BiMat: a MATLAB package to facilitate the analysis of bipartite networks
Bibliographic record
Abstract
Summary Bipartite networks are ubiquitous in community ecology, including examples of facilitative interactions, such as plant‐pollinator networks, and antagonistic interactions, such as virus–host infection networks. Statistical network analysis is increasingly used to identify emergent, nonrandom patterns of interaction and the effect of interaction patterns on ecological and evolutionary dynamics. Two recurring patterns are that of modularity and nestedness. Modularity is a feature of networks in which there are densely interacting subgroups. Nestedness is a feature of networks in which the interactions form ordered subsets. Here we describe BiMat, an open‐source MATLAB package for the study of the structure of bipartite ecological networks. Unlike alternative tools, BiMat enables both multiscale analysis of the structure of a bipartite ecological network – spanning global (i.e. entire network) to local (i.e. module‐level) scales – and meta‐analyses of many bipartite networks simultaneously. In common with other tools, BiMat calculates the degree to which a bipartite network is modular and/or nested, the statistical significance of these patterns, and enables visualization of latent structures in the network. BiMat is available as an open‐source MATLAB package, with a quick‐start guide and worked examples at: http://bimat.github.io/ .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.134 | 0.054 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".