# Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks
## 2 Related Work  
Over the past few decades, significant strides have been made in the realm of social network analysis. Heterogeneous information social networks, which encompass diverse types of interactions modeled through distinct node and link categories, have garnered increasing attention within the domain of link prediction [9]. In contrast to homogeneous networks characterized by uniform node and link types, heterogeneous information social networks differentiate between various categories of social interactions, introducing novel challenges for link prediction and addressing security concerns related to network structure completion and safeguarding user interaction data. Existing link prediction methods can be broadly categorized into three types: similarity-based traditional computational methods, embedding representation-based methods, and graph neural network-based methods. **Traditional similarity-based computational methods**. With a long developmental history, traditional similarity-based computational methods have exhibited commendable results in link prediction within homogeneous social networks. These methods can be classified into several subcategories: Firstly, node similarity-based models frequently leverage fundamental node attributes to define structural similarities among nodes, such as common topological relationships between nodes [10; 11], as well as first-order and second-order neighbor features [12]. Secondly, probability models [13; 14] abstract the underlying network structure using existing network attributes, constructing models with a defined number of parameters via objective function optimization and estimating the likelihood of nonexistent link existence through conditional probability. Finally, maximum likelihood-based methods aim to maximize the probability of observed structure existence, often adhering to certain organizational and probabilistic principles [15]. However, the computational complexity of maximum likelihood increases exponentially with a substantial rise in node count (exceeding 10,000), and its computational demands on the model become excessive, rendering it unsuitable for scalability to large-scale network datasets [16]. **Network embedding representation methods**. While traditional methods still apply to such networks, they do not fully capture the diverse network features, leading to incomplete information gathering, compromised network structure, and potential security issues. Therefore, network embedding representation methods have emerged as a viable solution to address these challenges. Compared to traditional link prediction techniques, embedding representation methods exhibit enhanced performance by leveraging neural network capabilities. For instance, [PERSON] et al. [17] treat link prediction in heterogeneous networks as a multi-task and metric learning problem, learning specific distance metrics for distinct link types. Their approach also accounts for task relevance, non-reinforcing feature robustness, and network distribution characteristics. Similarly, [PERSON] et al. [18] introduce a novel multi-view adversarial completion model that leverages topological distinct structures within each view through relational spaces. This model enhances the semantic representation of nodes by aggregating neighborhood information from synchronized views. [PERSON] et al. [19] use the fully connected network (FCN) adjacency matrix for low-rank representation, capturing local structures through FCN interactions. The new objective function penalizes the nuclear norm of the reconstructed network adjacency matrix. [PERSON] et al. [20] present a novel model that adjusts users across information networks using a seed set of known anchor links. The model integrates four embedding techniques to learn from the same latent space and employs an aggregation method to achieve the final network alignment embedding matrix. **Graph neural network-based methods**.