CommunityNet

KO | EN

gitlite — search

#javascript #python #hacktoberfest #golang #go #machine-learning #deep-learning #linux #docker #java #awesome #vue #pytorch #ios

(★ 19)

Hierarchical GNN for graph datasets with community structure

CommunityNet 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# CommunityNet

`CommunityNet` is a hierarchical Graph Neural Network (GNN) designed for graph datasets with community structure (e.g. social networks, molecules, etc.). It's designed to encode information at both the within-community level and the inter-community level.

## Installation

You can download `CommunityNet` from PyPi:

```bash
$ pip install communitynet
```

## Usage

Before instantiating `CommunityNet`, you must define a "base" GNN and an "output" GNN. The _base GNN_ is used to create vector embeddings of each community in an input graph. These embeddings are used as node features in an "inter-community" graph, where each node represents a community and each edge is the mean of the edges between two communities. This graph is submitted to the _output GNN_ to make a prediction. Both GNNs can be constructed using the `GraphNet` and `MLP` PyTorch modules supplied by the library. For example, to construct the `CommunityNet` shown in the diagram above, you can do the following:

```python
import torch.nn as nn
from communitynet import GraphNet, MLP, CommunityNet

# Example numbers (arbitrary)
num_node_features = 4
num_edge_features = 2

base_gnn = GraphNet(in_channels=num_node_features, out_channels=8,
                    num_edge_features=num_edge_features)
output_gnn = nn.Sequential(
  GraphNet(in_channels=8, out_channels=4, num_edge_features=num_edge_features),
  MLP(in_channels=4, out_channels=1)
)
community_net = CommunityNet(base_gnn, output_gnn, num_communities=3)
```

`GraphNet` and `MLP` both have additional hyperparameters (e.g. hidden layers, dropout, etc.) which are described in the reference below. The `CommunityNet` class itself derives from `torch.nn.Module`, so it can be trained like any other PyTorch model.

Each graph you submit to `CommunityNet` must be an instance of `torch_geometric.data.Data` with an additional `communities` attribute. `data.communities` should hold a list of communities, where each community is a set of node indices. For example:

```python
import torch
from torch_geometric.data import Data

edge_index = torch.tensor([[0, 1, 0, 2, 1, 2, 2, 3, 3, 4, 3, 5, 4, 5],
                           [1, 0, 2, 0, 2, 1, 3, 2, 4, 3, 5, 3, 5, 4]],
                          dtype=torch.long)
x = torch.tensor([[-1], [0], [1], [0.5], [0.75], [-0.25]], dtype=torch.float)

data = Data(x=x, edge_index=edge_index)
data.communities = [{0, 1, 2}, {3, 4, 5}]
```

Note that every graph in your dataset must have the same number of communities.

## Reference

### GraphNet

PyTorch module that implements a GNN. Uses `NNConv` (an edge-conditioned convolutional operator) as a filter and global pooling to convert a graph into a vector embedding.

**Parameters:**

1. **`in_channels` _(int)_:** Number of node features
2. **`out_channels` _(int)_:** Number of output features
3. **`num_edge_features` _(int)_:** Number of edge features
4. **`hidden_channels` _(list, optional (default=[]))_:** List of hidden state sizes; length of list == number of layers
5. **`use_pooling` _(bool, optional (default=False))_:** Whether or not to use top-k pooling
6. **`dropout_prob` _(float, optional (default=0.0))_:** Dropout probability applied to each GNN layer
7. **`global_pooling` _(str, optional (default="mean"))_:** Global pooling mode; options are: "mean", "add", and "max"
8. **`activation` _(torch.nn.Module, optional (default=None))_:** Activation function used for `NNConv`
9. **`edge_nn_kwargs` _(dict, optional (default={}))_:** Dictionary of parameters for the MLP used to process edge features in `NNConv`

### MLP

PyTorch module that implements a multi-layer perceptron. This can be used in an output GNN to convert a graph embedding into a prediction (e.g. a classification/regression).

**Parameters:**

1. **`in_channels` _(int)_:** Number of input features
2. **`out_channels` _(int)_:** Number of output features
3. **`hidden_channels` _(list, optional (default=[]))_:** List of hidden state sizes; length of list == number of layers
4. **`h_activation` _(torch.nn.Module, optional (default=None))_:** Hidden activation function
5. **`out_activation` _(torch.nn.Module, optional (default=None))_:** Output activation function

### CommunityNet

PyTorch module that implements a hierarchical GNN.

**Parameters:**

1. **`base_gnn` _(torch.nn.Module)_:** Base GNN used to process each community
2. **`output_gnn` _(torch.nn.Module)_:** Output GNN used to process the inter-community graph and produce a prediction
3. **`num_communities` _(int)_:** Number of communities in each input graph
4. **`num_jobs` _(int, optional (default=1))_:** Number of jobs (CPU cores) to distribute the community embedding work across

# Authors

`CommunityNet` was created by Jonathan Shobrook with the help of [Paul C. Bogdan](https://github.com/paulcbogdan/) as part of our research in the [Dolcos Lab](https://dolcoslab.beckman.illinois.edu/) at the Beckman Institute for Advanced Science and Technology.

CommunityNet

CommunityNet 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

CommunityNet

CommunityNet 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README