LION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning
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 inevit
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- PossiblePossibly related (embedding) · 48%Graph Neural Networks: When Relationships Are the Signal - Snowflake →
- PossiblePossibly related (embedding) · 48%BaryGraph - knowledge graph where every relationship is its own embedded document (not an edge) [R] →
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“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
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- LinkedLinked via arxiv author · 85%Xunkai Li →
“LION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning”