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..
Multi-View Attribute Graph Convolution Networks for Clustering
Authors: Jiafeng Cheng, Qianqian Wang, Zhiqiang Tao, Deyan Xie, Quanxue Gao
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three benchmark graph databases show the effectiveness of our method compared with several state-of-the-art algorithms. |
| Researcher Affiliation | Academia | Jiafeng Cheng1 , Qianqian Wang1 , Zhiqiang Tao2 , Deyan Xie1 and Quanxue Gao1,3 1State Key Laboratory of Integrated Services Networks, Xidian University 2Northeastern University 3Unmanned System Research Institue, Northwestern Polytechnical university |
| Pseudocode | No | The paper describes the proposed methodology using mathematical equations and textual explanations, but does not include a dedicated pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | We conduct extensive experiments on three citation network databases (Cora, Citeseer and Pubmed) [Sen et al., 2008] |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits (e.g., percentages or counts) needed to reproduce the experiment. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., libraries, frameworks, or programming language versions) used for the experiments. |
| Experiment Setup | Yes | In our experiments, we used two layers of multi-view attribute graph convolution encoders for all three databases. For Cora database, node representation dimensions of the two layer are set as [512, 512]. As to the Citeseer database, node representation dimensions of the two layer are set as [2000, 512]. For Pubmed, the dimension of two-layer multi-view attribute graph convolution auto-encoder is [128, 64]. In integrate-encoder, we use a fully connected layer in all three databases. We use non-linear activation function σ as Relu function in the multi-view graph convolution auto-encoder. As for regular term coefficient λ1, λ2 and λ3, we set λ1 as 1. λ2 and λ3 are set range from 10−2 to 102, and analyze the influence of parameters later in Sec. 4.2: Impact of Loss Coefficient. |