# DeeWaNA: An Unsupervised Network Representation Learning Framework Integrating Deepwalk and Neighborhood Aggregation for Node Classification

## TL;DR

DeeWaNA is a new unsupervised framework that combines random walks and neighborhood aggregation to improve node classification in networks.

## Contribution

The novel integration of DeepWalk and attention-based neighborhood aggregation into a unified model for network representation learning.

## Key findings

- DeeWaNA outperforms four existing unsupervised methods in node classification tasks.
- The attention-based weighting mechanism improves neighborhood relationship modeling.
- The framework enhances structural feature extraction and representation quality.

## Abstract

This paper introduces DeeWaNA, an unsupervised network representation learning framework that unifies random walk strategies and neighborhood aggregation mechanisms to improve node classification performance. Unlike existing methods that treat these two paradigms separately, our approach integrates them into a cohesive model, addressing limitations in structural feature extraction and neighborhood relationship modeling. DeeWaNA first leverages DeepWalk to capture global structural information and then employs an attention-based weighting mechanism to refine neighborhood relationships through a novel distance metric. Finally, a weighted aggregation operator fuses these representations into a unified low-dimensional space. By bridging the gap between random-walk-based and neural-network-based techniques, our framework enhances representation quality and improves classification accuracy. Extensive evaluations on real-world networks demonstrate that DeeWaNA outperforms four widely used unsupervised network representation learning methods, underscoring its effectiveness and broader applicability.