---
title: "LION：基于克利福德代数的多模态属性图学习新范式"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.24795v1"
permalink: "https://feed.astralor.com/items/itm_a069185bdadc6e8f3b7f2e05"
published: "2026-08-25T16:39:59.000Z"
score: 77
channel: "ai"
tags: ["arXiv","Clifford代数","LION","图神经网络","多模态属性图"]
---

# LION：基于克利福德代数的多模态属性图学习新范式

## AI 点评

> 这篇论文的价值在于它没有简单地堆砌网络模块，而是从几何角度重新思考多模态图的对齐与融合，对研究图基础模型和多模态推理的人会很有启发。如果你在做图文联合建模，LION的传播-聚合分离设计尤其值得细读。

## 速览

多模态属性图学习面临模态对齐忽略图上下文、模态融合缺乏自适应两大挑战。现有方法多采用拓扑约束或模态特定算子，难以有效交互。LION基于克利福德代数与解耦图范式，先构建模态感知几何流形，通过高阶图传播实现模态对齐，再提出自适应全息聚合，融合分量能量与传播尺度信息。在9个图文多模态属性图数据集上，LION在3类图任务和3类模态任务上全面超越现有最先进基线。

## 关键点

- 现有模态对齐方法忽略图上下文，LION用克利福德代数构造几何流形以强化模态交互
- 提出自适应全息聚合，融合分量能量与传播尺度信息，提升模态融合泛化性
- 在9个图文多模态属性图数据集上，覆盖3类图任务与3类模态任务，全面超越SOTA基线

## 全文

Recently, the rapid advancement of multimodal domains has driven a data-centric paradigm shift in graph ML, transitioning from text-attributed to multimodal-attributed graphs. This advancement significantly enhances data representation and expands the scope of graph downstream tasks, such as modality-oriented tasks, thereby improving the practical utility of graph ML. Despite its promise, limitations exist in the current neural paradigms:(1) Neglect Context in Modality Alignment: Most existing methods adopt topology-constrained or modality-specific operators as tokenizers.These aligners inevitably neglect graph context and inhibit modality interaction, resulting in suboptimal alignment.(2) Lack of Adaptation in Modality Fusion: Most existing methods are simple adaptations for 2-modality graphs and fail to adequately exploit aligned tokens equipped with topology priors during fusion, leading to poor generalizability and performance degradation.To address the above issues, we propose LION (c\underline{LI}ff\underline{O}rd \underline{N}eural paradigm) based on the Clifford algebra and decoupled graph neural paradigm (i.e., propagation-then-aggregation) to implement alignment-then-fusion in multimodal-attributed graphs. Specifically, we first construct a modality-aware geometric manifold grounded in Clifford algebra.This geometric-induced high-order graph propagation efficiently achieves modality interaction, facilitating modality alignment.Then, based on the topology-aware Clifford components of aligned tokens, we propose adaptive holographic aggregation. This module integrates component-wise energy and propagation-scale information with learnable parameters to improve modality fusion. Extensive experiments on 9 text-image MAG datasets demonstrate that LION significantly outperforms SOTA baselines across 3 graph and 3 modality downstream tasks.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_a069185bdadc6e8f3b7f2e05