In many graphs such as social networks, nodes have associated attributes representing their behavior. Predicting node attributes in such graphs is an important task with applications in many domains like recommendation systems, privacy preservation, and targeted advertisement. Attribute values can be predicted by treating each node as a data point described by attributes and employing classification/regression algorithms. However, in social networks, there is complex interdependence between node attributes and pairwise interaction. For instance, attributes of nodes are influenced by their neighbors (social influence), and neighborhoods (friendships) between nodes are established based on pairwise (dis)similarity between their attributes (social selection). In this article, we establish that information in network topology is extremely useful in determining node attributes. In particular, we use self- and cross-proclivity measures (quantitative measures of how much a node attribute depends on the same and other attributes of its neighbors) to predict node attributes. We propose a feature map to represent a node with respect to a specific attribute a , using all attributes of its h -hop neighbors. Different classifiers are then learned on these feature vectors to predict the value of attribute a . We perform extensive experimentation on 10 real-world datasets and show that the proposed method significantly outperforms known approaches in terms of prediction accuracy.

中文翻译：

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使用网络结构预测节点的属性

在社交网络等许多图表中，节点具有代表其行为的关联属性。在此类图中预测节点属性是一项重要任务，在推荐系统、隐私保护和定向广告等许多领域都有应用。可以通过将每个节点视为由属性描述的数据点并采用分类/回归算法来预测属性值。然而，在社交网络中，节点属性和成对交互之间存在复杂的相互依赖关系。例如，节点的属性受其邻居的影响（社会影响），节点之间的邻居（友谊）是基于其属性之间的成对（不）相似性（社会选择）建立的。在本文中，我们确定网络拓扑中的信息对于确定节点属性非常有用。特别是，我们使用自倾向和交叉倾向测量（节点属性在多大程度上依赖于其邻居的相同和其他属性的定量测量）来预测节点属性。我们提出了一个特征图来表示一个关于特定属性的节点一种, 使用它的所有属性H-跳邻居。然后在这些特征向量上学习不同的分类器来预测属性的值一种. 我们对 10 个真实世界的数据集进行了广泛的实验，并表明所提出的方法在预测准确性方面明显优于已知方法。