and its normal counterpart at the proteomic level. While the short range correlations in the eigenvalues exhibiting universality provide an evidence towards the importance of random connections in the underlying networks, the long range correlations along with the localization properties reveal insightful structural patterns involving functionally important proteins. The analysis provides a benchmark for designing drugs which can target a subgraph instead of individual proteins.

q-bio.MN↗

arXiv2014

Emergence of clustering: Role of inhibition

Though biological and artificial complex systems having inhibitory connections exhibit high degree of clustering in their interaction pattern, the evolutionary origin of clustering in such systems remains a challenging problem. Using genetic algorithm we demonstrate that inhibition is required in the evolution of clique structure from primary random architecture, in which the fitness function is assigned based on the largest eigenvalue. Further, the distribution of triads over nodes of the network evolved from mixed connections exhibits a negative correlation with its degree providing insight into origin of this trend observed in real networks.

cond-mat.dis-nn↗

arXiv2014

Uncovering Randomness and Success in Society

An understanding of how individuals shape and impact the evolution of society is vastly limited due to the unavailability of large-scale reliable datasets that can simultaneously capture information regarding individual movements and social interactions. We believe that the popular Indian film industry, 'Bollywood', can provide a social network apt for such a study. Bollywood provides massive amounts of real, unbiased data that spans more than 100 years, and hence this network has been used as a model for the present paper. The nodes which maintain a moderate degree or widely cooperate with the other nodes of the network tend to be more fit (measured as the success of the node in the industry) in comparison to the other nodes. The analysis carried forth in the current work, using a conjoined framework of complex network theory and random matrix theory, aims to quantify the elements that determine the fitness of an individual node and the factors that contribute to the robustness of a network. The authors of this paper believe that the method of study used in the current paper can be extended to study various other industries and organizations.

physics.soc-ph↗

arXiv2014

Heterogeneous delays making parents synchronized: A coupled maps on Cayley tree model

We study the phase synchronized clusters in the diffusively coupled maps on the Cayley tree networks for heterogeneous delay values. Cayley tree networks comprise of two parts: the inner nodes and the boundary nodes. We find that heterogeneous delays lead to various cluster states, such as; (a) cluster state consisting of inner nodes and boundary nodes, and (b) cluster state consisting of only boundary nodes. The former state may comprise of nodes from all the generations forming self-organized cluster or nodes from few generations yielding driven clusters depending upon on the parity of heterogeneous delay values. Furthermore, heterogeneity in delays leads to the lag synchronization between the siblings lying on the boundary by destroying the exact synchronization among them. The time lag being equal to the difference in the delay values. The Lyapunov function analysis sheds light on the destruction of the exact synchrony among the last generation nodes. To the end we discuss the relevance of our results with respect to their applications in the family business as well as in understanding the occurrence of genetic diseases.

nlin.CD↗

arXiv2014

Quantifying randomness in protein-protein interaction networks of different species: A random matrix approach

We analyze protein-protein interaction networks for six different species under the framework of random matrix theory. Nearest neighbor spacing distribution of the eigenvalues of adjacency matrices of the largest connected part of these networks emulate universal Gaussian orthogonal statistics of random matrix theory. We demonstrate that spectral rigidity, which quantifies long range correlations in eigenvalues, for all protein-protein interaction networks follow random matrix prediction up to certain ranges indicating randomness in interactions. After this range, deviation from the universality evinces underlying structural features in network.

q-bio.MN↗

arXiv2014

Impact of heterogeneous delays on cluster synchronization

We investigate cluster synchronization in coupled map networks in the presence of heterogeneous delays. We find that while parity of heterogeneous delays plays a crucial role in determining the phenomenon of cluster formation, the synchronizability of network predominantly gets affected by the amount of heterogeneity. The heterogeneity in delays induces a rich cluster patterns as compared to the homogeneous delays. The complete bipartite networks stands as an extreme example of this richness, where instead of robust ideal driven clusters, versatile cluster patterns are observed for which we provide arguments using the Lyapunov function analysis. Furthermore, interplay between the number of connections in the network and the amount of heterogeneity has important role in deciding cluster formation.

nlin.CD↗

arXiv2013

Balanced condition in networks leads to Weibull statistics

The importance of the balance in inhibitory and excitatory couplings in the brain has increasingly been realized. Despite the key role played by inhibitory-excitatory couplings in the functioning of brain networks, the impact of a balanced condition on the stability properties of underlying networks remains largely unknown. We investigate properties of the largest eigenvalues of networks having such couplings, and find that they follow completely different statistics when in the balanced situation. Based on numerical simulations, we demonstrate that the transition from Weibull to Fréchet via the Gumbel distribution can be controlled by the variance of the column sum of the adjacency matrix, which depends monotonically on the denseness of the underlying network. As a balanced condition is imposed, the largest real part of the eigenvalue emulates a transition to the generalized extreme value statistics, independent of the inhibitory connection probability. Furthermore, the transition to the Weibull statistics and the small-world transition occur at the same rewiring probability, reflecting a more stable system.

q-bio.NC↗

arXiv2013

Extreme-value statistics of networks with inhibitory and excitatory couplings

Inspired by the importance of inhibitory and excitatory couplings in the brain, we analyze the largest eigenvalue statistics of random networks incorporating such features. We find that the largest real part of eigenvalues of a network, which accounts for the stability of an underlying system, decreases linearly as a function of inhibitory connection probability up to a particular threshold value, after which it exhibits rich behaviors with the distribution manifesting generalized extreme value statistics. Fluctuations in the largest eigenvalue remain somewhat robust against an increase in system size but reflect a strong dependence on the number of connections, indicating that systems having more interactions among its constituents are likely to be more unstable.

cond-mat.dis-nn↗

arXiv2013

Delay-induced driven patterns in coupled Cayley tree networks

We study effects of delay in diffusively coupled logistic maps on the Cayley tree networks. We find that smaller coupling values exhibits sensitiveness for value of delay, and leads to different cluster patterns of self-organized and driven types. Whereas larger coupling strengths are very robust against change in delay values, and leads to stable driven clusters comprising only nodes from last generation of the Calaye tree. Furthermore, introduction of delay exhibits suppression as well as enhancement of synchronization depending upon coupling strength values, hence demonstrating richness of the model. To the end we relate the results with social conflicts and cooperation observed in families.

nlin.CD↗