{"id":11563734,"url":"https://github.com/VachelHU/EvoNet","last_synced_at":"2025-10-03T14:31:14.571Z","repository":{"id":53582147,"uuid":"185926858","full_name":"VachelHU/EvoNet","owner":"VachelHU","description":"Time-Series Event Prediction with Evolutionary State Graph, WSDM 2021","archived":false,"fork":false,"pushed_at":"2021-01-04T02:49:38.000Z","size":69669,"stargazers_count":88,"open_issues_count":15,"forks_count":26,"subscribers_count":7,"default_branch":"master","last_synced_at":"2024-09-29T14:31:50.603Z","etag":null,"topics":["deep-learning","time-series-analysis"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/VachelHU.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-05-10T05:54:45.000Z","updated_at":"2024-07-27T15:00:33.000Z","dependencies_parsed_at":"2022-09-02T01:12:14.288Z","dependency_job_id":null,"html_url":"https://github.com/VachelHU/EvoNet","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VachelHU%2FEvoNet","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VachelHU%2FEvoNet/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VachelHU%2FEvoNet/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VachelHU%2FEvoNet/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/VachelHU","download_url":"https://codeload.github.com/VachelHU/EvoNet/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":235146413,"owners_count":18943257,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","time-series-analysis"],"created_at":"2024-06-23T05:59:11.591Z","updated_at":"2025-10-03T14:31:09.517Z","avatar_url":"https://github.com/VachelHU.png","language":"Python","funding_links":[],"categories":["时间序列"],"sub_categories":["网络服务_其他"],"readme":"# EvoNet\nThis project implements the Evolutionary State Graph Neural Network proposed in [1], which is a GNN-based method for time-series event prediction.\n\n## Compatibility\n\nCode is compatible with tensorflow version 1.2.0 and Pyhton 3.6.2.\n\nSome Python module dependencies are listed in `requirements.txt`, which can be easily installed with pip:\n\n```\npip install -r requirements.txt\n```\n\n### Input Format \n\nAn example data format is given where data is stored as a list containing 4 dimensionals tensors such as\n \n`[number of samples × segment number × segment length × dimension of observation]`\n\n\n### Configuration\nWe can use `./model_core/config.py` to set the parameters of model.\n\n```\nclass ModelParam(object):\n    # basic\n    model_save_path = \"./model\"\n    n_jobs = os.cpu_count()\n\n    # dataset\n    data_path = './data'\n    data_name = 'webtraffic'\n    his_len = 15\n    segment_len = 24\n    segment_dim = 2\n    n_event = 2\n    norm = True\n\n    # state recognition\n    n_state = 30\n    covariance_type = 'diag'\n\n    # model\n    graph_dim = 256\n    node_dim = 96\n    learning_rate = 0.001\n    batch_size = 1000\n    id_gpu = '0'\n    pos_weight = 1.0\n```\n\n\n### Main Script\n\n```\npython run.py -h\n\nusage: run.py [-h] [-d {djia30, webtraffic}] [-g GPU]\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -d {djia30,webtraffic}, --dataset {djia30,webtraffic} select the dataset\n  -g GPU, --gpu GPU     target gpu id\n```\n\n## Reference\n\n[1] Wenjie, H; Yang, Y; Ziqiang, C; Carl, Y and Xiang, R, 2021, Time-Series Event Prediction with Evolutionary State Graph, In WSDM, 2021\n```\n@inproceedings{hu2021evonet, \n    title={Time-Series Event Prediction with Evolutionary State Graph},\n    author={Wenjie Hu and Yang Yang and Ziqiang Cheng and Carl Yang and Xiang Ren},\n    booktitle={Proceedings of WSDM},\n    year={2021}\n}\n```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FVachelHU%2FEvoNet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FVachelHU%2FEvoNet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FVachelHU%2FEvoNet/lists"}