GiGL contains six components, each designed to facilitate the platforms end-to-end graph machine learning (ML) tasks. The components and there documentation (linked) are as follows:
Config Populator: Processing template config files and updating with fields that are needed for downstream components.
Data Preprocessor: Reading and processing node, edge, and feature/data engineering.
Subgraph Sampler: Generate k-hop localized subgraphs for each node in the graph.
Split Generator: Split the data into training, validation, and test sets.
Trainer: Run distributed training either locally or on the cloud.
Inferencer: Runs inference to generate output embeddings and/or predictions.
| Component | Source Code |
|---|---|
| Config Populator | {py:class}gigl.src.config_populator.config_populator.ConfigPopulator |
| Data Preprocessor | {py:class}gigl.src.data_preprocessor.data_preprocessor.DataPreprocessor |
| Subgraph Sampler | {py:class}gigl.src.subgraph_sampler.subgraph_sampler.SubgraphSampler |
| Split Generator | {py:class}gigl.src.split_generator.split_generator.SplitGenerator |
| Trainer | {py:class}gigl.src.training.trainer.Trainer |
| Inferencer | {py:class}gigl.src.inference.inferencer.Inferencer |
The figure below illustrates at a high level how all the components work together. (Purple items are work-in-progress.)
The figure below is a example GiGL workflow with tabularized subgraph sampling for the task of link prediction, in which the model is trained with triplet-style contrastive loss on a set of anchor nodes along with their positives and (in-batch) negatives.
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:hidden:
components/config_populator
components/data_preprocessor
components/subgraph_sampler
components/split_generator
components/trainer
components/inferencer