Small-world network structure mimics spontaneous synchronization in epileptic seizures

Scilight ◽  
2020 ◽  
Vol 2020 (51) ◽  
pp. 511102
Author(s):  
Jodi Ackerman Frank
Keyword(s):  
Network Structure ◽  
Small World ◽  
Epileptic Seizures ◽  
Small World Network

scholarly journals Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song

2019 ◽  
Vol 286 (1917) ◽  
pp. 20192014 ◽  
Author(s):  
Jenny A. Allen ◽  
Ellen C. Garland ◽  
Rebecca A. Dunlop ◽  
Michael J. Noad
Keyword(s):  
Network Structure ◽  
Communication Systems ◽  
Vocal Communication ◽  
Vocal Learning ◽  
Small World ◽  
Structural Features ◽  
Humpback Whales ◽  
Small World Network ◽  
Network Modelling ◽  
Cultural Conformity

Vocal communication systems have a set of rules that govern the arrangement of acoustic signals, broadly defined as ‘syntax’. However, there is a limited understanding of potentially shared or analogous rules across vocal displays in different taxa. Recent work on songbirds has investigated syntax using network-based modelling. This technique quantifies features such as connectivity (adjacent signals in a sequence) and recurring patterns. Here, we apply network-based modelling to the complex, hierarchically structured songs of humpback whales ( Megaptera novaeangliae ) from east Australia. Given the song's annual evolving pattern and the cultural conformity of males within a population, network modelling captured the patterns of multiple song types over 13 consecutive years. Song arrangements in each year displayed clear ‘small-world’ network structure, characterized by clusters of highly connected sounds. Transitions between these connected sounds further suggested a combination of both structural stability and variability. Small-world network structure within humpback songs may facilitate the characteristic and persistent vocal learning observed. Similar small-world structures and transition patterns are found in several birdsong displays, indicating common syntactic patterns among vocal learning in multiple taxa. Understanding the syntactic rules governing vocal displays in multiple, independently evolving lineages may indicate what rules or structural features are important to the evolution of complex communication, including human language.

scholarly journals Developing A Seizure Prediction Algorithm for A Non-Invasive Neuromodulator

2020 ◽  
Vol 5 (6) ◽  
pp. 715-724
Author(s):  
Mahnaz Asgharpour ◽  
Mehdi Sedighi ◽  
Mohammad Reza Jahed Motlagh
Keyword(s):  
Small World ◽  
Epileptic Seizures ◽  
Prediction Algorithm ◽  
Epileptogenic Zone ◽  
Small World Network ◽  
Seizure Prediction ◽  
Non Invasive ◽  
The Mean ◽  
Induced Currents ◽  
Precise Time

In this study, a novel real-time seizure prediction algorithm is introduced to predict epileptic seizures. The proposed algorithm is expected to be applicable in a noninvasive neuromodulator. As a model of the epileptogenic zone, a small-world network of Huber-Braun neurons was built up. To assess the effects of noninvasive stimulation techniques, such as transcranial magnetic stimulation, this network was modified, and the magneto-motive forces and the electromagnetically induced currents were further applied on the network. Comprehensive investigations of the electroencephalograms of epilepsy patients have suggested that some chaotic mechanisms generate the seizures. Hence, chaos and bifurcation theory was applied, and the induced current was considered as the bifurcation parameter. The bifurcation diagram of the 'inter-spike' intervals of the mean voltage of the small world network was obtained. The precise time at which the bifurcation took place was subsequently considered as the time of the seizure onset. Comparisons of the bifurcation diagrams obtained from the patients’ electroencephalographs showed that the proposed network model could reasonably represent the actual neuronal networks of the epileptogenic zone. A dataset of the electroencephalographs of epilepsy patients and normal volunteers from an epilepsy center in Germany was used to validate the prediction algorithm. The simulation results show that the proposed algorithm has a significant capability to predict the precise occurrence of seizures and the achieved sensitivity, accuracy, and specificity of this approach were remarkably higher than those reported in previous studies.

scholarly journals Network Structure in Service Ecosystems: An Empirical Study in the Region of Campania

2019 ◽  
Vol 14 (10) ◽  
pp. 61
Author(s):  
Marco Ferretti ◽  
Eva Panetti ◽  
Adele Parmentola ◽  
Annamaria Sabetta
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Empirical Study ◽  
Network Structure ◽  
Southern Italy ◽  
Small World ◽  
Small World Network ◽  
Regional Service ◽  
Service Ecosystems

The objective of this study is that of exploring the relational dimension of service ecosystems with specific regard to the structure of their networks, by conducting a social network analysis. In particular, this work attempts to primarily unveil which types of network configurations (i.e., open, closed or small words) are typical of service ecosystems. Secondly, we explore the nature of the most central actors in these networks. To these purposes, we conduct an empirical study in the Region of Campania (Southern Italy) by analyzing six regional service ecosystems in different sectors. We gathered data from the PONREC platform (Programma Operativo Nazionale "Ricerca e Competitività" 2007-2013) in order to map links among the actors in all six ecosystems. Main results show that universities and research institutions occupy brokering positions within the service ecosystems’ networks. This, in turn, suggests the efficacy of public regional initiatives in favoring the establishment of forms of collaboration between organizations of different nature. Finally, our findings show that service ecosystems are characterized by open and small world network configurations. This paper contributes to the literature focused on service ecosystems’ networks by providing an empirical and quantitative approach to the analysis of their relational characteristics.

