LSPAN: Spectrally Localized Augmentation for Graph Consistency Learning
Authors: Heng-Kai Zhang, Yi-Ge Zhang, Zhi Zhou, Yu-Feng Li
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical evaluation on real-world datasets clearly shows the performance gain of spectrally localized augmentation, as well as its good convergence and efficiency compared to existing graph methods. In this section, we give a comprehensive evaluation of the LSPAN method, including the prediction results, convergence analysis and the ablation study. |
| Researcher Affiliation | Academia | Heng-Kai Zhang , Yi-Ge Zhang , Zhi Zhou and Yu-Feng Li National Key Laboratory for Novel Software Technology, Nanjing University, China School of Artificial Intelligence, Nanjing University, China {zhanghk,zhangyg,zhouz,liyf}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 Augmentation Phase of LSPAN Input: Original graph G = (X, A), eigenvectors of graph Laplacian {ui}N i=1, parameters m and n, temperature T Output: Augmented graph G 1: Obtain the adjacency matrix: A = A. 2: Compute the summation of eigenvectors: u = (um + um+1 + um+n 1). 3: Generate the augmented feature matrix: X = [X ; Tu ]. 4: return G = (X , A ) |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We perform evaluations on six publicly available benchmarks across four domains: i) citation networks, including CORA and CITESEER [Kipf and Welling, 2017]; ii) protein-protein interactions, including PPI [Hamilton et al., 2017]; iii) social networks, including BLOGCATALOG and FLICKR [Huang et al., 2017]; iv) air traffic, including AIRUSA [Wu et al., 2019]. Statistics and splits of them are summarized in Appendix C.1. |
| Dataset Splits | Yes | We follow the standard semi-supervised graph learning procedure [Kipf and Welling, 2017; Veliˇckovi c et al., 2018]. The setup and implementation details of LSPAN can be found in Appendix C.3. Datasets. We perform evaluations on six publicly available benchmarks across four domains: i) citation networks, including CORA and CITESEER [Kipf and Welling, 2017]; ii) protein-protein interactions, including PPI [Hamilton et al., 2017]; iii) social networks, including BLOGCATALOG and FLICKR [Huang et al., 2017]; iv) air traffic, including AIRUSA [Wu et al., 2019]. Statistics and splits of them are summarized in Appendix C.1. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. It only mentions 'The setup and implementation details of LSPAN can be found in Appendix C.3.' |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Specifically, given an input graph G with feature matrix X and adjacency matrix A, we first generate S augmented graphs by Equation (6) where T, n are set as hyper-parameters and we randomly choose m from 1 to N n + 1 for each augmentation. The setup and implementation details of LSPAN can be found in Appendix C.3. |