FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2613478770> ?p ?o ?g. }

• W2613478770 endingPage "91" @default.
• W2613478770 startingPage "74" @default.
• W2613478770 abstract "Build the connection between correlation analysis and community detection.A framework of utilizing correlation analysis to advance community detection.A novel graphic analysis for selecting methods.Solve the resolution problem through the proposed framework. In the era of big data, some data records are interrelated with each other in many areas, such as marketing, management, health care, and education. These interrelated data can be more naturally represented as networks with nodes and edges. Inside this type of networks, there is usually a hidden community structure where each community represents a relatively independent functional module. Such hidden community structures are useful for many applications, such as word-of-mouth marketing, promoting decentralized social interactions inside organizations, and searching biological pathways related to various diseases. Therefore, how to detect hidden community structures becomes an important task with wide applications. Currently, modularity-based methods are widely-used among many existing community structure detection methods. They detect communities with more internal edges than expected under the null hypothesis of independence. Since research in correlation analysis also searches for patterns which occur more than expected under the null hypothesis of independence, this paper proposed a framework of changing the original modularity function according to different existing correlation functions in the correlation analysis research area. Such a framework can utilize not only the current but also the future potential research progresses in correlation analysis to advance community detection. In addition, a novel graphical analysis on different modified-modularity functions is conducted to analyze their different preferences, which are also validated by our evaluation on both real life and simulated networks. Our work to connect modularity-based methods with correlation analysis has several significant impacts on the community detection research and its applications to expert and intelligent systems. First, the research progress in correlation analysis can be utilized to define a more effective objective function in community detection for better detection results since different real-life applications might need communities with different resolutions. Second, any existing research progress for the modularity function, such as the Louvain method for speeding up the search and different extensions for overlapping community detection, can be applied in a similar way to the new objective function derived from existing correlation functions, because the new objective function is unified within one framework with the modularity function. Third, our framework opens a large unexplored area for the researchers interested in community detection. For example, what is the best heuristic search method for each different objective function? What are the characteristics of each objective function when applied to overlapping community detection? Among different extensions to overlapping community detection, which extension is better for each objective function?" @default.
• W2613478770 created "2017-05-19" @default.
• W2613478770 creator A5028827998 @default.
• W2613478770 creator A5031033430 @default.
• W2613478770 creator A5060287641 @default.
• W2613478770 creator A5077625109 @default.
• W2613478770 date "2017-10-01" @default.
• W2613478770 modified "2023-10-09" @default.
• W2613478770 title "Utilizing advances in correlation analysis for community structure detection" @default.
• W2613478770 cites W1059873271 @default.
• W2613478770 cites W1565608089 @default.
• W2613478770 cites W1776448582 @default.
• W2613478770 cites W1971421925 @default.
• W2613478770 cites W1973036367 @default.
• W2613478770 cites W1985625141 @default.
• W2613478770 cites W1991059587 @default.
• W2613478770 cites W1993130246 @default.
• W2613478770 cites W2000644775 @default.
• W2613478770 cites W2008525336 @default.
• W2613478770 cites W2022704179 @default.
• W2613478770 cites W2023655578 @default.
• W2613478770 cites W2024128809 @default.
• W2613478770 cites W2025543856 @default.
• W2613478770 cites W2033403400 @default.
• W2613478770 cites W2038597076 @default.
• W2613478770 cites W2047940964 @default.
• W2613478770 cites W2060149972 @default.
• W2613478770 cites W2062533676 @default.
• W2613478770 cites W2070422353 @default.
• W2613478770 cites W2095012686 @default.
• W2613478770 cites W2098793158 @default.
• W2613478770 cites W2102297485 @default.
• W2613478770 cites W2109726592 @default.
• W2613478770 cites W2122603314 @default.
• W2613478770 cites W2127048411 @default.
• W2613478770 cites W2130377051 @default.
• W2613478770 cites W2131681506 @default.
• W2613478770 cites W2132202037 @default.
• W2613478770 cites W2132914434 @default.
• W2613478770 cites W2155050791 @default.
• W2613478770 cites W2155076541 @default.
• W2613478770 cites W2164928285 @default.
• W2613478770 cites W2335708241 @default.
• W2613478770 cites W2335760940 @default.
• W2613478770 cites W2403905234 @default.
• W2613478770 cites W3101413764 @default.
• W2613478770 cites W3105411815 @default.
• W2613478770 cites W3121640122 @default.
• W2613478770 cites W3126012494 @default.
• W2613478770 cites W4292081303 @default.
• W2613478770 doi "https://doi.org/10.1016/j.eswa.2017.05.010" @default.
• W2613478770 hasPublicationYear "2017" @default.
• W2613478770 type Work @default.
• W2613478770 sameAs 2613478770 @default.
• W2613478770 citedByCount "7" @default.
• W2613478770 countsByYear W26134787702017 @default.
• W2613478770 countsByYear W26134787702018 @default.
• W2613478770 countsByYear W26134787702019 @default.
• W2613478770 countsByYear W26134787702020 @default.
• W2613478770 countsByYear W26134787702022 @default.
• W2613478770 crossrefType "journal-article" @default.
• W2613478770 hasAuthorship W2613478770A5028827998 @default.
• W2613478770 hasAuthorship W2613478770A5031033430 @default.
• W2613478770 hasAuthorship W2613478770A5060287641 @default.
• W2613478770 hasAuthorship W2613478770A5077625109 @default.
• W2613478770 hasConcept C117220453 @default.
• W2613478770 hasConcept C119857082 @default.
• W2613478770 hasConcept C124101348 @default.
• W2613478770 hasConcept C153180895 @default.
• W2613478770 hasConcept C154945302 @default.
• W2613478770 hasConcept C2524010 @default.
• W2613478770 hasConcept C33923547 @default.
• W2613478770 hasConcept C41008148 @default.
• W2613478770 hasConceptScore W2613478770C117220453 @default.
• W2613478770 hasConceptScore W2613478770C119857082 @default.
• W2613478770 hasConceptScore W2613478770C124101348 @default.
• W2613478770 hasConceptScore W2613478770C153180895 @default.
• W2613478770 hasConceptScore W2613478770C154945302 @default.
• W2613478770 hasConceptScore W2613478770C2524010 @default.
• W2613478770 hasConceptScore W2613478770C33923547 @default.
• W2613478770 hasConceptScore W2613478770C41008148 @default.
• W2613478770 hasLocation W26134787701 @default.
• W2613478770 hasOpenAccess W2613478770 @default.
• W2613478770 hasPrimaryLocation W26134787701 @default.
• W2613478770 hasRelatedWork W2961085424 @default.
• W2613478770 hasRelatedWork W3046775127 @default.
• W2613478770 hasRelatedWork W3107474891 @default.
• W2613478770 hasRelatedWork W3170094116 @default.
• W2613478770 hasRelatedWork W3209574120 @default.
• W2613478770 hasRelatedWork W4205958290 @default.
• W2613478770 hasRelatedWork W4286629047 @default.
• W2613478770 hasRelatedWork W4306321456 @default.
• W2613478770 hasRelatedWork W4306674287 @default.
• W2613478770 hasRelatedWork W4224009465 @default.
• W2613478770 hasVolume "84" @default.
• W2613478770 isParatext "false" @default.
• W2613478770 isRetracted "false" @default.
• W2613478770 magId "2613478770" @default.

• next