scholarly journals A Genetic Simulated Annealing Algorithm to Optimize the Small-World Network Generating Process

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Haifeng Du ◽  
Jiarui Fan ◽  
Xiaochen He ◽  
Marcus W. Feldman
Keyword(s):  
Simulated Annealing ◽  
Network Structure ◽  
Simulated Annealing Algorithm ◽  
Small World ◽  
Clustering Coefficient ◽  
Small World Network ◽  
Small World Networks ◽  
Experimental Systems ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

Network structure is an important component of analysis in many parts of the natural and social sciences. Optimization of network structure in order to achieve specific goals has been a major research focus. The small-world network is known to have a high average clustering coefficient and a low average path length. Previous studies have introduced a series of models to generate small-world networks, but few focus on how to improve the efficiency of the generating process. In this paper, we propose a genetic simulated annealing (GSA) algorithm to improve the efficiency of transforming other kinds of networks into small-world networks by adding edges, and we apply this algorithm to some experimental systems. In the process of using the GSA algorithm, the existence of hubs and disassortative structure is revealed.

Analysis of Oncogene Protein Structure Using Small World Network Concept

2020 ◽  
Vol 15 (7) ◽  
pp. 732-740
Author(s):  
Neetu Kumari ◽  
Anshul Verma
Keyword(s):  
Amino Acids ◽  
Protein Structure ◽  
Degree Distribution ◽  
Protein Structures ◽  
Small World ◽  
Extreme Condition ◽  
Centrality Measures ◽  
Small World Network ◽  
Network Concept ◽  
Oncogene Protein

Background: The basic building block of a body is protein which is a complex system whose structure plays a key role in activation, catalysis, messaging and disease states. Therefore, careful investigation of protein structure is necessary for the diagnosis of diseases and for the drug designing. Protein structures are described at their different levels of complexity: primary (chain), secondary (helical), tertiary (3D), and quaternary structure. Analyzing complex 3D structure of protein is a difficult task but it can be analyzed as a network of interconnection between its component, where amino acids are considered as nodes and interconnection between them are edges. Objective: Many literature works have proven that the small world network concept provides many new opportunities to investigate network of biological systems. The objective of this paper is analyzing the protein structure using small world concept. Methods: Protein is analyzed using small world network concept, specifically where extreme condition is having a degree distribution which follows power law. For the correct verification of the proposed approach, dataset of the Oncogene protein structure is analyzed using Python programming. Results: Protein structure is plotted as network of amino acids (Residue Interaction Graph (RIG)) using distance matrix of nodes with given threshold, then various centrality measures (i.e., degree distribution, Degree-Betweenness correlation, and Betweenness-Closeness correlation) are calculated for 1323 nodes and graphs are plotted. Conclusion: Ultimately, it is concluded that there exist hubs with higher centrality degree but less in number, and they are expected to be robust toward harmful effects of mutations with new functions.

scholarly journals The structure of co-publications multilayer network

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ghislain Romaric Meleu ◽  
Paulin Yonta Melatagia
Keyword(s):  
Power Law ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Small World ◽  
Generation Model ◽  
Small World Network ◽  
Power Law Distribution ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Scientific Papers

AbstractUsing the headers of scientific papers, we have built multilayer networks of entities involved in research namely: authors, laboratories, and institutions. We have analyzed some properties of such networks built from data extracted from the HAL archives and found that the network at each layer is a small-world network with power law distribution. In order to simulate such co-publication network, we propose a multilayer network generation model based on the formation of cliques at each layer and the affiliation of each new node to the higher layers. The clique is built from new and existing nodes selected using preferential attachment. We also show that, the degree distribution of generated layers follows a power law. From the simulations of our model, we show that the generated multilayer networks reproduce the studied properties of co-publication networks.

Top Museums on Instagram

2019 ◽  
Vol 3 (2) ◽  
pp. 18-42 ◽  
Author(s):  
Vasiliki G. Vrana ◽  
Dimitrios A. Kydros ◽  
Evangelos C. Kehris ◽  
Anastasios-Ioannis T. Theocharidis ◽  
George I. Kavavasilis
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Small World ◽  
Marketing Strategies ◽  
The Other ◽  
Small World Network ◽  
Scale Free ◽  
Centrality Metrics ◽  
Photo Sharing ◽  
Visualization Algorithms

Pictures speak louder than words. In this fast-moving world where people hardly have time to read anything, photo-sharing sites become more and more popular. Instagram is being used by millions of people and has created a “sharing ecosystem” that also encourages curation, expression, and produces feedback. Museums are moving quickly to integrate Instagram into their marketing strategies, provide information, engage with audience and connect to other museums Instagram accounts. Taking into consideration that people may not see museum accounts in the same way that the other museum accounts do, the article first describes accounts' performance of the top, most visited museums worldwide and next investigates their interconnection. The analysis uses techniques from social network analysis, including visualization algorithms and calculations of well-established metrics. The research reveals the most important modes of the network by calculating the appropriate centrality metrics and shows that the network formed by the museum Instagram accounts is a scale–free small world network.

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