Adversarial Permutation Guided Node Representations for Link Prediction
Authors: Indradyumna Roy, Abir De, Soumen Chakrabarti9445-9453
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on diverse datasets show that PERMGNN outperforms several state-of-the-art link predictors, and can predict the most likely edges fast. |
| Researcher Affiliation | Academia | Indradyumna Roy, Abir De, Soumen Chakrabarti Indian Institute of Technology Bombay {indraroy15, abir, soumen}@cse.iitb.ac.in |
| Pseudocode | Yes | Algorithm 1: Reporting ranked list of potential edges fast. |
| Open Source Code | Yes | 1Code: https://www.cse.iitb.ac.in/ abir/codes/permgnn.zip. |
| Open Datasets | Yes | Datasets. We consider five real world datasets: (1) Twitter (Leskovec and Mcauley 2012), (2) Google+ (Leskovec et al. 2010), (3) Cora (Getoor 2005; Sen et al. 2008), (4) Citeseer (Getoor 2005; Sen et al. 2008) and (5) PB (Ackland et al. 2005). |
| Dataset Splits | Yes | Then, for each q Q, in the original graph, we partition the neighbors nbr(q) and the non-neighbors nbr(q) which are within 2-hop distance from q into 70% training, 10% validation and 20% test sets, where the node pairs are sampled uniformly at random. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or cloud computing instance types used for experiments. |
| Software Dependencies | No | The paper mentions “Torch” and “numpy” in the context of vectorized similarity computation but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | No | The paper describes the evaluation protocol and dataset splits but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |