# Predicting Attributes of Nodes Using Network Structure

arXiv: https://arxiv.org/abs/1912.12264 · HTML: https://mlsanity.com/paper/1912.12264/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2019.

## Built on by (most cited first, 15 of 15)

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- [Efficient Analysis of COVID-19 Clinical Data using Machine Learning Models](https://mlsanity.com/paper/2110.09606/) (2021)
- [Computing Graph Descriptors on Edge Streams](https://mlsanity.com/paper/2109.01494/) (2021)
- [Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers](https://mlsanity.com/paper/2211.00783/) (2022)
- [Evaluating COVID-19 Sequence Data Using Nearest-Neighbors Based Network Model](https://mlsanity.com/paper/2211.10546/) (2022)
- [Characterizing SARS-CoV-2 Spike Sequences Based on Geographical Location](https://mlsanity.com/paper/2110.00809/) (2021)
- [Locally Weighted Mean Phase Angle (LWMPA) Based Tone Mapping Quality Index (TMQI-3)](https://mlsanity.com/paper/2109.08774/) (2021)
- [Information We Can Extract About a User From 'One Minute Mobile Application Usage'](https://mlsanity.com/paper/2207.13222/) (2022)
- [Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN Performance](https://mlsanity.com/paper/2406.02772/) (2024)

## Abstract (arXiv)

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 ai (which is used as input for machine learning algorithms) using all attributes of neighbors to predict attributes values for ai. 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.