Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections
Abstract Projections of bipartite or two-mode networks capture co-occurrences, and are used in diverse fields (e.g., ecology, economics, bibliometrics, politics) to represent unipartite networks. A key challenge in analyzing such networks is determining whether an observed number of co-occurrences b...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-03238-3 |
| _version_ | 1819096234535682048 |
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| author | Zachary P. Neal Rachel Domagalski Bruce Sagan |
| author_facet | Zachary P. Neal Rachel Domagalski Bruce Sagan |
| author_sort | Zachary P. Neal |
| collection | DOAJ |
| description | Abstract Projections of bipartite or two-mode networks capture co-occurrences, and are used in diverse fields (e.g., ecology, economics, bibliometrics, politics) to represent unipartite networks. A key challenge in analyzing such networks is determining whether an observed number of co-occurrences between two nodes is significant, and therefore whether an edge exists between them. One approach, the fixed degree sequence model (FDSM), evaluates the significance of an edge’s weight by comparison to a null model in which the degree sequences of the original bipartite network are fixed. Although the FDSM is an intuitive null model, it is computationally expensive because it requires Monte Carlo simulation to estimate each edge’s p value, and therefore is impractical for large projections. In this paper, we explore four potential alternatives to FDSM: fixed fill model, fixed row model, fixed column model, and stochastic degree sequence model (SDSM). We compare these models to FDSM in terms of accuracy, speed, statistical power, similarity, and ability to recover known communities. We find that the computationally-fast SDSM offers a statistically conservative but close approximation of the computationally-impractical FDSM under a wide range of conditions, and that it correctly recovers a known community structure even when the signal is weak. Therefore, although each backbone model may have particular applications, we recommend SDSM for extracting the backbone of bipartite projections when FDSM is impractical. |
| first_indexed | 2024-12-21T23:55:57Z |
| format | Article |
| id | doaj.art-b527652371a34aa8a83143569a59051d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-21T23:55:57Z |
| publishDate | 2021-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b527652371a34aa8a83143569a59051d2022-12-21T18:45:49ZengNature PortfolioScientific Reports2045-23222021-12-0111111310.1038/s41598-021-03238-3Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projectionsZachary P. Neal0Rachel Domagalski1Bruce Sagan2Psychology Department, Michigan State UniversityMathematics Department, Michigan State UniversityMathematics Department, Michigan State UniversityAbstract Projections of bipartite or two-mode networks capture co-occurrences, and are used in diverse fields (e.g., ecology, economics, bibliometrics, politics) to represent unipartite networks. A key challenge in analyzing such networks is determining whether an observed number of co-occurrences between two nodes is significant, and therefore whether an edge exists between them. One approach, the fixed degree sequence model (FDSM), evaluates the significance of an edge’s weight by comparison to a null model in which the degree sequences of the original bipartite network are fixed. Although the FDSM is an intuitive null model, it is computationally expensive because it requires Monte Carlo simulation to estimate each edge’s p value, and therefore is impractical for large projections. In this paper, we explore four potential alternatives to FDSM: fixed fill model, fixed row model, fixed column model, and stochastic degree sequence model (SDSM). We compare these models to FDSM in terms of accuracy, speed, statistical power, similarity, and ability to recover known communities. We find that the computationally-fast SDSM offers a statistically conservative but close approximation of the computationally-impractical FDSM under a wide range of conditions, and that it correctly recovers a known community structure even when the signal is weak. Therefore, although each backbone model may have particular applications, we recommend SDSM for extracting the backbone of bipartite projections when FDSM is impractical.https://doi.org/10.1038/s41598-021-03238-3 |
| spellingShingle | Zachary P. Neal Rachel Domagalski Bruce Sagan Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections Scientific Reports |
| title | Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections |
| title_full | Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections |
| title_fullStr | Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections |
| title_full_unstemmed | Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections |
| title_short | Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections |
| title_sort | comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections |
| url | https://doi.org/10.1038/s41598-021-03238-3 |
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