Skip to content

Latest commit

 

History

History
148 lines (122 loc) · 8.22 KB

README.md

File metadata and controls

148 lines (122 loc) · 8.22 KB
Raw

HIF

Heterogeneous GNN Driven Information Prediction Framework

Description

Our HIF is implemented mainly based on the following libraries (see the README file in source code folder for more details):

File Tree

Our main project file structure and description:

HIF
├─ README.md
├─ environment.yaml       # our environment list
├─ data
│    ├─ processed       # processed data will be made by program
│    ├─ raw       # our raw data
├─ models       # package of models (HIF and its variants)
│    ├─ __init__.py
│    ├─ classfier.py
│    ├─ hetero.py
│    ├─ predictor.py
│    ├─ test.py
├─ nn       # package of neural network layers
│    ├─ coattention
│    ├─ conv
│    ├─ functional
│    ├─ metrics
│    ├─ nlp
│    ├─ tcn
│    ├─ time
│    ├─ __init__.py
│    ├─ conv.py	# graph convolution layer
│    ├─ embedding.py       # embedding module
│    ├─ noise.py
│    ├─ positional_encoding.py
│    ├─ reinforcements.py
│    └─ readout.py	# output layer
├─ run.py	# running entrance
└─ utils	# utils of drawing, dataloading, tensor and parameter propcessing
       ├─ data
       ├─ draw
       ├─ graph_op
       ├─ __init__.py
       ├─ additive_dict.py
       ├─ ckpt.py
       ├─ clean_lightning_logs.py
       ├─ group_tensorborad_data.py
       ├─ indexer.py

Installation

Installation requirements are described in environment.txt

  • Create Conda Environment:

    Open a terminal or command prompt and run the following command:

    conda env create -f environment.yml

  • Activate Conda Environment:

    After the installation is complete, activate the newly created Conda environment using the following command:

    conda activate <environment_name>

  • Verify Environment Installation:

    You can verify whether the environment is correctly installed:

    conda list

Usage

Before it starts running, please make sure that HIF is located in the /root/hif directory

Then, get helps for all parameters of data processing, training and optimization:

python run.py  --help

Run:

python run.py --paramater value

Experiment Settings

Here we list some basic settings of parameters for your reference:

Basic settings

Parameter Value Description
gpus 1 Number of GPUs to train on (int) or which GPUs to train on (list or str) applied per node.
time_loss_weig 5e-5 Wights of time nodes similarity.
num_workers 4 Number of workers.
num_heads 16 Number of attention heads.
log_every_n_steps 5 How often to log within steps.
batch_size 64 Batch Size.
name SSC Datasets name.
observation 1.0 Observation time.
sample 0.05 Sample.
accumulate_grad_batches 1 Accumulates grads every k batches or as set up in the dict.
in_feats 256 Dimension of inputs.
learning_rate 5e-3 Learning rate to optimize parameters.
weight_decay 5e-3 Weight decay.
l1_weight 0 L1 loss.
patience 35 Patience of early stopping.
num_time_nodes 6 Number of time nodes.
soft_partition 2 Time node soft partition size.
num_gcn_layers 2 The number of gcn layers.
readout ml The readout module.
num_readout_layers 3 The number of internal layers of readout module.
time_decay_pos all Position of time decay.
time_module transformer Time embedding module.
num_time_module_layers 4 The number of layers of time module, such as rnn layers or transformer encoder layers.
dropout 0.3 Dropout.
dropout_edge 0 Dropout edges.
noise_weight 0 Weight of noise.
noise_rate 1 Coverage rate of noise.
noise_dim 1 Dimension of noise.
hop 1 Hops of sampled follower.
alpha 10 Weight to adjust sampled follower
beta 100 Minimum number of sampled follower.
source_base 4 Weight of source nodes.
reposted_base 5 Weight of reposted nodes.
leaf_base 6 Weight of leaf nodes.
method bfs Method to sample follower.
gradient_clip_val 1 The value at which to clip gradients.
max_epochs 300 Stop training once this number of epochs is reached.
enable_progress_bar False Whether to enable to progress bar by default.
enable_model_summar False Whether to enable model summarization by default.