huihun/graphsage-tf
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Update README.md

Browse Source
This commit is contained in:
William L Hamilton 2017-05-30 18:32:35 +01:00 committed by GitHub
parent 23c819d98d
commit c304fe0357
1 changed files with 8 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
README.md
View File

@ -20,9 +20,10 @@ Recent versions of TensorFlow, numpy, scipy, and networkx are required.
### Running the code ### Running the code
The example_unsupervised.sh and example_supervised.sh files contain example usages of the code. The example_unsupervised.sh and example_supervised.sh files contain example usages of the code, which use the unsupervised and supervised variants of GraphSAGE, respectively.
(example_unsupervised.sh sets a very small max iteration number, which can be increased to improve performance.) Note that example_unsupervised.sh sets a very small max iteration number, which can be increased to improve performance.
#### Input format
As input, at minimum the code requires that a --train_prefix option is specified which specifies the following data files: As input, at minimum the code requires that a --train_prefix option is specified which specifies the following data files:
* <train_prefix>-G.json -- "A networkx-specified json file describing the input graph." * <train_prefix>-G.json -- "A networkx-specified json file describing the input graph."
@ -31,6 +32,7 @@ As input, at minimum the code requires that a --train_prefix option is specified
* <train_prefix>-feats.npy --- "A numpy-stored array of node features; ordering given by id_map.json" * <train_prefix>-feats.npy --- "A numpy-stored array of node features; ordering given by id_map.json"
* <train_prefix>-walks.txt --- "A text file specifying random walk co-occurrences (one pair per line)" (*only for unsupervised) * <train_prefix>-walks.txt --- "A text file specifying random walk co-occurrences (one pair per line)" (*only for unsupervised)
#### Model variants
The user must also specify a --model, the variants of which are described in detail in the paper: The user must also specify a --model, the variants of which are described in detail in the paper:
* graphsage_mean -- GraphSAGE with mean-based aggregator * graphsage_mean -- GraphSAGE with mean-based aggregator
* graphsage_seq -- GraphSAGE with LSTM-based aggregator * graphsage_seq -- GraphSAGE with LSTM-based aggregator
@ -38,10 +40,13 @@ The user must also specify a --model, the variants of which are described in det
* gcn -- GraphSAGE with GCN-based aggregator * gcn -- GraphSAGE with GCN-based aggregator
* n2v -- an implementation of [DeepWalk](https://arxiv.org/abs/1403.6652) (called n2v for short in the code.) * n2v -- an implementation of [DeepWalk](https://arxiv.org/abs/1403.6652) (called n2v for short in the code.)
#### Logging directory
Finally, a --base_log_dir should be specified (it defaults to the current directory). Finally, a --base_log_dir should be specified (it defaults to the current directory).
The output of the model and log files will be stored in a subdirectory of the base_log_dir. The output of the model and log files will be stored in a subdirectory of the base_log_dir.
The supervised model will output F1 scores, while the unsupervised model will train embeddings and store them. The supervised model will output F1 scores, while the unsupervised model will train embeddings and store them.
The unsupervised embeddings will be stored at val.npy with val.txt specifying the order of embeddings as a per-line list of node ids. The unsupervised embeddings will be stored at val.npy with val.txt specifying the order of embeddings as a per-line list of node ids.
Note that the full log outputs and stored embeddings can be 5-10Gb in size (on the full data). Note that the full log outputs and stored embeddings can be 5-10Gb in size (on the full data).
The other inputs and hyperparameters are described in the TensorFlow flags. #### Using the output of the unsupervised models
TODO