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README.md

@@ -1,53 +1,117 @@
 # Distributed Graph Flow
 
 <p align="center">
-<img src="doc/docs/image/logo.png" width="300" />
+<img src="https://raw.githubusercontent.com/google/distributed_graph_flow/main/doc/docs/image/logo.png" width="300" />
 </p>
 
-**(Distributed) Graph Flow** (GF) is a Python toolkit to develop and deploy
-Graph Neural Network (**GNN**) models.
+<p align="center">
+<a href="https://pypi.org/project/dgf/"><img src="https://img.shields.io/pypi/v/dgf" alt="PyPI"></a>
+<a href="https://pypi.org/project/dgf/"><img src="https://img.shields.io/pypi/pyversions/dgf" alt="Python"></a>
+<a href="https://github.com/google/distributed_graph_flow/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-Apache%202.0-blue" alt="License"></a>
+<a href="https://dgf.readthedocs.io/"><img src="https://img.shields.io/badge/docs-dgf.readthedocs.io-blue" alt="Documentation"></a>
+</p>
+
+**Graph Flow** (DGF) is an open-source Python library to train, evaluate, and
+deploy Graph Neural Networks (**GNNs**) on tabular, relational, and temporal
+data. It is developed by the Google GNN team, the team behind
+[TensorFlow GNN](https://github.com/tensorflow/gnn), and is its successor.
+
+Graph Flow has two APIs:
+
+-   The **Simple API** is as easy to use as scikit-learn: one function call
+    trains a GNN model from a graph and a target column, and the resulting
+    model can be evaluated, used for predictions, and saved.
+-   The **Advanced API** is a set of composable building blocks (graph IO,
+    samplers, feature normalizers, and JAX/Flax GNN layers). Like Lego bricks,
+    you pick the ones you need and assemble them with your own code, including
+    PyTorch Geometric models.
 
-For more information, check the documentation at https://dgf.readthedocs.io/
+📖 **Documentation:** https://dgf.readthedocs.io/
 
 This is not an officially supported Google product. This project is not
 eligible for the [Google Open Source Software Vulnerability Rewards
 Program](https://bughunters.google.com/open-source-security).
 
-## Installation
+## 📦 Installation
 
 To install DGF from [PyPI](https://pypi.org/project/dgf/), run:
 
 ```shell
 pip install dgf -U
 ```
 
-Currently, DGF is available on Python 3.11-13, on Linux x86-64.
+Graph Flow is available for Python 3.11–3.13 on Linux x86-64.
 
-## 😎 Minimal Usage example
+## 🔥 Why Graph Flow?
 
-```python
-# Temporary fix for Keras dependency.
-import os
-os.environ["TF_USE_LEGACY_KERAS"] = "1"
+-   **Simple API:** Train, evaluate, and save a GNN model in about 10 lines of
+    Python, without prior GNN experience.
+-   **Temporal graphs:** Dynamic graphs and time-series features are supported.
+    Time-aware sampling ensures that a model only sees information available
+    before the prediction time.
+-   **Scale:** Train in memory on graphs with up to 1B edges on a single
+    machine. For larger graphs, the distributed sampler (Apache Beam on Google
+    Cloud Dataflow) scales to trillions of edges.
+-   **Advanced API:** Message-passing layers (MPNN, GAT, Graph Transformer),
+    samplers, and normalizers are independent JAX/Flax building blocks for
+    custom models.
+-   **Interoperability:** Convert graphs to PyTorch Geometric, TensorFlow,
+    TF-GNN, and NetworkX.
+-   **Deployment:** Run inference in-process in Python, or export models to
+    TensorFlow SavedModel (e.g., for Vertex AI).
+
+## 😎 Minimal usage example
 
-# Import (distributed) graph flow
+```python
 import dgf
 
-# Fetch an example graph
+# Download an example graph
 graph, schema = dgf.io.fetch_ogb_graph("arxiv")
 
-# Train a model
-model = dgf.learning.train_node_model(graph=graph, schema=schema, target_column="labels")
+# Train a GNN model to predict the "labels" feature on the "nodes" nodeset
+model = dgf.learning.train_node_model(
+    graph=graph,
+    schema=schema,
+    target_column="labels",
+    target_nodeset="nodes",
+)
 
-# Look at the model
+# Inspect training statistics, architecture, and schema
 model.describe()
 
-# Evaluate the model
+# Evaluate quality metrics
 model.evaluate()
 
-# Make predictions
+# Make low-latency predictions in-process
 model.predict(graph, seed_node_idxs=[0, 1, 2])
 
-# Save the model for later
+# Save the model (architecture, weights, sampling config, training logs, etc.)
 model.save("/tmp/model")
 ```
+
+<details>
+<summary>Output of <code>model.describe()</code></summary>
+
+<img src="https://raw.githubusercontent.com/google/distributed_graph_flow/main/doc/docs/image/usage_example_describe.png" alt="Model description" />
+
+</details>
+
+<details>
+<summary>Output of <code>model.evaluate()</code></summary>
+
+<img src="https://raw.githubusercontent.com/google/distributed_graph_flow/main/doc/docs/image/usage_example_evaluate.png" alt="Model evaluation" />
+
+</details>
+
+See the
+[Getting Started tutorial](https://dgf.readthedocs.io/en/latest/tutorial/getting_started_simple_api.html)
+for the complete walkthrough.
+
+## 🤗 Need help?
+
+-   Read the [documentation](https://dgf.readthedocs.io/) and the
+    [Q&A](https://dgf.readthedocs.io/en/latest/qna.html).
+-   **Bugs & feature requests:** Open a
+    [GitHub issue](https://github.com/google/distributed_graph_flow/issues).
+-   **Contact the team:** Email us at
+    [distributed-graph-flow-contact@google.com](mailto:distributed-graph-flow-contact@google.com).