Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
LOHA: Direct Graph Spectral Contrastive Learning Between Low-Pass and High-Pass Views
Authors: Ziyun Zou, Yinghui Jiang, Lian Shen, Juan Liu, Xiangrong Liu
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present a comprehensive evaluation of LOHA, through a series of node classification experiments on 9 real-world datasets. Comparison between LOHA with other baselines and ablation studies validate the effectiveness of LOHA and help us to gain further insights. |
| Researcher Affiliation | Academia | Ziyun Zou1, Yinghui Jiang2, Lian Shen1, Juan Liu3, Xiangrong Liu1,2* 1Department of Computer Science and Technology, Xiamen University, 2 National Institute for Data Science in Health and Medicine, Xiamen University, 3Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods through mathematical equations and textual descriptions, but does not contain a dedicated pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement regarding the release of source code or a link to a code repository. |
| Open Datasets | Yes | We choose widely used real-world datasets with different homophily levels to evaluate the performance of LOHA. (1) Homophilic Graphs: Cora, Citeseer, and Pub Med from (Yang, Cohen, and Salakhudinov 2016). (2) Heterophilic Graphs: Cornell, Texas, Actor and Wisconsin from (Pei et al. 2020); Chameleon from (Rozemberczki, Allen, and Sarkar 2021); Amazon-ratings(Amazon for short in tables) from (Platonov et al. 2023). |
| Dataset Splits | Yes | We follow the training and validation strategies as (Chien et al. 2021), where nodes are randomly split into 60%, 20%, and 20%. All comparative methods share the same fixed random splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | Output embedding size and hyper-parameters in stage 2 are also fixed for fair comparison. More detailed settings can be found in Appendix. |