## 1 Introduction  
Emotions affect the status of humans physiologically and psychologically. One may make a quick decision simply because of a particular feeling. Nowadays, more and more people are used to sharing their emotions and opinions through texts on social networks. It is interesting to understand how an individual's emotions are affected by various factors and predict his future emotion. Individual emotion prediction aims to determine the future emotional state of a user from current and previous behavioral cues [1] and has potential applications in various fields, such as human-computer or human-robot interactions [2], market analysis [3], public opinion analysis [4], political decision-making [5], and recommendation systems [6]. In recent years, several researchers have conducted research on individual emotion prediction by using texts of users' posts and comments. For emotion prediction, most traditional methods use statistical models and machine learning methods. However, these methods, which are limited to binary classification, have poor performance and low effects. In recent years, deep learning has addressed the limitations of traditional approaches, which predict and classify individual emotions by modeling the time series of users' emotions. However, existing deep learning models are relatively simple. Most of them mainly rely on the user's own emotional development rules,while ignoring the neighbors' effect on the user's emotion. In fact, the way in which users share their emotions online affects not only their own emotions [7], but also the emotions of others with whom they relate [8]. Furthermore, [PERSON] et al. [9] find that strong ties are instrumental in spreading individual emotions both in the online and practical world. In addition, users' posts and comments in adjacent time periods are interrelated, and it is necessary to consider the relevance and dependence between users' posts when predicting their personal emotions. However, users optionally publish their posts and comments on social networks. Irregular time series data are common in social networks [10]. Thus, it is challenging for the individual emotion prediction task to use the time series data of user posts and comments. This study focuses on the problem of individual's future emotion prediction in the context of irregular time series. The problem setup is illustrated in Figure 1. Given a social network in which each vertex (A, B, C, D) represents a user, there is an undirected edge between two users if they have a social network relationship. The users' emotion data (from users' posts and comments) are classified into three types. It includes the observed data (green boxes and circles), missing data (red boxes and circles), and goal data (purple boxes and circles). Every user's emotion is represented by a six-dimensional vector, composed of the probability of six basic emotion categories (i.e., happiness, sadness, anger, disgust, fear, and surprise) proposed by [11]. The goal of this paper is to predict the emotions of users A and D at timestamp 3. To predict user A's emotion at timestamp 3, the traditional methods utilize [PERSON]'s previous emotion data at timestamps 1 and 2. However, in both timestamps 1 and 2, [PERSON]'s previous emotion data were missing. Interestingly, neighbors' (users B and C) emotion data can provide clues for predicting user A's emotions. For instance, as the number of user A's neighbors with happy emotions increases, the probability that the user will be happy continues to increase. It turns out that happy users influence other users to feel happy as well, which means that emotions spread through social networks [12]. These relationships are refered as the surrounding influence. The first challenge is to capture this surrounding influence and model the connection between the user's future emotion and the surrounding influence. Returning to Figure 1, user D's emotion also needs to be predicted.