add demo usage

README.md

@@ -1,4 +1,64 @@
 # HIN2vec_pytorch
 a pytorch implementation of [HIN2vec](https://github.com/csiesheep/hin2vec)
 
-in progress
+
+>*HIN2Vec* learns distributed representations of nodes in heterogeneous information networks (HINs) by capturing the distiguishing metapath relationships between nodes. 
+Please refer the paper [here](https://dl.acm.org/citation.cfm?doid=3132847.3132953).
+
+
+### Usage
+create your own edge.csv refer to `demo_data.csv`
+
+create a main.py in the project folder, copy the following code and modify it.
+```python
+    import torch
+    import pandas as pd
+    import torch.nn as nn
+    import torch.optim as optim
+    from torch.utils.data import DataLoader
+
+    from walker import load_a_HIN_from_pandas
+    from model import NSTrainSet, HIN2vec, train
+
+    # set parameters
+    window = 4
+    walk_length = 300
+    embed_size = 100
+    neg = 5
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    print(f'device = {device}')
+
+    # set dataset [PLEASE USE YOUR OWN DATASET TO REPLACE THIS]
+    demo_edge = pd.read_csv('./demo_data.csv', index_col=0)
+
+    edges = [demo_edge]
+
+    print('finish loading edges')
+
+    # init HIN
+    hin = load_a_HIN_from_pandas(edges)
+    hin.window = window
+
+    dataset = NSTrainSet(hin.sample(walk_length), hin.path_size, neg=neg)
+
+    hin2vec = HIN2vec(hin.node_size, hin.path_size, embed_size)
+
+    # set parameters
+    n_epoch = 10
+    batch_size = 20
+    log_interval = 200
+
+    data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
+    optimizer = optim.Adam(hin2vec.parameters())  # 原作者使用的是SGD？ 这里使用Adam
+    loss_function = nn.BCELoss()
+
+    for epoch in range(n_epoch):
+        train(log_interval, hin2vec, device, data_loader, optimizer, loss_function, epoch)
+
+    torch.save(hin2vec, 'hin2vec.pt')
+
+    hin2vec.output_embeddings('start_node_embed.txt', 'end_node_embed.txt', 'path_embed.txt')
+
+```
+
+Some more details could be found in [the original repo](https://github.com/csiesheep/hin2vec)