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Evidence record 1220 · automatically gathered

ERAlign: Energy-based Representation Alignment of GNNs and LLMs on Text-attributed Graphs

Text-attributed Graphs (TAGs) incorporate textual node attributes with graph structures to describe rich relational semantics. Recent efforts to integrate Graph Neural Networks (GNNs) and Large Language Models (LLMs) have shown promise for learning on TAGs, yet achieving well-aligned representations remains challenging. Prior studies largely rely on heuristics that perform coarse-grained matching. They lack sufficient constraints and ignore distributional alignment, leading to representation dri

Record details

Published: 9 June 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Environment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Safety & alignment · Shared topic: Environment

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How to cite this record

ethics.ai (9 June 2026), “ERAlign: Energy-based Representation Alignment of GNNs and LLMs on Text-attributed Graphs,” evidence record 1220, https://ethics.ai/record/1220 (originally published by arXiv).

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