We're sunsetting PodQuest on 2025-07-28. Thank you for your support!
Export Podcast Subscriptions
cover of episode Github Network Analysis

Github Network Analysis

2025/6/22
logo of podcast Data Skeptic

Data Skeptic
AI Deep Dive AI Chapters Transcript
People
A
Asaf
G
Gabriel Ramirez
K
Kyle
Topics
Asaf：我认为组织网络分析不应被视为严格的成绩单，不应期望开发者和项目经理有固定的模式。网络分析和中心性指标不是万能的，仪表板不适合，每个组织和网络都是不同的。定量分析可以指出需要调查的地方，但还需要定性研究，这更多关乎组织健康而非员工成功。如果发现资深专家或主题专家位于网络边缘，可能表明他们没有充分参与知识传递，或者管理者没有充分利用他们。这可能关系到组织健康和员工表现。 Kyle：组织可以根据情况选择让专家培训他人或专注于自己的工作，GH Explorer项目可以帮助了解如何组织。 Gabriel Ramirez：我创建了一个丰富的数据集，包括工程师、项目经理和参与软件制作相关对话的其他人，而不仅仅是提交代码。链接是用户和GitHub对象之间的所有交互，例如创建问题、在问题中被提及、批准或拒绝拉取请求以及参与讨论。我希望看到所有团队成员紧密合作。我不认为这些指标应该放在仪表板上，因为脱离了管理者的对话，这些指标可能意义不大，甚至可能意味着相反的事情。成为经理后，我意识到自己始终处于网络的中心，这让我意识到我是一个关键节点，如果我休假或离职，网络就会崩溃，这不是我希望看到的，经理应该赋能他人，而不是占用他们的工作。仅仅依靠数字无法量化所有的定性因素，人们的故事以及正在发生的事情，人们可能会通过在所有事情上发表评论来操纵系统，但评论的质量可能很低，因此网络指标可能会很高，但工作质量却很低。网络分析只是更大拼图中的一部分，而不是我们可以作为任何依据的指标。

Deep Dive

Chapters
This chapter explores using GitHub metadata (pull requests, issues, discussions) for network analysis to understand team collaboration. It introduces the concept of analyzing this data as a bipartite graph and using network centrality measures to reveal organizational dynamics.
  • GitHub metadata, including pull requests, issues, and discussions, can be analyzed as a bipartite graph to understand team collaboration.
  • Network centrality measures, such as eigenvector and betweenness centrality, reveal organizational dynamics.
  • LLMs can be used to analyze networks, particularly smaller ones, providing insights into team collaboration.

Shownotes Transcript

In this episode we'll discuss how to use Github data as a network to extract insights about teamwork.

Our guest, Gabriel Ramirez, manager of the notifications team at GitHub, will show how to apply network analysis to better understand and improve collaboration within his engineering team by analyzing GitHub metadata - such as pull requests, issues, and discussions - as a bipartite graph of people and projects.

Some insights we'll discuss are how network centrality measures (like eigenvector and betweenness centrality) reveal organizational dynamics, how vacation patterns influence team connectivity, and how decentralizing communication hubs can foster healthier collaboration. 

Gabriel’s open-source project, GH Graph Explorer, enables other managers and engineers to extract, visualize, and analyze their own GitHub activity using tools like Python, Neo4j, Gephi and LLMs for insight generation, but always remember – don't take the results on face value. Instead, use the results to guide your qualitative investigation.