In many graphs such as social networks, nodes have associated attributes representing their behavior. Predicting node attributes in such graphs is an important problem with applications in many domains like recommendation systems, privacy preservation, and targeted advertisement. Attributes values can be predicted by analyzing patterns and correlations among attributes and employing classification/regression algorithms. However, these approaches do not utilize readily available network topology information. In this regard, interconnections between different attributes of nodes can be exploited to improve the prediction accuracy. In this paper, we propose an approach to represent a node by a feature map with respect to an attribute $a_i$ (which is used as input for machine learning algorithms) using all attributes of neighbors to predict attributes values for $a_i$. We perform extensive experimentation on ten real-world datasets and show that the proposed feature map significantly improves the prediction accuracy as compared to baseline approaches on these datasets.
翻译:在许多图表中, 如社交网络, 节点具有代表其行为的关联属性 。 在这类图表中预测节点属性是许多领域应用的一个重要问题, 比如建议系统、 隐私保护和有针对性的广告。 属性值可以通过分析属性的模式和相关性以及使用分类/ 递减算法来预测。 但是, 这些方法并不使用现成的网络地形信息 。 在这方面, 节点不同属性之间的互联可以用来提高预测准确性 。 在本文中, 我们提出一种方法, 利用邻居的所有属性来预测 $_ i 的属性( 用作机器学习算法的输入) 来代表一个节点 。 我们在十个真实世界数据集上进行了广泛的实验, 并显示拟议的地貌图与这些数据集的基准方法相比, 大大提高了预测准确性 。