# Graph Based Relational Features for Collective Classification

## TL;DR

This paper introduces new relational features for standard machine learning methods that leverage direct and indirect relations, achieving comparable or better results than collective inference techniques on benchmark datasets.

## Contribution

The paper proposes a novel approach to incorporate relational features into ML models, reducing reliance on complex inference procedures and strong label assumptions.

## Key findings

- Relational features improve classification accuracy.
- Comparable results to collective inference methods.
- Outperforms collective methods with additional information.

## Abstract

Statistical Relational Learning (SRL) methods have shown that classification accuracy can be improved by integrating relations between samples. Techniques such as iterative classification or relaxation labeling achieve this by propagating information between related samples during the inference process. When only a few samples are labeled and connections between samples are sparse, collective inference methods have shown large improvements over standard feature-based ML methods. However, in contrast to feature based ML, collective inference methods require complex inference procedures and often depend on the strong assumption of label consistency among related samples. In this paper, we introduce new relational features for standard ML methods by extracting information from direct and indirect relations. We show empirically on three standard benchmark datasets that our relational features yield results comparable to collective inference methods. Finally we show that our proposal outperforms these methods when additional information is available.