  Multi-modal entity alignment (MMEA) aims to identify equivalent entities
between multi-modal knowledge graphs (MMKGs), where the entities can be
associated with related images. Most existing studies integrate multi-modal
information heavily relying on the automatically-learned fusion module, rarely
suppressing the redundant information for MMEA explicitly. To this end, we
explore variational information bottleneck for multi-modal entity alignment
(IBMEA), which emphasizes the alignment-relevant information and suppresses the
alignment-irrelevant information in generating entity representations.
Specifically, we devise multi-modal variational encoders to generate
modal-specific entity representations as probability distributions. Then, we
propose four modal-specific information bottleneck regularizers, limiting the
misleading clues in refining modal-specific entity representations. Finally, we
propose a modal-hybrid information contrastive regularizer to integrate all the
refined modal-specific representations, enhancing the entity similarity between
MMKGs to achieve MMEA. We conduct extensive experiments on two cross-KG and
three bilingual MMEA datasets. Experimental results demonstrate that our model
consistently outperforms previous state-of-the-art methods, and also shows
promising and robust performance in low-resource and high-noise data scenarios.
