Comments (1)
for example, stopping criteria for e.g. PageRank could be different
Currently, igraph does not use power iteration at all for PageRank. Older versions did, but that code was removed, as it was slow, and did not support all features (such as personalization).
Mathematically, PageRank can be formulated either as a linear equation (assuming a damping factor less than 1), or as an eigenvector problem. At the moment, igraph has two methods: it can use the PRPACK library, which uses the linear equation picture, or it can use ARPACK to compute PageRank as an eigenvector.
See also https://igraph.discourse.group/t/implementing-the-ldbc-graphalytics-benchmark/417
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Related Issues (13)
- Automated testing to compare with networkx HOT 1
- COMMUNITY: Regular GraphBLAS Algorithms community conference calls (Open to all)
- Alternative for square counting (for square clustering)
- Improving onboarding and enabling contributions HOT 2
- multi-property graphs and handling of "weight" parameter
- Update README
- Raw vs Wrapped objects
- Add `graph-tool` backend to `scripts/bench.py`
- Logo for `graphblas-algorithms` HOT 3
- square_clustering output using networkx api HOT 14
- `NodeMap` and other results need better reprs
- Redo/refactor calculations and caching of properties such as degrees
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