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New graph prompt learning framework enhances community search

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced PLACE, a novel graph prompt learning framework designed for attributed community search in large graphs. Inspired by NLP prompt-tuning, PLACE integrates structural and learnable prompt tokens to refine graph queries, enhancing the Graph Neural Network's ability to identify relevant patterns. The framework employs an alternating training paradigm for joint optimization and a divide-and-conquer strategy for scalability on million-node graphs. Experiments show PLACE significantly outperforms state-of-the-art methods, achieving an average F1 score improvement of 22% across various attributed community search tasks. AI

IMPACT Introduces a novel method for attributed community search in large graphs, potentially improving pattern recognition and scalability in graph-based AI applications.

RANK_REASON The cluster contains an academic paper detailing a new method for graph analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New graph prompt learning framework enhances community search

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The cluster contains an academic paper detailing a new method for graph analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shuheng Fang, Kangfei Zhao, Rener Zhang, Yu Rong, Jeffrey Xu Yu ·

    PLACE: Prompt Learning for Attributed Community Search in Large Graphs

    arXiv:2507.05311v2 Announce Type: replace-cross Abstract: In this paper, we propose PLACE (Prompt Learning for Attributed Community Search), an innovative graph prompt learning framework for ACS. Enlightened by prompt-tuning in Natural Language Processing (NLP), where learnable p…

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