Unit Disk Graph-Based Node Similarity Index for Complex Network Analysis
We seek to quantify the extent of similarity among nodes in a complex network with respect to two or more node-level metrics (like centrality metrics). In this pursuit, we propose the following unit disk graph-based approach: we first normalize the values for the node-level metrics (using the sum of...
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| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/6871874 |
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| author | Natarajan Meghanathan |
| author_facet | Natarajan Meghanathan |
| author_sort | Natarajan Meghanathan |
| collection | DOAJ |
| description | We seek to quantify the extent of similarity among nodes in a complex network with respect to two or more node-level metrics (like centrality metrics). In this pursuit, we propose the following unit disk graph-based approach: we first normalize the values for the node-level metrics (using the sum of the squares approach) and construct a unit disk graph of the network in a coordinate system based on the normalized values of the node-level metrics. There exists an edge between two vertices in the unit disk graph if the Euclidean distance between the two vertices in the normalized coordinate system is within a threshold value (ranging from 0 tok, where k is the number of node-level metrics considered). We run a binary search algorithm to determine the minimum value for the threshold distance that would yield a connected unit disk graph of the vertices. We refer to “1 − (minimum threshold distance/k)” as the node similarity index (NSI; ranging from 0 to 1) for the complex network with respect to the k node-level metrics considered. We evaluate the NSI values for a suite of 60 real-world networks with respect to both neighborhood-based centrality metrics (degree centrality and eigenvector centrality) and shortest path-based centrality metrics (betweenness centrality and closeness centrality). |
| format | Article |
| id | doaj-art-717af50606d644d9a8c9ee212e215335 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-717af50606d644d9a8c9ee212e2153352025-02-03T07:25:00ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/68718746871874Unit Disk Graph-Based Node Similarity Index for Complex Network AnalysisNatarajan Meghanathan0Professor of Computer Science, Jackson State University, Jackson, MS 39217, USAWe seek to quantify the extent of similarity among nodes in a complex network with respect to two or more node-level metrics (like centrality metrics). In this pursuit, we propose the following unit disk graph-based approach: we first normalize the values for the node-level metrics (using the sum of the squares approach) and construct a unit disk graph of the network in a coordinate system based on the normalized values of the node-level metrics. There exists an edge between two vertices in the unit disk graph if the Euclidean distance between the two vertices in the normalized coordinate system is within a threshold value (ranging from 0 tok, where k is the number of node-level metrics considered). We run a binary search algorithm to determine the minimum value for the threshold distance that would yield a connected unit disk graph of the vertices. We refer to “1 − (minimum threshold distance/k)” as the node similarity index (NSI; ranging from 0 to 1) for the complex network with respect to the k node-level metrics considered. We evaluate the NSI values for a suite of 60 real-world networks with respect to both neighborhood-based centrality metrics (degree centrality and eigenvector centrality) and shortest path-based centrality metrics (betweenness centrality and closeness centrality).http://dx.doi.org/10.1155/2019/6871874 |
| spellingShingle | Natarajan Meghanathan Unit Disk Graph-Based Node Similarity Index for Complex Network Analysis Complexity |
| title | Unit Disk Graph-Based Node Similarity Index for Complex Network Analysis |
| title_full | Unit Disk Graph-Based Node Similarity Index for Complex Network Analysis |
| title_fullStr | Unit Disk Graph-Based Node Similarity Index for Complex Network Analysis |
| title_full_unstemmed | Unit Disk Graph-Based Node Similarity Index for Complex Network Analysis |
| title_short | Unit Disk Graph-Based Node Similarity Index for Complex Network Analysis |
| title_sort | unit disk graph based node similarity index for complex network analysis |
| url | http://dx.doi.org/10.1155/2019/6871874 |
| work_keys_str_mv | AT natarajanmeghanathan unitdiskgraphbasednodesimilarityindexforcomplexnetworkanalysis |