# Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks
## 1 Introduction  
Driven by the rapid surge in social interactions on global online platforms, social network analysis has gained momentum. Concurrently, the security of social media has garnered increased attention. Online social networks involve user-entity interactions and hold significant user privacy data. Yet, network propagation or user-defined controls might hinder complete data acquisition. To address this, we explore integrating link prediction research to comprehensively capture network structure and protect user interactions. Link prediction is a well-explored area in social network analysis, with a growing focus on heterogeneous information social networks. These networks are crucial due to diverse node-link types, mirroring contemporary social networks. Here, we concentrate on this network paradigm where nodes represent individuals and links embody their social interactions [1]. To achieve a holistic network structure and integrity of user interaction data, effective methods for predicting unobserved links become imperative. Various methods for predicting links in heterogeneous networks have been proposed, including traditional ones and network embedding approaches. Traditional methods usually create probabilistic models to represent network connections and calculate the likelihood of links forming between nodes. Alternatively, they use structural node similarities and network topologies to fit models or predict likelihoods of links based on predefined principles. In contrast, network embedding methods take advantage of the powerful learning ability of neural networks and show significant performance enhancement. These existing methods have achieved good results in homogeneous social networks but do not perform well in heterogeneous information social networks. Unlike homogeneous social networks with a single type of node and link [2; 3], heterogeneous information social networks make it difficult to collect a sufficient number of different observation samples to learn different features between different link types [4; 5], thus leading to inaccurate predictions of unobserved links. To achieve our research objectives of designing a versatile model capable of accurately predicting various types of unobserved links in heterogeneous information networks, several key challenges need to be addressed. First and foremost, we must tackle the task of capturing the individual features of different node types and leveraging the acquired type-specific priori knowledge. The second challenge pertains to the fusion of feature representations from diverse enhancing predictive experts. Given the crucial importance of feature representations for different types of unobserved links, an effective fusion mechanism must be devised to amalgamate the link feature representations offered by distinct enhancing predictive experts. The third challenge involves assessing the trustworthiness value of each enhancing predictive expert, indicative of the reliability of the link feature representations they provide. Consequently, we propose an approach named the \"Enhancing Predictive Expert Method\" (EPEM), composed of three components: the individual feature projector, the predictive expert constructor, and the trustworthiness investor. The individual feature projector acquires individual feature representations of distinct node and link types, thereby guiding the prediction of unobserved links. The predictive expert constructor generates multiple enhancing predictive experts based on the individual feature projector, obtaining fused individual feature representations for unobserved links. This fusion assists in distinguishing different link types, thus enhancing prediction accuracy. The trustworthiness investor assesses the reliability of each enhancing predictive expert, amalgamating fusion predictions from diverse experts. The fusion mechanism operates on the principle that enhancing predictive experts with higher trustworthiness levels provides dependable predictions.