# Network Semantic Segmentation with Application to GitHub

Source: [Network Semantic Segmentation with Application to GitHub](<https://www.sota2.com/research/paper/68fa65731e209131cea0a874>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:38.288Z

Authors: Neda Hajiakhoond Bidoki, Gita Sukthankar

Published: 2019-02-14T04:58:52.000Z

## 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

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1902.05220>)

[PDF](<https://arxiv.org/pdf/1902.05220.pdf>)