Detection of core-periphery structure in networks using spectral methods and geodesic paths
We introduce several novel and computationally efficient methods for detecting “core– periphery structure” in networks. Core–periphery structure is a type of mesoscale structure that includes densely-connected core vertices and sparsely-connected peripheral vertices. Core vertices tend to be well-co...
| Váldodahkkit: | , , , |
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| Materiálatiipa: | Journal article |
| Almmustuhtton: |
Cambridge University Press
2016
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| _version_ | 1826263202943467520 |
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| author | Cucuringu, M Rombach, P Lee, S Porter, M |
| author_facet | Cucuringu, M Rombach, P Lee, S Porter, M |
| author_sort | Cucuringu, M |
| collection | OXFORD |
| description | We introduce several novel and computationally efficient methods for detecting “core– periphery structure” in networks. Core–periphery structure is a type of mesoscale structure that includes densely-connected core vertices and sparsely-connected peripheral vertices. Core vertices tend to be well-connected both among themselves and to peripheral vertices, which tend not to be well-connected to other vertices. Our first method, which is based on transportation in networks, aggregates information from many geodesic paths in a network and yields a score for each vertex that reflects the likelihood that a vertex is a core vertex. Our second method is based on a low-rank approximation of a network’s adjacency matrix, which can often be expressed as a tensor-product matrix. Our third approach uses the bottom eigenvector of the random-walk Laplacian to infer a coreness score and a classification into core and peripheral vertices. We also design an objective function to (1) help classify vertices into core or peripheral vertices and (2) provide a goodness-of-fit criterion for classifications into core versus peripheral vertices. To examine the performance of our methods, we apply our algorithms to both synthetically-generated networks and a variety of networks constructed from real-world data sets. |
| first_indexed | 2024-03-06T19:47:59Z |
| format | Journal article |
| id | oxford-uuid:22fd3690-4acc-43e9-b4c5-fd538e2a02df |
| institution | University of Oxford |
| last_indexed | 2024-03-06T19:47:59Z |
| publishDate | 2016 |
| publisher | Cambridge University Press |
| record_format | dspace |
| spelling | oxford-uuid:22fd3690-4acc-43e9-b4c5-fd538e2a02df2022-03-26T11:41:49ZDetection of core-periphery structure in networks using spectral methods and geodesic pathsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:22fd3690-4acc-43e9-b4c5-fd538e2a02dfSymplectic Elements at OxfordCambridge University Press2016Cucuringu, MRombach, PLee, SPorter, MWe introduce several novel and computationally efficient methods for detecting “core– periphery structure” in networks. Core–periphery structure is a type of mesoscale structure that includes densely-connected core vertices and sparsely-connected peripheral vertices. Core vertices tend to be well-connected both among themselves and to peripheral vertices, which tend not to be well-connected to other vertices. Our first method, which is based on transportation in networks, aggregates information from many geodesic paths in a network and yields a score for each vertex that reflects the likelihood that a vertex is a core vertex. Our second method is based on a low-rank approximation of a network’s adjacency matrix, which can often be expressed as a tensor-product matrix. Our third approach uses the bottom eigenvector of the random-walk Laplacian to infer a coreness score and a classification into core and peripheral vertices. We also design an objective function to (1) help classify vertices into core or peripheral vertices and (2) provide a goodness-of-fit criterion for classifications into core versus peripheral vertices. To examine the performance of our methods, we apply our algorithms to both synthetically-generated networks and a variety of networks constructed from real-world data sets. |
| spellingShingle | Cucuringu, M Rombach, P Lee, S Porter, M Detection of core-periphery structure in networks using spectral methods and geodesic paths |
| title | Detection of core-periphery structure in networks using spectral methods and geodesic paths |
| title_full | Detection of core-periphery structure in networks using spectral methods and geodesic paths |
| title_fullStr | Detection of core-periphery structure in networks using spectral methods and geodesic paths |
| title_full_unstemmed | Detection of core-periphery structure in networks using spectral methods and geodesic paths |
| title_short | Detection of core-periphery structure in networks using spectral methods and geodesic paths |
| title_sort | detection of core periphery structure in networks using spectral methods and geodesic paths |
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