The creation of social ties is largely determined by the entangled effects of people’s similarities in terms of individual characters and friends. However, feature and structural characters of people usually appear to be correlated, making it difficult to determine which has greater responsibility in the formation of the emergent network structure. We propose AN2VEC, a node embedding method which ultimately aims at disentangling the information shared by the structure of a network and the features of its nodes. Building on the recent developments of Graph Convolutional Networks (GCN), we develop a multitask GCN Variational Autoencoder where different dimensions of the generated embeddings can be dedicated to encoding feature information, network structure, and shared feature-network information. We explore the interaction between these disentangled characters by comparing the embedding reconstruction performance to a baseline case where no shared information is extracted. We use synthetic datasets with different levels of interdependency between feature and network characters and show (i) that shallow embeddings relying on shared information perform better than the corresponding reference with unshared information, (ii) that this performance gap increases with the correlation between network and feature structure, and (iii) that our embedding is able to capture joint information of structure and features. Our method can be relevant for the analysis and prediction of any featured network structure ranging from online social systems to network medicine.

社会纽带的建立在很大程度上取决于人们在个性和朋友方面的相似性所产生的纠缠效应。但是，人们的特征和结构特征通常看起来是相关的，这使得很难确定哪个人在紧急网络结构的形成中承担更大的责任。我们建议AN2VEC，一种节点嵌入方法，其最终目的是解开由网络结构及其节点的特征共享的信息。基于图卷积网络（GCN）的最新发展，我们开发了一种多任务GCN变分自动编码器，其中生成的嵌入的不同维度可以专用于编码特征信息，网络结构和共享的特征网络信息。我们通过将嵌入重建性能与未提取共享信息的基线情况进行比较，来探索这些解开字符之间的相互作用。我们使用特征和网络字符之间具有不同级别的相互依赖性的合成数据集，并显示（i）依赖共享信息的浅层嵌入比未共享信息的相应参考具有更好的性能;（ii）随着网络和特征结构，以及（iii）我们的嵌入能够捕获结构和特征的联合信息。我们的方法可用于分析和预测从在线社交系统到网络医学的任何特色网络结构。