A PyTorch implementation of the "Gossip and Attend: Context-sensitive Graph Representation Learning" paper, to appear on the International AAAI Conference on Web and Social Media (ICWSM 2020)

• Python 3.6+
• PyTorch 1.3.1+
• networkx 2.3+
• Numpy 1.17.2+

• Scikit-learn 0.20.3+
• Pandas 0.24.2+

$ bash ./goat.sh

or

$ python src/main.py

--input: A path to a graph file. Default is ./data/cora/graph.txt

--fmt: The format of the input graph, either edgelist or adjlist. Default is edgelist

--output-dir: A path to a directory to save intermediate and final outputs of GOAT. Default is ./data/cora/outputs

--dim: The size (dimension) of nodes' embedding (representation) vector. Default is 200.

--epochs: The number of epochs. Default is 100.

--tr-rate: Training rate, i.e. the fraction of edges to be used as a training set. A value in (0, 1]. Default is .15. The remaining fraction of edges (1 - tr_rate), test edges, will be saved in the directory specified by --ouput-dir argument.

--dev-rate: Development rate, i.e. the fraction of the training set to be used as a development (validation) set. A value in [0, 1). Default is 0.2.

--learning-rate: Learning rate, a value in [0, 1]. Default is 0.0001

--dropout-rate: Dropout rate, a value in [0, 1]. Deafult is 0.5

--nbr-size: The number of neighbors to be sampled. Default is 100.

--directed: Whether the graph is directed or not. 1 for directed and 0 for undirected. Default is 1.

--workers: The number of parallel workers. Default is 8.

--verbose:. Whether to turn on a verbose logger or not. 1 is on and 0 is off. Default is 1.

If you find GOAT useful in your research, we kindly ask that you cite the following paper:

@inproceedings{ZekariasICWSM2020,
  author    = {Zekarias T. Kefato and
               Sarunas Girdzijauskasr},
  title     = {Gossip and Attend: Context-sensitive Graph Representation Learning},
  booktitle = {in Proceedings of the International AAAI Conference on Web and Social Media (Association for the Advancement of Artificial Intelligence, 2020).},
  year      = {2020},
  month     = June,
  type      = {CONFERENCE},
}