scispace - formally typeset
Search or ask a question
Topic

Network theory

About: Network theory is a research topic. Over the lifetime, 2257 publications have been published within this topic receiving 109864 citations.


Papers
More filters
Journal ArticleDOI
TL;DR: A review of results from Queueing Network Theory which arose from Dobrushin's ideas and were connected to him in other ways is given in this article, where the authors also comment on various open problems.
Abstract: R.L. Dobrushin (1929-1995) made substantial contributions to Queueing Network Theory (QNT). A review of results from QNT which arose from his ideas or were connected to him in other ways is given. We also comment on various related open problems.

20 citations

Journal ArticleDOI
TL;DR: Results based on simulated scenarios suggest that the statistical power of BioNetStat is less sensitive to the increase of the number of networks than Gene Set Coexpression Analysis (GSCA), and besides being able to identify nodes with modified centralities, BioNetstat identified altered networks associated with signaling pathways that were not identified by other methods.
Abstract: The study of interactions among biological components can be carried out by using methods grounded on network theory. Most of these methods focus on the comparison of two biological networks (e.g., control vs. disease). However, biological systems often present more than two biological states (e.g., tumor grades). To compare two or more networks simultaneously, we developed BioNetStat, a Bioconductor package with a user-friendly graphical interface. BioNetStat compares correlation networks based on the probability distribution of a feature of the graph (e.g., centrality measures). The analysis of the structural alterations on the network reveals significant modifications in the system. For example, the analysis of centrality measures provides information about how the relevance of the nodes changes among the biological states. We evaluated the performance of BioNetStat in both, toy models and two case studies. The latter related to gene expression of tumor cells and plant metabolism. Results based on simulated scenarios suggest that the statistical power of BioNetStat is less sensitive to the increase of the number of networks than Gene Set Coexpression Analysis (GSCA). Also, besides being able to identify nodes with modified centralities, BioNetStat identified altered networks associated with signaling pathways that were not identified by other methods.

20 citations

Journal ArticleDOI
TL;DR: An augmented Boolean pseudo-dynamics approach to a priori determine the critical network interactions in biological interaction networks is developed, which utilises network topology and dynamic state information to determine the set of active pathways.
Abstract: Network theory has established that highly connected nodes in regulatory networks (hubs) show a strong correlation with criticality in network function. Although topological analysis is fully capable of identifying network hubs, it does not provide an objective method for ranking the importance of a particular node by relating its contribution to the overall network response. Towards this end, the authors have developed an augmented Boolean pseudo-dynamics approach to a priori determine the critical network interactions in biological interaction networks. The approach utilises network topology and dynamic state information to determine the set of active pathways. The active pathways are used in conjunction with the key cellular properties of efficiency and robustness, to rank the network interactions based on their importance in the sustenance of network function. To demonstrate the utility of the approach, the authors consider the well characterised guard cell signalling network in plant cells. An integrated analysis of the network revealed the critical mechanisms resulting in stomata closure in the presence and absence of abscisic acid, in excellent agreement with published results.

20 citations

Posted Content
TL;DR: It is shown that the degree and link weight distributions in social networks can be expressed in terms of the dynamical exponents characterizing human activity patterns, and the validity of these theoretical predictions on datasets capturing various facets of human interactions is tested.
Abstract: The increasing availability of large-scale data on human behavior has catalyzed simultaneous advances in network theory, capturing the scaling properties of the interactions between a large number of individuals, and human dynamics, quantifying the temporal characteristics of human activity patterns. These two areas remain disjoint, each pursuing as separate lines of inquiry. Here we report a series of generic relationships between the quantities characterizing these two areas by demonstrating that the degree and link weight distributions in social networks can be expressed in terms of the dynamical exponents characterizing human activity patterns. We test the validity of these theoretical predictions on datasets capturing various facets of human interactions, from mobile calls to tweets.

20 citations


Network Information
Related Topics (5)
Empirical research
51.3K papers, 1.9M citations
73% related
Competitive advantage
46.6K papers, 1.5M citations
71% related
Supply chain
84.1K papers, 1.7M citations
71% related
Organizational learning
32.6K papers, 1.6M citations
70% related
Cluster analysis
146.5K papers, 2.9M citations
70% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202319
202240
202175
2020109
201989
2018115