Predicting Attributes of Nodes Using Network Structure

TL;DR

This paper introduces a novel method that leverages network topology and neighboring node attributes to improve the prediction of node attributes in graphs, outperforming baseline methods across multiple real-world datasets.

Contribution

It proposes a new feature map representation that utilizes neighbor attributes for better node attribute prediction, enhancing accuracy over existing approaches.

Findings

Significant improvement in prediction accuracy on ten real-world datasets.

The proposed method outperforms baseline approaches.

Network topology information enhances attribute prediction.

Abstract

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.