Bridging Concepts: Multi-Agent Reasoning over Scientific Concept Graphs for High-Impact Research Prediction

Key points

• Within a research community, new topics often emerge when previously unconnected concepts are first studied together.1
• We construct scientific concept graphs (SCGs)--domain knowledge graphs whose nodes are fine-grained research concepts extracted from paper text by LLMs and whose edges capture their co-occurrence in papers--and study the task of forecasting high-impact concept pairs, i.e., discovering concept pairs that are currently unconnected but will have high impact when connected.1
• To this end, we introduce new benchmark datasets constructed from CIKM, KDD, and SIGIR conference papers, with varied prediction settings, including impact forecasting for emerging concepts unseen during training.1
• MiroGraph outperforms various impact forecasting and temporal link prediction methods.1

Sources (1)

• [1]Bridging Concepts: Multi-Agent Reasoning over Scientific Concept Graphs for High-Impact Research Prediction
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 01:56 PM

  We propose MiroGraph, a Multi-agent reasoning framework with scientific concept Graphs, in which a feature designer agent designs predictive features for a given concept pair from structural, semantic, and trend perspectives, and then three LLM agents compute the actual feature values from the enclosing subgraph of the concept pair.

  Within a research community, new topics often emerge when previously unconnected concepts are first studied together.

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