基于图神经网络的社交网络影响力预测算法

Social influence prediction with graph neural network

近十年来,通过社交网络(如微博、推特)分享信息已经成为人们日常生活中不可缺少的一个环节,如何有效地预测信息传播的影响力成为社交网络研究中的重要课题,不论是识别病毒式营销和虚假新闻还是精确推荐和在线广告都有许多应用.目前,一些应用深度学习进行社交网络影响力预测的方法已经取得了一定进展,但在进行深度学习时仍会面临以下难点:用户通常具有不同的行为和兴趣并且他们同时通过不同的渠道进行互动;用户之间的关系难以检测和形式化表达.传统的社交网络影响力预测方法通过设计复杂的规则来手动提取用户及其所处网络的特征信息,这一方法的有效性严重依赖于设置规则的专业性,所以很难将某一领域的规则推广到其他领域的应用中去.基于深度神经网络模型,设计一种端到端的神经网络来学习用户的隐藏特征信息以预测其社交网络影响力.首先通过图嵌入的方式对用户的局部网络进行特征提取,然后将特征向量作为输入对图神经网络进行训练,从而对用户的社会表征进行预测.该方法的创新之处:运用图卷积和图关注方法,将社交网络中用户的特征属性和其所处局域网络特征相结合,大大提高了模型预测的精度.通过在推特、微博、开放知识图谱等数据集上的大量实验,证明该方法在不同类型的网络中都有较好的表现.

For a decade,sharing information through social networks(e. g. Microblog and Twitter) has become an indispensable part in our daily life. Therefore,how to effectively predict the social influence has become an important subject in the study of social network. There are many applications,such as identifying viral marketing and fake news, accurate recommendations and online advertising. In recent years,some deep learning social network influence prediction methods have made some progress,but still faces the following difficulties: users typically have different behaviors and interests and they interact through different channels at the same time,and relationships between users are difficult to detect and formally describe. Traditional social network influence prediction methods manually extract the feature information of users and their networks by designing complex rules. However,the effectiveness of this method heavily depends on the domain knowledge of the rules set,which makes it difficult to generalize the rules in one field to the application in other fields. Based on deep learning method,we design an end-to-end neural network to learn the features of users’ hidden information to predict their influence in the social network. Firstly,feature extraction is carried out on users’ local network through graph embedding,and then the graph neural network is trained with feature vector as input,so as to predict users’ social representation. Compared with previous work,we combine the feature attributes of users in social network with the local area network features by using graph convolution and graph attention method,which greatly improves the accuracy of model prediction.