Add files via upload

CONTRIBUTING.md

@@ -0,0 +1,31 @@
+# Contributing guidelines
+
+## How to become a contributor and submit your own code
+
+### Contributor License Agreements
+
+We'd love to accept your patches! Before we can take them, we have to jump a
+couple of legal hurdles.
+
+Please fill out either the individual or corporate Contributor License Agreement
+(CLA).
+
+*   If you are an individual writing original source code and you're sure you
+    own the intellectual property, then you'll need to sign an [individual
+    CLA](http://code.google.com/legal/individual-cla-v1.0.html).
+*   If you work for a company that wants to allow you to contribute your work,
+    then you'll need to sign a [corporate
+    CLA](http://code.google.com/legal/corporate-cla-v1.0.html).
+
+Follow either of the two links above to access the appropriate CLA and
+instructions for how to sign and return it. Once we receive it, we'll be able to
+accept your pull requests.
+
+***NOTE***: Only original source code from you and other people that have signed
+the CLA can be accepted into the main repository.
+
+### Contributing code
+
+If you have improvements to this library, send us your pull requests! For those
+just getting started, GitHub has a
+[howto](https://help.github.com/articles/using-pull-requests/).

LICENSE

@@ -1,3 +1,4 @@
+
                                  Apache License
                            Version 2.0, January 2004
                         http://www.apache.org/licenses/
@@ -199,3 +200,4 @@
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License.
+

README.md

@@ -0,0 +1,118 @@
+# Graph Nets library
+
+[Graph Nets](https://github.com/deepmind/graph_nets) is DeepMind's library for
+building graph networks in Tensorflow and Sonnet.
+
+Contact graph-nets@google.com for comments and questions.
+
+#### What are graph networks?
+
+A graph network takes a graph as input and returns a graph as output. The input
+graph has edge- (*E* ), node- (*V* ), and global-level (**u**) attributes. The
+output graph has the same structure, but updated attributes. Graph networks are
+part of the broader family of "graph neural networks" (Scarselli et al., 2009).
+
+To learn more about graph networks, see our arXiv paper: [Relational inductive
+biases, deep learning, and graph networks](https://arxiv.org/abs/1806.01261).
+
+![Graph network](images/graph-network.png)
+
+## Installation
+
+The Graph Nets library can be installed from pip.
+
+This installation is compatible with Linux/Mac OS X, and Python 2.7 and 3.4+.
+
+To install the library, run:
+
+```shell
+$ pip install graph_nets
+```
+
+## Usage example
+
+The following code constructs a simple graph net module and connects it to data.
+
+```python
+import graph_nets as gn
+import sonnet as snt
+
+# Provide your own functions to generate graph-structured data.
+input_graphs = get_graphs()
+
+# Create the graph network.
+graph_net_module = gn.modules.GraphNetwork(
+    edge_model_fn=lambda: snt.nets.MLP([32, 32]),
+    node_model_fn=lambda: snt.nets.MLP([32, 32]),
+    global_model_fn=lambda: snt.nets.MLP([32, 32]))
+
+# Pass the input graphs to the graph network, and return the output graphs.
+output_graphs = graph_net_module(input_graphs)
+```
+
+## Demo Jupyter notebooks
+
+The library includes demos which show how to create, manipulate, and
+train graph networks to reason about graph-structured data, on a
+shortest path-finding task, a sorting task, and a physical prediction task.
+Each demo uses the same graph network architecture, which highlights the
+flexibility of the approach.
+
+### Try the demos in your browser in [Colaboratory](https://colab.research.google.com)
+
+To try out the demos without installing anything locally, you can run the demos
+in your browser (even on your phone) via a cloud Colaboratory backend. Click a
+demo link below, and follow the instructions in the notebook.
+
+----------------
+
+#### [Run "shortest path demo" in browser](https://colab.research.google.com/github/deepmind/graph_nets/blob/master/demos/shortest_path.ipynb)
+
+The "shortest path demo" creates random graphs, and trains a graph network to
+label the nodes and edges on the shortest path between any two nodes. Over a
+sequence of message-passing steps (as depicted by each step's plot), the
+model refines its prediction of the shortest path.
+
+![Shortest path](images/shortest-path.png)
+
+----------------
+
+#### [Run "sort demo" in browser](https://colab.research.google.com/github/deepmind/graph_nets/blob/master/demos/sort.ipynb)
+
+The "sort demo" creates lists of random numbers, and trains a graph network to
+sort the list. After a sequence of message-passing steps, the model makes an
+accurate prediction of which elements (columns in the figure) come next after
+each other (rows).
+
+![Sort](images/sort.png)
+
+----------------
+
+#### [Run "physics demo" in browser](https://colab.research.google.com/github/deepmind/graph_nets/blob/master/demos/physics.ipynb)
+
+The "physics demo" creates random mass-spring physical systems, and trains a
+graph network to predict the state of the system on the next timestep. The
+model's next-step predictions can be fed back in as input to create a rollout of
+a future trajectory. Each subplot below shows the true and predicted mass-spring
+system states over 50 steps. This is similar to the model and experiments in
+Battaglia et al. (2016)'s "interaction networks".
+
+![Physics](images/physics.png)
+
+----------------
+
+### Run the demos on your local machine
+
+To install the necessary dependencies, run:
+
+```shell
+$ pip install jupyter matplotlib scipy
+```
+
+To try the demos, run:
+
+```shell
+$ cd <path-to-graph-nets-library>/demos
+$ jupyter notebook
+```
+then open a demo through the Jupyter notebook interface.

setup.py

@@ -0,0 +1,61 @@
+# Copyright 2018 The GraphNets Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Setuptools installation script."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from setuptools import find_packages
+from setuptools import setup
+
+description = """Graph Nets is DeepMind's library for building graph networks in
+Tensorflow and Sonnet.
+"""
+
+setup(
+    name="graph_nets",
+    version="1.0.0",
+    description="Library for building graph networks in Tensorflow and Sonnet.",
+    long_description=description,
+    author="DeepMind",
+    license="Apache License, Version 2.0",
+    keywords=["graph networks", "tensorflow", "sonnet", "machine learning"],
+    url="https://github.com/deepmind/graph-nets",
+    packages=find_packages(),
+    install_requires=[
+        "absl-py",
+        "dm-sonnet",
+        "future",
+        "networkx",
+        "numpy",
+        "setuptools",
+        "six",
+        "tensorflow",
+    ],
+    classifiers=[
+        "Development Status :: 4 - Beta",
+        "Intended Audience :: Developers",
+        "Intended Audience :: Education",
+        "Intended Audience :: Science/Research",
+        "License :: OSI Approved :: Apache Software License",
+        "Operating System :: POSIX :: Linux",
+        "Operating System :: Microsoft :: Windows",
+        "Operating System :: MacOS :: MacOS X",
+        "Programming Language :: Python :: 2.7",
+        "Programming Language :: Python :: 3.4",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    ],
+)