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In considering a social network, there are cases where people is transferred to another place. Then the physical (direct) relations among nodes are lost by the movement. In terms of a network theory, some nodes break the present connections with neighboring nodes, move and there build new connections of nodes. For simplicity we here consider only that two nodes exchange the place each other on a network. Such exchange is assumed to be constantly carried out. We study this dynamic network (node exchange network NEN) and uncover some new features which usual networks do not contain. We mainly consider average path length and the diameter. Lastly we consider a propagation of one virus on the network by a computer simulation. They are compared to other networks investigated hitherto. The relation to a scale free network is also discussed.

In considering a social network, there are cases where people is transferred to another place. Then the physical (direct) relations among nodes are lost by the movement. In terms of a network theory, some nodes break the present connections with neighboring nodes, move and there build new connections of nodes. For simplicity we here consider only that two nodes exchange the place each other on a network. Such exchange is assumed to be constantly carried out. We study this dynamic network (node exchange network NEN) and uncover some new features which usual networks do not contain. We mainly consider average path length and the diameter. Lastly we consider a propagation of one virus on the network by a computer simulation. They are compared to other networks investigated hitherto. The relation to a scale free network is also discussed.

This article is from Frontiers in Psychiatry , volume 5 . Abstract We combined continuous theta-burst stimulation (cTBS) and resting state (RS)-fMRI approaches to investigate changes in functional connectivity (FC) induced by right dorsolateral prefrontal cortex (DLPFC)–cTBS at rest in a group of healthy subjects. Seed-based fMRI analysis revealed a specific pattern of correlation between the right prefrontal cortex and several brain regions: based on these results, we defined a 29-node network to assess changes in each network connection before and after, respectively, DLPFC–cTBS and sham sessions. A decrease of correlation between the right prefrontal cortex and right parietal cortex (Brodmann areas 46 and 40, respectively) was detected after cTBS, while no significant result was found when analyzing sham-session data. To our knowledge, this is the first study that demonstrates within-subject changes in FC induced by cTBS applied on prefrontal area. The possibility to induce selective changes in a specific region without interfering with functionally correlated area could have several implications for the study of functional properties of the brain, and for the emerging therapeutic strategies based on transcranial stimulation.

The Statistical Neural Network Analysis Package (SNNAP) was developed to support research and application of neural network personnel models within the Armstrong Laboratory and other government agencies. The package provides extensive facilities for developing and analyzing networks. It utilizes training heuristics developed in prior research to improve out-of-sample performance of network models. SNNAP provides extensive tools for analyzing and visualizing the structure of trained network models. The report provides an overview of SNNAP facilities and an extensive example using SNNAP to analyze the linkage between task performance, task experience, and airman aptitude.

Due to the unpredictable interruptions within the functions of the human brain, disturbance occurs and it affects the behavior of the human and is equally laid low with the frequent occurrence termed as seizures. Therefore, the proposed system detects the seizure using machine learning algorithms. The electroencephalogram (EEG) contains information of the brain to detect the seizure. The objective is to evaluate the performance of machine learning classifiers K-nearest neighbors (KNN), artificial neural network (ANN), support vector machine (SVM) and principal component analysis (PCA) by comparing the accuracy of the classifier. This work uses total of 11,500 EEG samples from the UCI machine learning repository. The seizure detection was done in two ways. First method, features extracted from the EEG signal and classification techniques are done to classify the seizure. The second method uses the principal component analysis algorithm to improve the significant selections of features from the dataset. The outcomes are analyzed using the statistical package for the social science (SPSS) tools. ANN with extracted functions achieved 96% of accuracy and significant efficiency of (p<0.05) in comparison with different machine learning classifiers. It would be prudent to conclude that the ANN demonstrated the best accuracy, sensitivity, and specificity.

