{"id":24358807,"url":"https://github.com/bupt-gamma/graph-structure-estimation-neural-networks","last_synced_at":"2025-04-10T05:51:44.944Z","repository":{"id":103997648,"uuid":"347586043","full_name":"BUPT-GAMMA/Graph-Structure-Estimation-Neural-Networks","owner":"BUPT-GAMMA","description":"Source code for WWW 2021 paper \"Graph Structure Estimation Neural 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GEN\n\nSource code for WWW2021 paper [\"**Graph Structure Estimation Neural Networks**\"](https://doi.org/10.1145/3442381.3449952)\n\n\n\n## Environment Settings\n\n* python == 3.6.9\n* torch == 1.6.0\n\n\n\n## Parameter Settings\n\n- k: k of knn graph\n- threshold: threshold for adjacency matrix\n- tolerance: tolerance to stop EM algorithm\n- iter: number of iterations to train the GEN\n- base: backbone GNNs\n- seed: random seed\n- lr: learning rate\n- weight_decay: weight decay (L2 loss on parameters)\n- hidden: embedding dimension\n- dropout: dropout rate\n- activation: activation function selection\n- dataset: str in ['cora', 'citeseer', 'pubmed', 'chameleon', 'squirrel', 'actor', 'sbm']\n- epoch: number of epochs to train the base model\n\n\n\n## Files in the folder\n\n~~~~\nGEN/\n├── code/\n│   ├── train.py: training the GEN model\n│   ├── models.py: implementation of GEN and backbone GNNs\n│   ├── utils.py\n│   ├── generator.py: generating dataset based on attribute SBM\n│   ├── nx.py: saving graph structure as .gexf files for Gephi\n│   └── heatmap.py: generating heatmaps of community matrices\n├── data/\n│   ├── ind.cora.x: cora dataset\n│   ├── ind.cora.y\n│   ├── ind.cora.tx\n│   ├── ind.cora.ty\n│   ├── ind.cora.allx\n│   ├── ind.cora.ally\n│   ├── ind.cora.graph\n│   ├── ind.cora.test.index\n│   ├── ind.citeseer.x: citeseer dataset\n│   ├── ind.citeseer.y\n│   ├── ind.citeseer.tx\n│   ├── ind.citeseer.ty\n│   ├── ind.citeseer.allx\n│   ├── ind.citeseer.ally\n│   ├── ind.citeseer.graph\n│   ├── ind.citeseer.test.index\n│   ├── ind.pubmed.x: pubmed dataset\n│   ├── ind.pubmed.y\n│   ├── ind.pubmed.tx\n│   ├── ind.pubmed.ty\n│   ├── ind.pubmed.allx\n│   ├── ind.pubmed.ally\n│   ├── ind.pubmed.graph\n│   ├── ind.pubmed.test.index\n│   ├── squirrel_node_feature_label.txt: squirrel dataset\n│   ├── squirrel_graph_edges.txt\n│   ├── chameleon_node_feature_label.txt: chameleon dataset\n│   ├── chameleon_graph_edges.txt\n│   ├── actor_node_feature_label.txt: actor dataset\n│   ├── actor_graph_edges.txt\n│   ├── sbm.p: synthetic dataset\n│   └── sbm_adj.p: graph structure estimated by GEN\n└── README.md\n~~~~\n\n\n\n## Basic Usage\n\n~~~\npython ./code/train.py \n~~~\n\n\n\n## Hyper-parameter Tuning\n\nThere are three key hyper-parameters: *k*, *threshold* and *tolerance*.\n\n- k: [3, 4, 5 …, 14, 15]\n- threshold: [0.1, 0.2, 0.3, …, 0.8, 0.9]\n- tolerance: [0.1, 0.01]\n\nFor the hyper-parameter settings of six benchmark datasets used in this paper, please refer to Section 4.4.\n\n\n\n\n\n# Reference\n\n```\n@inproceedings{wang2021graph,\n  title={Graph Structure Estimation Neural Networks},\n  author={Wang, Ruijia and Mou, Shuai and Wang, Xiao and Xiao, Wanpeng and Ju, Qi and Shi, Chuan and Xie, Xing},\n  booktitle={Proceedings of the Web Conference 2021},\n  pages={342--353},\n  year={2021}\n}\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbupt-gamma%2Fgraph-structure-estimation-neural-networks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbupt-gamma%2Fgraph-structure-estimation-neural-networks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbupt-gamma%2Fgraph-structure-estimation-neural-networks/lists"}