alibaba / graphlearn-for-pytorch
Components

An overview of source code logical components.

Intro

Logical decomposition is a representation of the organization of the main source code, where every and each file is put in exactly one logical component.

Logical Decompositions Overview

Analyzed system has 1 logical decomposition:

Logical Decomposition #1: PRIMARY

The decompositions is based on the folder structure at level 1 (relative to the source code root).

Bubble Chart | Tree Map
Component Sizes (Lines of Code)
The "primary" logical decomposition has 3 components.
  • 111 files, 12,255 LOC (100.0% vs. main code).
  • "graphlearn_torch" is biggest, containing 96.15% of LOC.
  • "ROOT" is smallest, containing 0.52% of LOC.


graphlearn_torch11783 LOC (96%) 106 files
benchmarks408 LOC (3%) 4 files
ROOT64 LOC (<1%) 1 file
Component Commits
Components ordered by number of commits
Total Commits per Component
All commits, some commits may include files from multiple components.
graphlearn_torch60 commits (48%)
ROOT12 commits (9%)
benchmarks4 commits (3%)
Yearly File Updates Trend per Components
The number of file changes in commits
animated commit history: all time cumulative | 12 months window
2025 2024 2023
graphlearn_torch
9
49
281
ROOT
4
8
benchmarks
3
7


Dependencies between components in same commits (past 180 days)
The number on the lines shows the number of shared commits.
See detailed temporal dependencies report...

G graphlearn_torch graphlearn_torch graphlearn_torch--graphlearn_torch 1 benchmarks benchmarks graphlearn_torch--benchmarks 1 benchmarks--benchmarks 1


2025-05-14 18:56