Multi-View Attribute Graph Convolution Networks for Clustering
Authors: Jiafeng Cheng, Qianqian Wang, Zhiqiang Tao, Deyan Xie, Quanxue Gao
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | 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. |