HAC-Q network clustering
Algorithms AIMech:HACQ DRAFT
Aliases: HAC-Q
A hierarchical network-clustering variant that uses a degree-corrected modularity contribution to guide group merges and retains hierarchical group structure for link prediction rather than using only a modularity-selected flat partition.
Identity Notes & Concept Scope
Compared all 442 pinned AIO and 669 native IDs, names, aliases and scoped synonyms for the three candidates and publication identifiers; no normalized identity match. Ignored- and hidden-inclusive fixed-string searches of the recovery, older discovery and original Documents checkouts returned 158, 0 and 5 lines. All 158 public matches were inspected and belong to source/discovery/review context and rendered existing records, not these maintained identities. The five original-checkout matches are unrelated Spanish hace text in ignored dependencies. Semantic assessment covered 46 AIO and 264 native definitions: 44 AIO and 260 native rows were unchanged from prior reading; two additional AIO and four additional native definitions were read. Sixteen complete annotated AIO node/edge/generated contexts and the candidate-specific native records were assessed. Retain the source-named HAC-Q hierarchical link-prediction variant. Its modularity merge criterion is closely related to CNM, as the paper explicitly states; retaining hierarchical density estimates is distinct from using only a modularity-maximizing flat cut. No claim of a new modularity objective is made.
Claims & Grounded Evidence
5 Claims
Mechanism
#modularity-merge-guide
The merge score sums observed cross-group adjacency minus the degree-product expectation under the graph’s degree-based null model. Equivalently it is the observed edge count minus the product of the two groups’ total degrees divided by twice the number of edges. Unlike edge density, it depends on the graph-wide edge total and vertex degrees.
Scope
#cnm-and-gain-boundary
The authors describe the score-driven algorithm as essentially CNM while emphasizing a different retained representation for link prediction. Under the standard normalized undirected modularity convention, the normalized modularity change for merging two fixed groups is this score divided by the graph’s number of edges. Greedy local gain is not a guarantee of a globally optimal partition or hierarchy.
Scope
#hierarchy-versus-flat-prediction
For hierarchical methods the study estimates densities across the two sides of tree joins; for CNM it estimates within- and between-group densities for the selected flat clustering. Retaining a hierarchy can therefore produce different link estimates even when related merge criteria are used. A finite tree-versus-partition example illustrates this representation distinction conditionally, without asserting uninspected stopping or collapse behavior.
Limitation
#score-domain-boundary
The degree-corrected score may be negative, and its printed degree-product denominator is undefined for a graph with no edges. It is a merge contribution rather than an edge probability. The record does not invent handling of edgeless graphs, negative-gain completion or implementation-specific tie and termination policies.
Scope
#evaluation-scope
The study compares this named variant with HAC-ML and other methods on biological interaction networks using held-out-edge prediction. Positive test pairs are balanced by an equal number of sampled holes, and maximum F scores summarize ranked predictions. This selection changes the negative prevalence relative to deployment. The reported comparisons support a setting-specific empirical assessment, not universal superiority or calibrated real-world link probabilities.
⚠️ Curated Limitations & Negative Results
- HAC-Q does not introduce a distinct general modularity objective. Its source-defined scope preserves hierarchical representation for link prediction, separately from the CNM flat-cut baseline.
- The three-vertex path calculation is a conditional mathematical example, not a claim about the paper’s degree-filtered evaluation network or about whether its uninspected code would retain that entire binary tree.
- This record covers a named 2011 score variant in Park and Bader’s network-clustering study, not a fitted interactome or the entire family of agglomerative clustering methods.
- The paper defines each alternative by replacing the merge guide. It does not separately specify every candidate-restriction, tie, stopping and terminal-collapse convention for each variant. The detailed HAC-ML implementation and its complexity are not automatically asserted for this variant.
- The experiments use undirected, unweighted interaction graphs with iterative removal of isolated and degree-one vertices. Missing observed edges can include unobserved biological interactions; sampled holes are not established biological negatives.
- No author implementation, data or supplementary archive was audited or executed. Finite constructed checks assess local scores and algebraic distinctions, not complete clustering runs, benchmark replication or global optimality. DRAFT remains.
Artificial Intelligence Ontology (AIO) Grounding
Mapped AIO Classes
| AIO Target Class | Mapping Relation | Mapping Rationale |
|---|---|---|
AIO:HierarchicalClustering
Hierarchical Clustering
|
related_match | This algorithm constructs a hierarchy of vertex groups. The target is a related task, not an equivalent algorithm or asserted superclass. |
AIO:UnsupervisedClustering
Unsupervised Clustering
|
related_match | The procedure groups graph vertices using observed relationships rather than supervised label prediction. The task relation does not establish algorithm identity. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
park2011
|
Yongjin Park, Joel S Bader
|
primary paper
full_text
|
ID: doi:10.1186/1471-2105-12-S1-S44
sha256:e930abd762f0…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-14T01:20:36Z
Updated: 2026-10-14T01:20:36Z
Source File:
data/curated/HACQ.yaml