Dynamical complexity in the perception-based network formation model

Many link formation mechanisms for the evolution of social networks have been successful to reproduce various empirical findings in social networks. However, they have largely ignored the fact that individuals make decisions on whether to create links to other individuals based on cost and benefit of linking, and the fact that individuals may use perception of the network in their decision making. In this paper, we study the evolution of social networks in terms of perception-based strategic link formation. Here each individual has her own perception of the actual network, and uses it to decide whether to create a link to another individual. An individual with the least perception accuracy can benefit from updating her perception using that of the most accurate individual via a new link. This benefit is compared to the cost of linking in decision making. Once a new link is created, it affects the accuracies of other individuals' perceptions, leading to a further evolution of the actual network. As for initial actual networks, we consider homogeneous and heterogeneous cases. The homogeneous initial actual network is modeled by Erd\H{o}s-R\'enyi (ER) random networks, while we take a star network for the heterogeneous case. In any cases, individual perceptions of the actual network are modeled by ER random networks with controllable linking probability. Then the stable link density of the actual network is found to show discontinuous transitions or jumps according to the cost of linking. As the number of jumps is the consequence of the dynamical complexity, we discuss the effect of initial conditions on the number of jumps to find that the dynamical complexity strongly depends on how much individuals initially overestimate or underestimate the link density of the actual network. For the heterogeneous case, the role of the highly connected individual as an information spreader is discussed.Comment: 8 pages, 7 figure

Coevolution of a network and perception

How does an individual's cognition change a system which is a collective behavior of individuals? Or, how does a system affect an individual's cognition? To examine the interplay between a system and individuals, we study a cognition-based network formation. When a network is not fully observable, individuals' perception of a network plays an important role in decision making. Assuming that a communication link is costly, and more accurate perception yields higher network utility, an agent decides whether to form a link in order to get better information or not. Changes in a network with newly added links affect individuals' perception accuracy, which may cause further changes in a network. We characterize the early stage of network dynamics and information dispersion. Network structures in a steady state are also examined. Additionally, we discuss local interactions and a link concentration in a frequently changing network.Comment: 32 pages, 8 figure

Self-optimized Coverage Coordination in Femtocell Networks

This paper proposes a self-optimized coverage coordination scheme for two-tier femtocell networks, in which a femtocell base station adjusts the transmit power based on the statistics of the signal and the interference power that is measured at a femtocell downlink. Furthermore, an analytic expression is derived for the coverage leakage probability that a femtocell coverage area leaks into an outdoor macrocell. The coverage analysis is verified by simulation, which shows that the proposed scheme provides sufficient indoor femtocell coverage and that the femtocell coverage does not leak into an outdoor macrocell.Comment: 16 pages, 5 figure

The effect of spatially correlated noise on coherence resonance in a network of excitable cells

We study the effect of spatially correlated noise on coherence resonance (CR) in a Watts-Strogatz small-world network of Fitz Hugh-Nagumo neurons, where the noise correlation decays exponentially with distance between neurons. It is found that CR is considerably improved just by a small fraction of long-range connections for an intermediate coupling strength. For other coupling strengths, an abrupt change in CR occurs following the drastic fracture of the clustered structures in the network. Our study shows that spatially correlated noise plays a significant role in the phenomenon of CR through enforcing the clustering of the network.Comment: 11 pages, 4 figur

Re-presenting a Story by Emotional Factors using Sentiment Analysis Method

Remembering events is affected by personal emotional status. We examined the psychological status, personalfactors, and social factor of undergraduate students (N=64) and got summaries of a story, Chronicle of a Death Foretold fromthem. As transfer learning, we collected 38,265 movie review data to train a sentimental analysis model based on convolutionalneural network, using the model to score each summary. The results of CES-D and PANAS show the relationship betweenemotion and memory retrieval; depressed people have shown a tendency of representing a story more negatively, and seemedless expressive. People with full of emotion have retrieved their memory more expressively, using more negative words. Thecontributions of this study can be summarized as follows: First, we lighten the relationship between emotion and its effects onstoring or retrieving memories. Second, we suggest objective methods to evaluate the intensity of emotion in words, using asentimental analysis model

Network Analysis of Characters’ Relationship in ”Chronicle of Death foretold”using Graph Theory

Over the past decade, there has been an explosion of interest in network analysis research across the social sciencesand computer science. As it is an idea that can be applied in many fields, this study, in particular, its influence in the literature.We present a method for extracting social networks from literature. This study focuses on the relation between novel itself,narration in fiction and was carried out experiments with 89 undergraduate students. They were instructed to write down theirremembered memory of the novel after reading the novel ‘Chronicle of a death foretold.’ We extract features from the socialnetworks of characters in students’ recall story and examine their differentiation with one another, as well as novel’s setting.This study compares graph theory–based cohesion measures characters’ relationship in novel and students’ story. Our resultssuggest an alternative explanation for difference in social networks