Multinet: an interactive program for analysing and visualizing complex networks
Why this work is in the frame
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Bibliographic record
Abstract
MultiNet is a Windows-based computer program designed for exploratory data analysis of social and other networks. MultiNet is highly interactive and always provides both textual and visual representations of results. The visualizations are innovative in the use of colour and interaction, and some are unique to MultiNet. MultiNet was designed from the beginning to handle large amounts of data, and uses compact data formats, special storage schemes, and calculation methods that are highly efficient in terms of both space and time. MultiNet was also designed to handle large numbers of variables, both attribute (node) and network (link); it allows easy construction of new variables of either type by means of various operations on existing ones. Hybrid variables are easily constructed: node variables derived fiom networks; link variables derived fiom attributes. These capabilities provide crucial links among other parts of the program. The application of spectral methods to large, sparse networks is both the theoretical and practical centre of the research and development that has gone into MultiNet. Spectral methods provide analytic visualizations of network data: pictures that not only provide understanding, but that provide numerical values that can be used in further analysis. The results of the spectral methods, as well as other attribute and network data, are used together with simple, standard statistical methods such as cross-tabulations, analysis of variance and correlations for testing hypotheses about relationships among the data. MultiNet provides unique methods that allow attributes and networks to be freely mixed in such analyses, and presents results in both textual and interactive visualizations that include two or three discrete or continuous variables. The largest part of this thesis consists of descriptions of the seven main MultiNet program modules. Supplementary sections describe the theoretical background for spectral analysis and provide specific examples of spectral analysis, including a peer-reviewed, published paper that uses most of the parts of MultiNet together. In addition, a separate CDROM provides a working version ofthe program, electronic documentation, sample datasets, software aids and videos showing how the program is used.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it