{"id":13680041,"url":"https://github.com/aditya-grover/node2vec","last_synced_at":"2025-04-14T05:18:49.820Z","repository":{"id":9568476,"uuid":"62104170","full_name":"aditya-grover/node2vec","owner":"aditya-grover","description":null,"archived":false,"fork":false,"pushed_at":"2022-07-21T14:37:25.000Z","size":52,"stargazers_count":2668,"open_issues_count":95,"forks_count":913,"subscribers_count":62,"default_branch":"master","last_synced_at":"2025-04-14T05:18:38.689Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"http://snap.stanford.edu/node2vec/","language":"Scala","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/aditya-grover.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2016-06-28T02:39:51.000Z","updated_at":"2025-04-09T19:27:11.000Z","dependencies_parsed_at":"2022-07-14T04:50:35.684Z","dependency_job_id":null,"html_url":"https://github.com/aditya-grover/node2vec","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/aditya-grover%2Fnode2vec","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aditya-grover%2Fnode2vec/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aditya-grover%2Fnode2vec/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aditya-grover%2Fnode2vec/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/aditya-grover","download_url":"https://codeload.github.com/aditya-grover/node2vec/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248824693,"owners_count":21167345,"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":[],"created_at":"2024-08-02T13:01:12.467Z","updated_at":"2025-04-14T05:18:49.800Z","avatar_url":"https://github.com/aditya-grover.png","language":"Scala","funding_links":[],"categories":["Graph Embedding","Scala","[Label Inference Methods](#content)","图嵌入、网络表征学习"],"sub_categories":["Graph Embedding Approaches","网络服务_其他"],"readme":"# node2vec\n\nThis repository provides a reference implementation of *node2vec* as described in the paper:\u003cbr\u003e\n\u003e node2vec: Scalable Feature Learning for Networks.\u003cbr\u003e\n\u003e Aditya Grover and Jure Leskovec.\u003cbr\u003e\n\u003e Knowledge Discovery and Data Mining, 2016.\u003cbr\u003e\n\u003e \u003cInsert paper link\u003e\n\nThe *node2vec* algorithm learns continuous representations for nodes in any (un)directed, (un)weighted graph. Please check the [project page](https://snap.stanford.edu/node2vec/) for more details. \n\n### Basic Usage\n\n#### Example\nTo run *node2vec* on Zachary's karate club network, execute the following command from the project home directory:\u003cbr/\u003e\n\t``python src/main.py --input graph/karate.edgelist --output emb/karate.emd``\n\n#### Options\nYou can check out the other options available to use with *node2vec* using:\u003cbr/\u003e\n\t``python src/main.py --help``\n\n#### Input\nThe supported input format is an edgelist:\n\n\tnode1_id_int node2_id_int \u003cweight_float, optional\u003e\n\t\t\nThe graph is assumed to be undirected and unweighted by default. These options can be changed by setting the appropriate flags.\n\n#### Output\nThe output file has *n+1* lines for a graph with *n* vertices. \nThe first line has the following format:\n\n\tnum_of_nodes dim_of_representation\n\nThe next *n* lines are as follows:\n\t\n\tnode_id dim1 dim2 ... dimd\n\nwhere dim1, ... , dimd is the *d*-dimensional representation learned by *node2vec*.\n\n### Citing\nIf you find *node2vec* useful for your research, please consider citing the following paper:\n\n\t@inproceedings{node2vec-kdd2016,\n\tauthor = {Grover, Aditya and Leskovec, Jure},\n\t title = {node2vec: Scalable Feature Learning for Networks},\n\t booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},\n\t year = {2016}\n\t}\n\n\n### Miscellaneous\n\nPlease send any questions you might have about the code and/or the algorithm to \u003cadityag@cs.stanford.edu\u003e.\n\n*Note:* This is only a reference implementation of the *node2vec* algorithm and could benefit from several performance enhancement schemes, some of which are discussed in the paper.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faditya-grover%2Fnode2vec","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faditya-grover%2Fnode2vec","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faditya-grover%2Fnode2vec/lists"}