# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 2 Related Work
### Unsupervised EA Methods  
Existing EA methods can be classified into three categories: supervised, semi-supervised, and unsupervised, based on the utilization of a training set. Supervised EA methods, which are predominant in earlier research, necessitate the input of alignment seeds (also known as pre-aligned entities) as the training data. However, annotating these alignment seeds is inherently a time-consuming and labor-intensive process; thus, despite achieving significant performance improvements, the scarcity of labeled data limits the practical applicability of these supervised methods. In response to this challenge, some researchers explore semi-supervised methods that employ iterative strategies to expand the training set by generating new alignment seeds during the training process. Notable contributions in this area include MRAA [24], RANM [8], and PEEA [9].  
In recent years, the rapid advancement of contrastive learning, active learning, and other related technologies has garnered increasing academic attention towards unsupervised EA. Current unsupervised EA methods that leverage external information to obviate the necessity for labeled data can be categorized into two categories: (1) One category involves the automatic construction of supervised information from large-scale unsupervised data through auxiliary tasks (pretexts), subsequently utilizing this as supervised learning. For instance, MultiKE [10] develops several cross-KG inference techniques to enhance the generation of labeled data. EVA [11] employs images as pivots to generate pseudo-labeleddata. UPLR [12] computes graph interaction divergence among entity pairs across KGs and adaptively mines high-confidence samples from unlabeled data. PRASE [42] iteratively combines probabilistic reasoning with semantic embedding to compute the probability of entity equivalence, selecting high-confidence pairs as alignment seeds. (2) The second category does not involve a process for generating labeled data; rather, it constructs a loss function from the perspective of data distribution. For instance, ICLEA [13] and SelfKG [14] both employ contrastive learning to facilitate unsupervised feature extraction of entities within KGs. SEU [43] and UDCEA [44] reformulate EA tasks as assignment problems. They leverage machine translation alongside pre-trained language models to compute similarities across multi-view information, subsequently integrating these with global alignment.  
As we know, existing unsupervised EA methods optimize model performance mainly by introducing auxiliary information or more complex graph encoders. However, this optimization is often achieved at the expense of reducing model practicality or increasing model complexity. Consequently, achieving a balance between efficacy and complexity remains a critical challenge for unsupervised EA methods. Based on the study of the relevant data, it can be found that many aligned entities have certain similarities in the multi-hop neighborhood. To address this problem, we investigate the similarity of multi-hop neighborhoods of aligned entities, which is rarely considered by current methods. In this study, we propose a novel efficient and effective method for modeling multi-hop neighborhoods of entities and improving alignment accuracy.