## 1 Introduction
The first endeavor in this direction is GCN-align [22], which utilizes the entity relationships in each KG to construct the network structure of GCN. This method embeds multiple languages into a unified vector space and discovers entity alignment based on the distance between the entities in the embedding space. However, GCN-align is mainly aimed at aligning isomorphic graphs, but its processing ability for heterogeneous graphs is weak, resulting in the loss of heterogeneous edge information. Therefore, in recent years, many studies have attempted to integrate edge information into GCN to enhance the relationship perception ability of the model. MuGNN [23] and NAEA [24] introduce attention mechanisms to learn different weights for different types of relationships. HMAN [25] uses GCNs to combine multiple aspects of entity information, including topological connections, relationships, and attributes, to learn entity embeddings. RDGCN [26] merges relational information via the attentional interaction between the original graph and the dual relationship graph, and further captures adjacent structures to learn better entity representations. MRAEA [27] models cross-linguistic entity embeddings directly by focusing on the metasemantics of the incoming and outgoing neighbors and their connection relationships regarding the nodes. PSR [28] proposes a simplified graph encoder with relation graph sampling, which achieves high performance, scalability, and robustness through symmetric non-negative alignment loss and incremental semi-supervised learning. All these efforts demonstrate the importance of relation information in entity alignment. However, these methods do not consider the role of edge alignment in EA and only consider the integrated semantic information of the relationships regarding entity embeddings. Additionally, some recent works incorporate extra external information as weak supervision signals. For instance, EVA [29] proposes a structure-aware uncertainty sampling strategy that can measure the uncertainty of each entity in KG and its impact on the adjacent entities. JEANS [30] jointly represents multilingual KG and text corpora in a shared embedding scheme and seeks to improve the alignment of entities and text with accompanying supervisory signals. Furthermore, CG-MuAlign [31] employs a designed attention mechanism to facilitate the collaborative alignment of positive information from the entity neighborhood and comparably effective negative messages, achieving a joint alignment of multiple types for the entity. ActiveEA [32] designs an active learning framework to create seed comparisons with large amounts of information in order to obtain more effective EA models at lower annotation costs. With more and more research beginning to explore how to combine visual content in the internet with EA, a new trend is to associate images with entity names to enrich the information of entity pairs. At present, the research mainly focuses on designing fusion methods suitable for cross-modal data to achieve cross-modal EA. [PERSON] et al. [33] generated entity representations of relational knowledge, visual knowledge, and digital knowledge and integrated them through a multimodal knowledge fusion module. [PERSON] et al. [34] employed a modal enhancement mechanism to integrate visual features to guide relational feature learning, and adaptively assigned attention weights to capture valuable attributes for alignment. [PERSON] et al. [35] learned multiple individual representations from multiple modalities and then performed contrastive learning to jointly model the interactions within and between modalities. However, these methods learn multimodal fusion weights at the knowledge graph level, ignoring the intra-modal differences for each entity (such as node degree or relationship quantity) and inter-modal preferences (such as modality absence or ambiguity).