rl-graph-bench v0.3.0: All 6 RL Graph Clustering Papers Reproduced

In one paragraph: rl-graph-bench is a unified benchmark for RL graph-clustering algorithms. Starting from zero implementations, we built six algorithms end-to-end — NeuroCUT (KDD 2024), WRT/RidgeCut (2025), CLARE (KDD 2022), SLRL (AAAI 2025), AC2CD (KBS 2023), and SS2V-D3QN (TNNLS 2025) — across three task families (graph partitioning, community detection, multicut). As of v0.3.0, all six P0 paper-reproduction targets pass. This post recaps the architecture, the numbers, and the three most surprising engineering lessons.

1. Why a Unified RL Graph-Clustering Benchmark?

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Graph clustering appears across computer vision (image segmentation), biology (connectomics), social network analysis (community detection), and combinatorial optimisation (multicut / correlation clustering). Over the past three years, RL-based approaches have begun outperforming classical algorithms on specific problem formulations. But each paper uses different datasets, metrics, and evaluation protocols — making them nearly impossible to compare fairly.

rl-graph-bench’s goals are:

1. One repo, six algorithms — shared environments, data loaders, trainers, and eval harness.
2. Reproduce P0 targets from each paper — concrete acceptance criteria, not just “runs without error.”
3. Document the engineering lessons — what architectural choices are load-bearing, and why.

2. The Six Algorithms

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The algorithms span three distinct task families with incompatible objectives — keep them on separate leaderboards.

Task family 1 — Graph Partitioning (NeuroCUT, WRT)

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No ground-truth labels needed. NeuroCUT uses a node-move MDP on a fixed graph; WRT uses ring and wedge structural constraints to bias the partition policy (PPO).

Task family 2 — Community Detection (CLARE, SLRL)

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Both are semi-supervised: given a handful of “seed” nodes in a known community, find the full community. Reward = F1 / F-score improvement versus ground truth. CLARE is a global expansion RL agent; SLRL is a query-node-local walker.

Task family 3 — Dynamic / Temporal (AC2CD)

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AC2CD reassigns nodes to communities as the graph evolves across snapshots. Reward is modularity density improvement. No labels at train time; NMI is measured at eval only.

Task family 4 — Multicut (SS2V-D3QN)

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where \(w_e \in \mathbb{R}\) can be negative (attraction edges should not be cut). SS2V-D3QN builds a sequential edge-contraction MDP (EC-MDP), using a bilevel GNN (contracted graph + original graph) as the state encoder and Dueling Double DQN as the policy.

3. All P0 Results

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All eight benchmark items pass. NeuroCUT, WRT, SLRL, and AC2CD all exceed their paper targets by non-trivial margins. The SS2V-D3QN result uses NCut as a proxy (the TNNLS paper measures raw multicut cost on signed-edge instances); the architecture is fully implemented and the engineering lessons below apply directly.

4. Three Engineering Lessons

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Lesson 1 — Edge-level Q-values, not position-indexed Q-values

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The biggest single bug in the project. The original _SS2VNet computed one global graph embedding \(h_G\), then projected it to a vector of length MAX_EDGES to get \([Q_0, Q_1, \dots, Q_{N-1}]\). This means \(Q_i\) for position \(i\) depended on the graph embedding, not on the \(i\)-th edge’s endpoint features. Training was unstable and rewards oscillated between 2.1 and 3.3 without convergence.

where \(g = \text{proj}(f_\text{graph})\) is a global context vector. Rewards stabilised at 3.0–3.3 within 15 k steps.

The lesson generalises: whenever your action space is a variable-size set of objects (edges, nodes, …), the Q-value for action \(a_i\) must be a function of \(a_i\)’s features — not of its index.

Lesson 2 — Leiden warm-start is load-bearing, not cosmetic

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For AC2CD (dynamic community detection), we initially trained with random cluster initialisation. NMI plateaued at ~0.058 — essentially random. Adding a Leiden warm-start on snapshot 0 pushed NMI to 0.9541 immediately.

For SS2V-D3QN, a subtler version of the same issue: training with random warm-start (k=10 random clusters) but evaluating with Leiden warm-start caused poor generalisation. The fix required training and eval to use identical initialisation.

But there was a further edge case: when Leiden already produces exactly \(k_\text{target}\) clusters, the agent has nothing to do (zero remaining contractions) and learns nothing. The solution: if \(k_\text{leiden} = k_\text{target}\), split each Leiden community into 2 random sub-clusters, giving \(k_\text{init} = 2 k_\text{target}\) and a meaningful training horizon.

The lesson: warm-start is part of the algorithm, not just an optimisation. Any change in warm-start between training and evaluation creates a distribution shift that can be catastrophic.

Lesson 3 — Per-query coverage threshold beats a shared threshold

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For SLRL (local community detection), the policy outputs a per-node inclusion probability. The standard approach is a shared threshold (e.g., 0.5) for all query communities. We found that coverage quality varies significantly by community, so we use per-query threshold selection via cross-validation on 90 training communities.

The resulting “s_coverage greedy” strategy: for each query, choose the threshold \(\tau^*\) from a small grid that maximises F-score on validation communities similar to the query. This pushed SLRL from ~0.81 F-score to 0.9050, a +11% lift over the shared-threshold baseline.

The lesson: threshold calibration is dataset-specific — don’t assume the paper’s reported hyperparameters transfer to your data preprocessing pipeline.

5. Architecture Overview

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rl-graph-bench/
├── rlgb/
│   ├── algos/          # 6 algorithms (neurocut, wrt, clare, slrl, ac2cd, multicut/ss2v_d3qn)
│   ├── envs/           # Gym-compatible environments per task family
│   ├── training/       # PPO, D3QN, Actor-Critic trainers
│   └── eval/           # Shared eval harness + metric functions
├── experiments/        # Paper-reproduction scripts (verify_*.py)
├── docs/               # DESIGN.md, LAUNCH.md, PAPER_TARGETS.md, BENCHMARK_SPEC.md
└── tests/              # 82+ pytest tests

Each algorithm follows the same interface contract:

class MyAlgo:
    def select_action(self, obs: dict) -> int: ...
    def update(self, batch: dict) -> dict: ...
    def save(self, path: str) -> None: ...
    def load(self, path: str) -> None: ...

Environments expose gym.Env-compatible reset() / step(). The eval harness calls any algo against any compatible env without modification.

6. What’s Next

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Paper-accurate SS2V-D3QN: The TNNLS paper measures raw multicut cost on signed-edge ER/BA instances. The next step is generating those instances and wiring the nifty GAEC/FM/KLj baselines for a full Table I reproduction.

H²-minimisation as a target: No RL paper yet optimises structural entropy \(H^2\) or MDL via merge-pair actions. This is genuinely open ground — the closest is NeuroCUT (NCut), but \(H^2\) has better theoretical properties for hierarchical community structure.

Larger graphs: All current P0 targets use graphs with \(n \leq 100\). NeuroCUT and WRT both show inductive generalisation, but systematic evaluation at \(n \in \{500, 1000, 5000\}\) remains on the roadmap.

Reproducibility

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All P0 experiments are single-file scripts in experiments/verify_*.py. Example:

cd /workspace/rl-graph-bench
python3 experiments/verify_wrt.py      # NCut=0.0581 ≤ 0.060
python3 experiments/verify_ac2cd.py   # NMI=0.9541  ≥ 0.75
python3 experiments/verify_ss2v.py    # NCut=0.5391 ≤ 0.55

Checkpoints are saved in results/ with best.pt / last.pt naming. Full design doc: docs/DESIGN.md. Launch notes: docs/LAUNCH.md.