Multi-Agent Agentic Graph Learning via Structural Signatures

• URL: http://arxiv.org/abs/2609.09565v1
• Date: Wed, 09 Sep 2026 00:43:45 GMT
• Title: Multi-Agent Agentic Graph Learning via Structural Signatures
• Abstract summary: Agentic graph learning (AGL) has recently achieved promising results on graph reasoning tasks.<n>Existing methods either employ a single agent or orchestrate multiple role-based agents to reason and learn over the entire graph.<n>This paper introduces a multi-agent agentic graph learning (i.e., MAAGL) framework.
• Score: 15.798786167030853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agentic graph learning (AGL) has recently achieved promising results on graph reasoning tasks, where an agent powered by a large language model (LLM) sequentially samples the graph as evidence to support its final prediction. Existing methods either employ a single agent or orchestrate multiple role-based agents to reason and learn over the entire graph, but both essentially rely on a shared reasoning policy across different graph regions, which can be suboptimal for graphs with heterogeneous structural and semantic patterns. Inspired by the progress of multi-agent collaboration on complex reasoning tasks, a natural remedy is to let multiple agents own different memory and collaborate; however, applying this paradigm to graphs directly faces two challenges. First, existing AGL methods typically verbalize graph structures into natural-language descriptions for LLM agents, making the reasoning process sensitive to the ordering of structural information and thereby breaking the permutation-invariant nature of graphs. Second, incorporating increasingly large sampled neighborhoods leads to rapidly growing contexts. To address these challenges, this paper introduces a multi-agent agentic graph learning (i.e., MAAGL) framework. MAAGL partitions the graph into communities and assigns an independent agent to each community for region-specific specialization. MAAGL represents structural and semantic evidence separately. Structural evidence is summarized by a dynamically updated structural signature that is permutation-invariant and fixed in size, while semantic evidence is filtered to the top-k nodes ranked by relevance. Based on historical trajectories with similar signatures, agents estimate their confidence and trigger debate-style collaboration when needed. Extensive experiments on four benchmark datasets show that MAAGL outperforms SOTA AGL methods.

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