Topological Relational Learning on Graphs
Authors: Yuzhou Chen, Baris Coskunuzer, Yulia Gel
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on node classification tasks demonstrate that the new TRI-GNN outperforms all 14 state-of-the-art baselines on 6 out 7 graphs and exhibit higher robustness to perturbations, yielding up to 10% better performance under noisy scenarios.Our expansive node classification experiments show that TRI-GNN outperforms 14 state-of-the-art baselines on 6 out 7 graphs and delivers substantially higher robustness (i.e., up to 10% in performance gains under noisy scenarios) than baselines on all 7 datasets.We now empirically evaluate the effectiveness of our proposed method on seven node-classification benchmarks under semi-supervised setting with different graph size and feature type. We run all experiments for 50 times and report the average accuracy results and standard deviations.Table 1 shows the results for node classification results on graphs. |
| Researcher Affiliation | Academia | Yuzhou Chen Department of Electrical Engineering Princeton University yc0774@princeton.edu Baris Coskunuzer Department of Mathematical Sciences University of Texas at Dallas coskunuz@utdallas.edu Yulia R. Gel Department of Mathematical Sciences University of Texas at Dallas and National Science Foundation ygl@utdallas.edu |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as "Pseudocode" or "Algorithm". |
| Open Source Code | Yes | The source code of TRI-GNN is publicly available at https://github.com/TRI-GNN/TRI-GNN.git. |
| Open Datasets | Yes | Datasets We compare TRI-GNN with the state-of-the-art (SOA) baselines, using standard publicly available real and synthetic networks: (1) 3 citation networks [41]: Cora-ML, Cite Seer, and Pub Med, where nodes are publications and edges are citations; (2) 4 synthetic power grid networks [8, 7, 23]: IEEE 118-bus system, ACTIVSg200 system, ACTIVSg500 system, and ACTIVSg2000 system, where each node represents a load bus, transformer, or generator and we use total line charging susceptance (BR_B) as edge weight. |
| Dataset Splits | No | The provided text mentions |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., specific GPU or CPU models, memory, or cluster specifications). |
| Software Dependencies | No | The paper does not list specific software dependencies with their version numbers (e.g., programming languages, libraries, or frameworks with version details). |
| Experiment Setup | Yes | Selection of hyperparameters ϵ1 and ϵ2 can be performed by assessing quantiles of the empirical distribution of shape similarities and then cross-validation.For instance, the optimal quantile of ϵ1 and ϵ2 for ACTIVSg200 dataset is 0.55 and 2.50 respectively.For ϵ1, we generate a sequence from 0.50 to 2.00 with increment of the sequence 0.05; for ϵ2, we generate a sequence from 2.50 to 6.74 with increment of the sequence 0.5.In the experiments µ is selected from {0.1, 0.2, . . . , 0.9}... |