This thesis considers the best use of network traffic data to increase cyber security. This operational problem is one of great concern to network administrators and users generally. Our specific task was performed for the Network Security Division of the Army Research Laboratory, who requested we analyze a dataset of cyber-attacks masked in a background of representative network traffic (the Skaion dataset). We find that substantial preprocessing must done before statistical methods can be applied to raw network data, that no single predictor is sufficient, and that the most effective statistical analysis is logistic regression. Our approach is novel in that we consider not only single predictor events, but also combinations of reports from multiple tools. While we consider a number of different statistical techniques, we find that the most satisfactory model is based on logistic regression. Finally, we conclude that while the Skaion dataset is valuable in terms of its new approach to network traffic emulation, the 1999 KDD Cup and DARPA-MIT datasets-despite their many shortcomings-are more clearly organized and accessible to academic study. Cyber security is a globally important problem and datasets like Skaion's must maximize opportunities for cross-disciplinary academic endeavors.

The Metropolitan Seoul Subway system, consisting of 380 stations, provides the major transportation mode in the metropolitan Seoul area. Focusing on the network structure, we analyze statistical properties and topological consequences of the subway system. We further study the passenger flows on the system, and find that the flow weight distribution exhibits a power-law behavior. In addition, the degree distribution of the spanning tree of the flows also follows a power law.

Through the study of airport network of China (ANC), composed of 128 airports (nodes) and 1165 flights (edges), we show the topological structure of ANC conveys two characteristics of small worlds, a short average path length (2.067) and a high degree of clustering (0.733). The cumulative degree distributions of both directed and undirected ANC obey two-regime power laws with different exponents, i.e., the so-called Double Pareto Law. In-degrees and out-degrees of each airport have positive correlations, whereas the undirected degrees of adjacent airports have significant linear anticorrelations. It is demonstrated both weekly and daily cumulative distributions of flight weights (frequencies) of ANC have power-law tails. Besides, the weight of any given flight is proportional to the degrees of both airports at the two ends of that flight. It is also shown the diameter of each sub-cluster (consisting of an airport and all those airports to which it is linked) is inversely proportional to its density of connectivity. Efficiency of ANC and of its sub-clusters are measured through a simple definition. In terms of that, the efficiency of ANC's sub-clusters increases as the density of connectivity does. ANC is found to have an efficiency of 0.484.

We have performed a statistical analysis of the spatial distribution of operons in the transcriptional regulation network of Escherichia coli. The analysis reveals that operons that regulate each other and operons that are coregulated tend to lie next to each other on the genome. Moreover, these pairs of operons tend to be transcribed in diverging directions. This spatial arrangement of operons allows the upstream regulatory regions to interfere with each other. This affords additional regulatory control, as illustrated by a mean-field analysis of a feed-forward loop. Our results suggest that regulatory control can provide a selection pressure that drives operons together in the course of evolution.

In the e-learning context, social network analysis (SNA) can be used to build understanding around the ways students participate and interact in online forums. This study contributes to the growing body of research that uses statistical methods to test hypotheses about structures in social networks. Specifically, we show how statistical analysis can be used to examine the effect of students' past online interactions on their later participatory behavior, and to synthesize results across multiple networks. [This paper was published in: "Companion Proceedings 8th International Conference on Learning Analytics & Knowledge (LAK18)," 2018.]

The Cuban contact-tracing detection system set up in 1986 allowed the reconstruction and analysis of the sexual network underlying the epidemic (5,389 vertices and 4,073 edges, giant component of 2,386 nodes and 3,168 edges), shedding light onto the spread of HIV and the role of contact-tracing. Clustering based on modularity optimization provides a better visualization and understanding of the network, in combination with the study of covariates. The graph has a globally low but heterogeneous density, with clusters of high intraconnectivity but low interconnectivity. Though descriptive, our results pave the way for incorporating structure when studying stochastic SIR epidemics spreading on social networks.

The Network Control Center (NCC) currently uses the NCC Data System (NCCDS) to schedule customer spacecraft communication requests for the Space Network (SN). The NCC/Request Oriented Scheduling Engine (NCC/ROSE), which implements an operational concept called flexible scheduling, is being tested as a potential replacement for the NCCDS scheduler in an effort to increase the efficiency of the NCC scheduling operations. This paper describes the high fidelity benchmark tests being conducted on NCC/ROSE, the evaluation techniques used to compare schedules, and the results of the tests. This testing will verify the increases in efficiency and productivity that can help the NCC meet the anticipated scheduling loads well into the next century.

In this paper we study the Indian Highway Network as a complex network where the junction points are considered as nodes, and the links are formed by an existing connection. We explore the topological properties and community structure of the network. We observe that the Indian Highway Network displays small world properties and is assortative in nature. We also identify the most important road-junctions (or cities) in the highway network based on the betweenness centrality of the node. This could help in identifying the potential congestion points in the network. Our study is of practical importance and could provide a novel approach to reduce congestion and improve the performance of the highway network

The similarity between protein sequences is a directly and easly computed quantity from which to deduce information about their evolutionary distance and to detect homologous proteins. The SIMAP database -- Similarity Matrix of Proteins -- provides a pre-computed similarity matrix covering the similarity space formed by about all publicly available amino acid sequences from public databases and completely sequenced genomes. From SIMAP we construct the protein homology network, where the proteins are the nodes and the links represent homology relationships. With more than 5 million nodes and about 70 10^9 edges it is the greatest protein homology network ever been builded. We describe the basic features and we perform a global statistical analysis of the network. Starting from the Smith-Waterman similarity score, we define for each edge a weight w to measure the similarity distance between two nodes. Keeping only edges with a weigth greater than a minimal w_min, and by varying w_min we build a family of networks with different degree of similarity. We investigate the distribution of connected components (clusters) of the networks at different w_min and in particular we find a behaviour similar to a phase transition guided by the formation of a giant component. Moreover we study selected sequence features and protein domains of protein pairs that connect different clusters in the networks at different level of similarity. We observed specific, non-random distributions of the protein features and domains for proteins connecting clusters at certain weight intervals.

Extracting information from large graphs has become an important statistical problem since network data is now common in various fields. In this minicourse we will investigate the most natural statistical questions for three canonical probabilistic models of networks: (i) community detection in the stochastic block model, (ii) finding the embedding of a random geometric graph, and (iii) finding the original vertex in a preferential attachment tree. Along the way we will cover many interesting topics in probability theory such as P\'olya urns, large deviation theory, concentration of measure in high dimension, entropic central limit theorems, and more.

This article is from Frontiers in Computational Neuroscience , volume 8 . Abstract Comparing networks in neuroscience is hard, because the topological properties of a given network are necessarily dependent on the number of edges in that network. This problem arises in the analysis of both weighted and unweighted networks. The term density is often used in this context, in order to refer to the mean edge weight of a weighted network, or to the number of edges in an unweighted one. Comparing families of networks is therefore statistically difficult because differences in topology are necessarily associated with differences in density. In this review paper, we consider this problem from two different perspectives, which include (i) the construction of summary networks, such as how to compute and visualize the summary network from a sample of network-valued data points; and (ii) how to test for topological differences, when two families of networks also exhibit significant differences in density. In the first instance, we show that the issue of summarizing a family of networks can be conducted by either adopting a mass-univariate approach, which produces a statistical parametric network (SPN). In the second part of this review, we then highlight the inherent problems associated with the comparison of topological functions of families of networks that differ in density. In particular, we show that a wide range of topological summaries, such as global efficiency and network modularity are highly sensitive to differences in density. Moreover, these problems are not restricted to unweighted metrics, as we demonstrate that the same issues remain present when considering the weighted versions of these metrics. We conclude by encouraging caution, when reporting such statistical comparisons, and by emphasizing the importance of constructing summary networks.

This thesis considers the best use of network traffic data to increase cyber security. This operational problem is one of great concern to network administrators and users generally. Our specific task was performed for the Network Security Division of the Army Research Laboratory, who requested we analyze a dataset of cyber-attacks masked in a background of representative network traffic (the Skaion dataset). We find that substantial preprocessing must done before statistical methods can be applied to raw network data, that no single predictor is sufficient, and that the most effective statistical analysis is logistic regression. Our approach is novel in that we consider not only single predictor events, but also combinations of reports from multiple tools. While we consider a number of different statistical techniques, we find that the most satisfactory model is based on logistic regression. Finally, we conclude that while the Skaion dataset is valuable in terms of its new approach to network traffic emulation, the 1999 KDD Cup and DARPA-MIT datasetsdespite their many shortcomingsare more clearly organized and accessible to academic study. Cyber security is a globally important problem and datasets like Skaions must maximize opportunities for cross-disciplinary academic endeavors.