arXiv Machine Learning
Bridging Academia and Industry: A Comprehensive Benchmark for Attributed Graph Clustering
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Towards One-for-All Foundation Model for Attributed Graph Clustering
The paper introduces OFAG, a foundation model designed for attributed graph clustering that can be trained once and applied to diverse graphs without graph‑specific tuning. It learns a reusable clustering strategy from synthetic graphs and uses a dimension‑agnostic encoder to handle varying feature spaces, producing clustering‑friendly node representations in a single forward pass. Across ten datasets, a frozen OFAG model outperforms baselines in both speed and clustering quality, and the authors provide code and pretrained checkpoints for easy adoption.
arXiv Machine Learning
Jun 11
GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs
arXiv:2606. 11562v1 Announce Type: new Abstract: Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repurposing, user preference, and scientific theme are all inferred from a node together with its neighbourhood.
arXiv Machine Learning
Sep 7
Efficient Clustering with Quality Guardrails for LLM-based Recommender Systems at Industry Scale
The paper presents a scalable two‑stage clustering algorithm that guarantees per‑sample quality guardrails for large‑scale LLM‑based recommender systems. By first forming Mini‑batch K‑Means clusters and then greedily selecting representatives that meet user‑specified similarity and attribute constraints, the method ensures each sample inherits only relevant and safe outputs. Benchmarks show the approach runs faster and scales to millions of inputs, achieving a 50‑fold reduction in downstream LLM cost and runtime while maintaining personalization.
arXiv Machine Learning
Jul 23
Efficient Clustering with Provable Guardrails for LLM Inference at Scale
arXiv:2607. 19704v1 Announce Type: new Abstract: Scaling LLM-based applications to millions of users is bottlenecked by the inference cost and latency of modern foundation models.
arXiv Machine Learning
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SynCo: Synthetic Community-Aware Attributed Graph Generator for Graph Neural Network Benchmarking
SynCo is a synthetic graph generator that lets users control node degree distributions and sub‑community structures, addressing limitations of existing generators that rely on power‑law distributions and lack flexibility. It is evaluated on graph mimicking, hyperparameter tuning, and node clustering, outperforming state‑of‑the‑art methods while preserving original data distributions. SynCo can generate large graphs with up to 2.1 million nodes.
arXiv Machine Learning
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TextClusterLab: An Integrated Framework for Reliable Text Clustering Studies
arXiv:2606. 28328v1 Announce Type: cross Abstract: In recent years, text clustering has become a critical technique for applications including intent discovery, topic mining, and recommendation systems.