Community Detection in Graphs.¶
Fortunato, S. (2010). Community Detection in Graphs. Physics Reports, 486(3), 3-5.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Modifiable Areal Unit Problem
- In network science, modular structure is partition-dependent, with resolution-parameterized community-detection algorithms exposing it as varying with a resolution choice.
This sourceReviews community detection and modularity, including resolution-dependence: detected community structure varies with the resolution parameter chosen.
SupportedVerified against the work's full text
Fortunato's review describes multiresolution community detection whose tunable resolution parameter (e.g. gamma, alpha, time) sets the scale of the clusters found.
“In general, multiresolution methods have a freely tun- able parameter, that allows to set the characteristic size of the clusters to be detected.”
- In network science, modular structure is partition-dependent, with resolution-parameterized community-detection algorithms exposing it as varying with a resolution choice.
- Partition Dependence of Aggregates
- The transfer has moved historically from spatial statistics into epidemiology (cluster detection, rate comparison across administrative levels), network science (resolution-limit literature, multi-resolution community detection), causal inference (Simpson's paradox, the distinction between conditioning on and marginalising over partition variables), machine learning (clustering stability, ensemble clustering, consensus partitions), macroeconometrics (time-aggregation bias, temporal disaggregation), and political science (mathematical redistricting, compactness criteria).
This sourceComprehensive review establishing that detected modular structure is partition-/resolution-dependent on the same graph.
Supported in partVerified against the work's full text
Fortunato's review documents the resolution limit and multiresolution methods in network science, but covers none of the other fields or the historical transfer the sentence describes.
“The conclusion is striking: modularity optimization has a resolution limit that may prevent it from detecting clusters which are comparatively small with respect to the graph as a whole”
- The transfer has moved historically from spatial statistics into epidemiology (cluster detection, rate comparison across administrative levels), network science (resolution-limit literature, multi-resolution community detection), causal inference (Simpson's paradox, the distinction between conditioning on and marginalising over partition variables), machine learning (clustering stability, ensemble clustering, consensus partitions), macroeconometrics (time-aggregation bias, temporal disaggregation), and political science (mathematical redistricting, compactness criteria).
Domain-specific¶
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Read against the text for 2 of 3 citations: 1 supported, 1 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Registry ID ref:6a34f7780e34 · see in the full table