Dependency network (original) (raw)
The dependency network approach provides a system level analysis of the activity and topology of directed networks. The approach extracts causal topological relations between the network's nodes (when the network structure is analyzed), and provides an important step towards inference of causal activity relations between the network nodes (when analyzing the network activity). This methodology has originally been introduced for the study of financial data, it has been extended and applied to other systems, such as the immune system, and semantic networks.
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dbo:abstract | The dependency network approach provides a system level analysis of the activity and topology of directed networks. The approach extracts causal topological relations between the network's nodes (when the network structure is analyzed), and provides an important step towards inference of causal activity relations between the network nodes (when analyzing the network activity). This methodology has originally been introduced for the study of financial data, it has been extended and applied to other systems, such as the immune system, and semantic networks. In the case of network activity, the analysis is based on partial correlations, which are becoming ever more widely used to investigate complex systems. In simple words, the partial (or residual) correlation is a measure of the effect (or contribution) of a given node, say j, on the correlations between another pair of nodes, say i and k. Using this concept, the dependency of one node on another node is calculated for the entire network. This results in a directed weighted adjacency matrix of a fully connected network. Once the adjacency matrix has been constructed, different algorithms can be used to construct the network, such as a threshold network, Minimal Spanning Tree (MST), Planar Maximally Filtered Graph (PMFG), and others. (en) |
dbo:thumbnail | wiki-commons:Special:FilePath/Dependency_network_for_financial_data.jpg?width=300 |
dbo:wikiPageExternalLink | http://www.ocs.unipa.it https://archive.today/20121218203818/http:/www.futurict.org.il/ http://www.unifr.ch/econophysics/ |
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dbo:wikiPageWikiLink | dbr:Partial_correlation dbr:Semantic_lexicon dbr:Dependency_network_(graphical_model) dbr:Complex_system dbr:Complex_systems dbr:Correlation dbr:Graph_(discrete_mathematics) dbr:Topology dbr:Economic_sectors dbr:Adjacency_matrix dbr:Pearson_product-moment_correlation_coefficient dbr:Hierarchical_clustering dbc:Network_analysis dbr:New_York_Stock_Exchange dbr:Semantic_networks dbr:Immune_system dbr:PLoS_ONE dbr:Financial_sector dbr:Minimal_spanning_tree dbr:Network_node dbr:File:Dependency_network_for_financial_data.jpg dbr:File:Dependency_network_for_immune_system_data.jpg dbr:File:Dependency_network_for_semantic_data.jpg dbr:Wikt:hub |
dbp:cs1Dates | ly (en) |
dbp:date | April 2020 (en) |
dbp:wikiPageUsesTemplate | dbt:Reflist dbt:Use_dmy_dates |
dct:subject | dbc:Network_analysis |
rdfs:comment | The dependency network approach provides a system level analysis of the activity and topology of directed networks. The approach extracts causal topological relations between the network's nodes (when the network structure is analyzed), and provides an important step towards inference of causal activity relations between the network nodes (when analyzing the network activity). This methodology has originally been introduced for the study of financial data, it has been extended and applied to other systems, such as the immune system, and semantic networks. (en) |
rdfs:label | Dependency network (en) |
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