Analyzing Github Repository Collaboration With Node2Vec

Introduction

In this blog post, we will explore a very random topic in the Machine Learning field: analyzing GitHub repository collaboration using Node2Vec. Node2Vec is an algorithm for learning continuous feature representations for nodes in networks. By leveraging techniques from natural language processing and network embedding methods, Node2Vec is capable of generating feature-rich node representations that can be utilized for various downstream applications, such as link prediction, document classification, and recommendation systems.

In the context of GitHub repository collaboration, we will be using Node2Vec to analyze and understand how developers work together on open-source projects. To perform this analysis, we will use Python and popular libraries such as NetworkX, node2vec, and scikit-learn.

Setting up the environment

Before proceeding, make sure to have the following Python packages installed in your environment:

pip install networkx
pip install node2vec
pip install scikit-learn
pip install requests
pip install pandas

Fetching collaboration data from GitHub repositories

First, we need to fetch data from GitHub repositories. We will use the public GitHub API and fetch data for popular open-source repositories. The following Python function retrieves a list of collaborators connected to a specified repository:

import requests

def fetch_collaborators(user, repo, access_token=''):
    url = f'https://api.github.com/repos/{user}/{repo}/collaborators'
    headers = {'Authorization': f'token {access_token}'} if access_token else {}
    response = requests.get(url, headers=headers)
    response.raise_for_status()
    return response.json()

Replace your_github_access-token_here with your personal GitHub access token.

access_token = 'your_github_access_token_here'
collaborators = fetch_collaborators('tensorflow', 'tensorflow', access_token)
print(collaborators)

Creating the collaboration network

Now, let's create a graph representing the collaboration network amongst GitHub users. We will use the NetworkX library to create and manipulate the graph.

import networkx as nx

def create_collaboration_graph(collaborators):
    graph = nx.Graph()
    for collaborator in collaborators:
        user = collaborator['login']
        graph.add_node(user)

        for follower in collaborator['followers']:
            graph.add_edge(user, follower['login'])
    return graph

collab_graph = create_collaboration_graph(collaborators)

Node2Vec for collaboration network

Next, we will use Node2Vec to generate vector representations for each node (GitHub user) in the collaboration network.

from node2vec import Node2Vec

node2vec = Node2Vec(collab_graph, dimensions=64, walk_length=30, num_walks=200, workers=4)
model = node2vec.fit(window=10, min_count=1, batch_words=4)

Analyzing collaboration patterns

With the generated node representations, we can now analyze collaboration patterns among GitHub users. For instance, we can use a clustering algorithm such as K-Means to group users based on their vector representations.

import numpy as np
from sklearn.cluster import KMeans

def cluster_users(model, n_clusters):
    user_vectors = [model.wv[user] for user in model.wv.vocab]
    kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit(user_vectors)
    return kmeans.labels_

n_clusters = 5
labels = cluster_users(model, n_clusters)

clustered_users = {i: [] for i in range(n_clusters)}
for user, cluster_label in zip(model.wv.vocab, labels):
    clustered_users[cluster_label].append(user)

print(clustered_users)

This example allows us to discover users with similar collaboration patterns, highlighting GitHub collaboration insights.

In conclusion, Node2Vec provides an effective way to analyze GitHub repository collaboration patterns. By creating a graph to represent the collaboration network and applying Node2Vec, we can reveal valuable insights into how developers work together and facilitate further exploration in the realm of open-source software development.