LION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning

• URL: http://arxiv.org/abs/2608.24795v1
• Date: Tue, 25 Aug 2026 16:39:59 GMT
• Title: LION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning
• Abstract summary: multimodal domains have driven a data-centric paradigm shift in graph ML, transitioning from text-attributed to multimodal-attributed graphs.<n>Despite its promise, limitations exist in the current neural paradigms.<n>We propose LION to implement alignment-then-fusion in multimodal-attributed graphs.
• Score: 36.90213853456115
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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