GELTOR: A Graph Embedding Method based on Listwise Learning to Rank

This repository provides the reference implementation of GELTOR as well as access to the data.

Installation and usage

GELTOR is a similarity-based embedding method that applies the listwise learning-to-rank to graph embedding. It is straightforward embedding method implemented by a simple deep neural network consisting of only a projection layer and an output layer. In order to run GELTOR, the following packages are required:

Python       >= 3.8
tensorflow   >= 2.2
networkx     =2.6.*
numpy        =1.21.*
scipy        =1.7.*
scikit-learn =1.0.*

GELTOR can be run directly from the command line or migrated to your favorite IDE.

Graph file format

A graph must be represented as a text file under the edge list format in which, each line corresponds to an edge in the graph, tab is used as the separator of the two nodes, and the node index is started from 0.

Running GELTOR

GELTOR has the following parameters:

--graph: Input graph

--dataset_name: dataset name

--result_dir: Destination to save the embedding result, default is "output/" in the root directory

--dim: The embedding dimension, default is 128

--topk: Number of nodes in topK; default is -1 (i.e., topK is automatically computed as explained in the paper), otherwise set the value manually

--itr: Number of Iterations to compute AdaSim*, default is 5

--epc: Number of Epochs for training, default is 300

--bch: batch size, default is -1 (i.e., it is automatically computed as explained in the paper), otherwise set the value manually

--lr: Learning rate, default is 0.0025

--reg: Regularization parameter which is suggested to be 0.001 and 0.0001 with directed and undirected graphs, respectively; default is 0.001

--early_stop: The flag indicating to stop the training process if the loss stops improving, default is True

--wait_thr: Number of epochs with no loss improvement after which the training will be stopped, default is 20

--gpu: The flag indicating to run GELTOR on GPU, default is True

NOTE:

1- It is worth to note that topk plays an important role in GELTOR; therefore, in order to obtain the best effectiveness with any datasets, it is recommended to perform an appropriate parameter tuning on topk as explained in the paper, Section 4.3.1.

2- It is recommended to set the regularization parameter as 0.001 and 0.0001 with directed and undirected graphs, respectively.

Sample:

1. topk and bch parameters are set automatically

python GELTOR.py --graph data/DBLP/DBLP_directed_graph.txt --dataset_name DBLP

2. topk and bch parameters are set manually

python GELTOR.py --graph data/DBLP/DBLP_directed_graph.txt --dataset_name DBLP --topk 50 --bch 1024

Citation:

Masoud Reyhani Hamedani, Jin-Su Ryu, and Sang-Wook Kim. 2023. GELTOR: A Graph Embedding Method based on Listwise Learning to Rank. In Proceedings of the ACM Web Conference 2023 (WWW ’23), April 30-May 4, 2023, Austin, TX, USA, pages: 6-16. https://doi.org/10.1145/3543507.3583193