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DocsModulesNetworksSocial, Knowledge & Time

Social, Knowledge, and Time

These features sit on the same /networks/ workspace. Switch graph mode (left) before generate, or use the timeline once a time field is mapped.

Social network analysis

Social mode replaces the standard centrality list with SNA-oriented jobs (POST /api/graphtheory/graphs/{id}/social-analysis):

OptionRole
Influence scoreCombined PageRank + betweenness style influence
Bridge scoreStructural holes / brokerage
ReciprocityMutual directed ties
ClusteringLocal clustering
Friend recommendationsSuggest links (count + min mutual friends)
Echo chambersHigh in-group density vs out-group (threshold)
Influence pathsBounded-length paths (max length, min strength)
Structural holesMin bridge score filter

Results appear in the social metrics table, not only as canvas colors.

Knowledge graphs

Knowledge mode treats vertices as typed entities and edges as relations.

  • Load a built-in ontology (example: academic Person / Paper / Institution) or auto-generate from the current graph
  • Validate schema against entity/relation types
  • Apply inference (max iterations) for transitive / implied links
  • Query related entities, semantic path, and custom knowledge queries

APIs live under /api/graphtheory/graphs/{id}/knowledge/*. This ontology is your domain graph, not the Runtime closed-loop catalog at /ontology/runtime.

Timeline and temporal algorithms

Map a time field and a bracket (minutes, hours, days, months, years). The canvas shows a histogram of buckets (server $dateTrunc; auto-coarser if the range would exceed 500 buckets).

Click a bucket (for example Jan 2023) to filter. From the timeline, run a temporal category:

CategoryExample algorithms
Structural shiftDeltaCon, spectral distance, BOCPD, PELT/CUSUM, top eigenspace
Influence rise/fallPageRank over periods, k-core evolution, EWMA, rank-change tests
Community evolutionmultilayer Leiden, label matching, event taxonomy, persistence/conductance
Tie formation / decaylink-prediction heuristics, gradient-boosted ranker, Cox PH, prediction evaluation
Cascade explosivenessHawkes, cascade metrics, changepoint

Large corpora should send largeMode plus timeField and period bounds so the job does not stream the whole history into the browser.

Geo and analytics drawer

Optional settings on the saved network:

  • Degree-distribution Chart.js widgets
  • Natural-language charts via Workbench /api/v1/analytics
  • Google Maps geo / earth charts (VITE_GOOGLE_MAPS_API_KEY / env-config.js) when lat/lng fields (or geocoded address) exist

Writeback and export

After analysis:

Writeback targetEffect
sourceMerge metrics onto existing Mongo documents (_network_metrics prefix by default)
exportOne score document per node in a target collection
full_exportCopy full source records plus scores

Export on the toolbar downloads ecosystem-network-v1 JSON (mappings + settings). Import restores a model into network_configs.

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