What bibnets Builds | Data Used in This Vignette | Reading Your Own Data | Author Collaboration | Counting Methods | Attention-Style Position Weights | Reference Co-citation | Document Coupling and Citation | Keyword Co-occurrence | Countries, Institutions, and Sources | Generic Co-networks | Normalization | Reducing Large Networks | Temporal Networks | Local Citations and Historiographs | Exporting Results | Interpreting a bibnets_network

1. Introduction and the standard schema | 2. read_biblio() | 3. Worked example — OpenAlex flat CSV | 4. Scopus | 5. Web of Science | 6. OpenAlex — two paths | Path A: in-memory tibble from openalexR | Path B: flat CSV | 7. Dimensions | 8. Lens.org | 9. BibTeX & RIS | 10. Crossref via rcrossref | 11. Generic CSV — read_biblio(format = "generic", ...) | 12. Custom columns and separators (no reader needed) | A separate separator for references | Quoted values | A safety net for the wrong delimiter | 13. Building data manually | 14. The split_field() helper | 15. Combining data from multiple sources | 16. Inspecting and sanity-checking | 17. Troubleshooting | Further reading

Part I: Transition Network Analysis | Theoretical Grounding | Data | Building Networks | Transition Network (TNA) | Frequency Network (FTNA) | Attention Network (ATNA) | Co-occurrence Network from Binary Data | Window-based TNA (WTNA) | Mixed Network (Transitions + Co-occurrences) | Validation | Reliability | Bootstrap Analysis | Centrality Stability | Clustering | Permutation Test for Clusters | Post-hoc Covariate Analysis | Part II: Psychological Network Analysis | Regularized Network (EBICglasso) | References

Introduction | Data | Dissimilarity-based Clustering | Visualizing Clusters | Distance Metrics | Clustering Methods | Choosing k, dissimilarity, and method | Validating the choice with cluster_diagnostics() | Mixture Markov Models | Building Networks per Cluster | From build_clusters() to per-cluster networks | Shortcut: cluster_network() (distance-based) | Shortcut: cluster_mmm() (model-based) | Comparing Clusters | Workflow Summary

Data | Selecting mostly-active learners | Sequence plots | Transition networks | Mean First Passage Times | Stationary distribution | Markov Stability

1. The dataset | 2. Split by time — early vs late interactions | 3. Build the grouped network | 4. Compare patterns between early and late | How to read the residuals | 5. Pyramid plot | 6. Heatmap | 7. Sort by frequency | 9. Note on the test choice

The dataset | 1. type = "heatmap" — clustered carpet | 1.1 Default — LCS distance, ward.D2 dendrogram | 1.2 Switch the sort strategy | 1.3 Cluster separators with k | 1.4 Legend position, custom palette, title | 1.5 Cell borders + tick thinning | 1.6 frame = TRUE brings back the outer box | 2. type = "index" — gap-ready carpet with facets | 2.1 Single panel | 2.2 Visible row gaps | 2.3 Faceted by net_clustering (auto 2×2 for k = 3) | 2.4 Force a 1×3 row | 3. type = "distribution" — state proportions over time | 3.1 Default stacked area | 3.2 Stacked bars, count scale | 3.3 NA band on/off | 3.4 Faceted by cluster | Cheat sheet

What bibnets Builds | Data Used in This Vignette | Reading Your Own Data | Author Collaboration | Counting Methods | Attention-Style Position Weights | Reference Co-citation | Document Coupling and Citation | Keyword Co-occurrence | Countries, Institutions, and Sources | Generic Co-networks | Normalization | Reducing Large Networks | Temporal Networks | Local Citations and Historiographs | Exporting Results | Interpreting a bibnets_network

1. Introduction and the standard schema | 2. read_biblio() | 3. Worked example — OpenAlex flat CSV | 4. Scopus | 5. Web of Science | 6. OpenAlex — two paths | Path A: in-memory tibble from openalexR | Path B: flat CSV | 7. Dimensions | 8. Lens.org | 9. BibTeX & RIS | 10. Crossref via rcrossref | 11. Generic CSV — read_biblio(format = "generic", ...) | 12. Building data manually | 13. The split_field() helper | 14. Combining data from multiple sources | 15. Inspecting and sanity-checking | 16. Troubleshooting | Further reading

What parse_names() is | The three name conventions | Output styles: format | The "parts" attribute | Input shape: vector, not data frame | Recommended workflow: normalise before building | Applying to an existing edgelist (and its hazards) | Limitations | Summary

Data | 1. Genre co-occurrence (delimited field) | Comparing similarity measures | Which similarity to use? | 2. Counting methods | 3. Scaling | 4. Filtering | 5. Actor co-occurrence (long/bipartite format) | 6. Splitting by groups | 7. Output formats | Gephi | cograph (with Gephi layout) | igraph | Matrix | 8. Converters | 9. Six input formats, one result | 10. Complete pipeline | References

Daily EMA — value distribution ticks | Daily EMA — distribution bars | Activity timelines | Line snake | Timeline snake | Tick lines with correlation arcs | Dot plot with dark bands | Mean and median markers | Faceted multi-construct | Survey sequence | Sequential distribution | Sequence snake | Built-in palettes