# Network Semantic Segmentation with Application to GitHub

## TL;DR

This paper introduces network semantic segmentation for social network analysis, specifically applied to GitHub, by combining node attributes and network connections to classify users into meaningful topics.

## Contribution

It proposes a novel approach that integrates network connections and attributes for semantic segmentation, enhancing community analysis in social coding networks.

## Key findings

- Semantic segmentation improves community topic coherence
- Method outperforms traditional community detection algorithms
- Enhanced understanding of user interests in GitHub networks

## Abstract

In this paper we introduce the concept of network semantic segmentation for social network analysis. We consider the GitHub social coding network which has been a center of attention for both researchers and software developers. Network semantic segmentation describes the process of associating each user with a class label such as a topic of interest. We augment node attributes with network significant connections and then employ machine learning approaches to cluster the users. We compare the results with a network segmentation performed using community detection algorithms and one executed by clustering with node attributes. Results are compared in terms of community diversity within the semantic segments along with topic

---
Source: https://tomesphere.com/paper/1902.05220