Diverse Message Passing for Attribute with Heterophily
Authors: Liang Yang, Mengzhe Li, Liyang Liu, bingxin niu, Chuan Wang, Xiaochun Cao, Yuanfang Guo
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluations on various real networks demonstrate the superiority of our DMP on handling the networks with heterophily and alleviating the over-smoothing issue, compared to the existing state-of-the-arts. |
| Researcher Affiliation | Academia | 1School of Artiļ¬cial Intelligence, Hebei University of Technology, Tianjin, China 2State Key Laboratory of Information Security, IIE, CAS, Beijing, China 3School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen, China 4State Key Laboratory of Software Development Environment, Beihang University, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly provide an open-source code repository link or a clear statement about code availability. |
| Open Datasets | Yes | Citation networks: Cora, Citeseer, and Pubmed, which are widely used to evaluate GNNs, are the standard citation network benchmark datasets [33, 34]. Web KB webpage networks: Cornell, Texas, and Wisconsin... Co-occurrence network: Actor network... Wikipedia networks: Chameleon and Squirrel... Besides, three heterogenous information networks (HINs), i.e., DBLP, ACM and IMDB, are also employed [37]. |
| Dataset Splits | Yes | For all the datasets, nodes in each class are randomly split into three groups, 48% for training, 32% for validation, and 20% for testing, as mentioned in [8]. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Adam [38] is adopted as the optimizer for all the models' but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | The hyper-parameters, including weight decay, dropout, initial learning rate and patience for learning rate decay, are tuned by searching on the validation set. Adam [38] is adopted as the optimizer for all the models. For fair comparisons to GCN and GAT, standard DMP utilizes a two-layered model. |