Dynamics in Graph Analysis: Adding Time as a Structure for Visual and Statisti
summary
Claims that embedding time as a structural element improves graph analytics and visualization, implemented with NetworkX and Graph Tool and tooling called tribe baline minky. An email graph of 6,174 addresses and 343,000 messages reduces to ~853 vertices by week filters and degree thresholds, turning a hairball into readable subgraphs. An NLP corpus produces 2.6M nodes and 46.9M edges (~20 GB), sampled to 139k nodes; week-ordered subgraphs plus centrality show surprising topical shifts. Graph Tool animations work but are memory‑heavy; subgraphing and time-bounded traversal are the practical wins.
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chapters (11)
- 0:00Introduction
- 1:24Graphs in analytics vs CS
- 3:10Algorithmic performance and visuals
- 8:31Property graph model
- 12:21Modeling time in graphs
- 17:40Time-based traversals and queries
- 22:13Encoding change in graphs
- 26:53Key phrases over time
- 31:30Static graph analytics with time
- 34:43Dynamic graph analytics and centrality
- 36:50Visualization challenges
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