Structural measures of similarity and complementarity in complex networks
Abstract The principle of similarity, or homophily, is often used to explain patterns observed in complex networks such as transitivity and the abundance of triangles (3-cycles). However, many phenomena from division of labor to protein-protein interactions (PPI) are driven by complementarity (diffe...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-20710-w |
| _version_ | 1797996087518167040 |
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| author | Szymon Talaga Andrzej Nowak |
| author_facet | Szymon Talaga Andrzej Nowak |
| author_sort | Szymon Talaga |
| collection | DOAJ |
| description | Abstract The principle of similarity, or homophily, is often used to explain patterns observed in complex networks such as transitivity and the abundance of triangles (3-cycles). However, many phenomena from division of labor to protein-protein interactions (PPI) are driven by complementarity (differences and synergy). Here we show that the principle of complementarity is linked to the abundance of quadrangles (4-cycles) and dense bipartite-like subgraphs. We link both principles to their characteristic motifs and introduce two families of coefficients of: (1) structural similarity, which generalize local clustering and closure coefficients and capture the full spectrum of similarity-driven structures; (2) structural complementarity, defined analogously but based on quadrangles instead of triangles. Using multiple social and biological networks, we demonstrate that the coefficients capture structural properties related to meaningful domain-specific phenomena. We show that they allow distinguishing between different kinds of social relations as well as measuring an increasing structural diversity of PPI networks across the tree of life. Our results indicate that some types of relations are better explained by complementarity than homophily, and may be useful for improving existing link prediction methods. We also introduce a Python package implementing efficient algorithms for calculating the proposed coefficients. |
| first_indexed | 2024-04-11T10:10:50Z |
| format | Article |
| id | doaj.art-89d98067db4043a5a72d544e22e5235f |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T10:10:50Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-89d98067db4043a5a72d544e22e5235f2022-12-22T04:30:06ZengNature PortfolioScientific Reports2045-23222022-10-0112111810.1038/s41598-022-20710-wStructural measures of similarity and complementarity in complex networksSzymon Talaga0Andrzej Nowak1Robert Zajonc Institute for Social Studies, University of WarsawFaculty of Psychology, University of WarsawAbstract The principle of similarity, or homophily, is often used to explain patterns observed in complex networks such as transitivity and the abundance of triangles (3-cycles). However, many phenomena from division of labor to protein-protein interactions (PPI) are driven by complementarity (differences and synergy). Here we show that the principle of complementarity is linked to the abundance of quadrangles (4-cycles) and dense bipartite-like subgraphs. We link both principles to their characteristic motifs and introduce two families of coefficients of: (1) structural similarity, which generalize local clustering and closure coefficients and capture the full spectrum of similarity-driven structures; (2) structural complementarity, defined analogously but based on quadrangles instead of triangles. Using multiple social and biological networks, we demonstrate that the coefficients capture structural properties related to meaningful domain-specific phenomena. We show that they allow distinguishing between different kinds of social relations as well as measuring an increasing structural diversity of PPI networks across the tree of life. Our results indicate that some types of relations are better explained by complementarity than homophily, and may be useful for improving existing link prediction methods. We also introduce a Python package implementing efficient algorithms for calculating the proposed coefficients.https://doi.org/10.1038/s41598-022-20710-w |
| spellingShingle | Szymon Talaga Andrzej Nowak Structural measures of similarity and complementarity in complex networks Scientific Reports |
| title | Structural measures of similarity and complementarity in complex networks |
| title_full | Structural measures of similarity and complementarity in complex networks |
| title_fullStr | Structural measures of similarity and complementarity in complex networks |
| title_full_unstemmed | Structural measures of similarity and complementarity in complex networks |
| title_short | Structural measures of similarity and complementarity in complex networks |
| title_sort | structural measures of similarity and complementarity in complex networks |
| url | https://doi.org/10.1038/s41598-022-20710-w |
| work_keys_str_mv | AT szymontalaga structuralmeasuresofsimilarityandcomplementarityincomplexnetworks AT andrzejnowak structuralmeasuresofsimilarityandcomplementarityincomplexnetworks |