{"id":20723606,"url":"https://github.com/networks-learning/influmax","last_synced_at":"2025-06-10T09:04:05.940Z","repository":{"id":6094017,"uuid":"7321028","full_name":"Networks-Learning/influmax","owner":"Networks-Learning","description":"Influmax C++/SNAP","archived":false,"fork":false,"pushed_at":"2012-12-25T22:47:39.000Z","size":1064,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-11T08:31:44.651Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"C++","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/Networks-Learning.png","metadata":{"files":{"readme":"ReadMe.txt","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":"2012-12-25T22:34:20.000Z","updated_at":"2015-11-10T09:51:04.000Z","dependencies_parsed_at":"2022-09-24T02:51:32.309Z","dependency_job_id":null,"html_url":"https://github.com/Networks-Learning/influmax","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/Networks-Learning%2Finflumax","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Networks-Learning%2Finflumax/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Networks-Learning%2Finflumax/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Networks-Learning%2Finflumax/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Networks-Learning","download_url":"https://codeload.github.com/Networks-Learning/influmax/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Networks-Learning%2Finflumax/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259043761,"owners_count":22797159,"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":"2024-11-17T04:09:13.105Z","updated_at":"2025-06-10T09:04:05.914Z","avatar_url":"https://github.com/Networks-Learning.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"==============================================================================\r\n    INFLUMAX: Influence Maximization in Continuous Time Diffusion Networks\r\n==============================================================================\r\n\r\nThe problem of finding the optimal set of source nodes in a diffusion\r\nnetwork that maximizes the spread of information, influence, and diseases in\r\na limited amount of time depends dramatically on the underlying temporal\r\ndynamics of the network. However, this still remains largely unexplored to\r\ndate. To this end, we have developed INFLUMAX, an efficient approximation\r\nalgorithm with provable near-optimal performance to find the most\r\ninfluential set of source nodes in the continuous time influence\r\nmaximization problem. The algorithm computes analytically the influence\r\nusing CTMCs and exploits submodularity for the optimization.\r\n\r\nFor more information about the procedure see:\r\n  Influence Maximization in Continuous Time Diffusion Networks\r\n  Manuel Gomez-Rodriguez, Bernhard Schölkopf\r\n  http://www.stanford.edu/~manuelgr/influmax/\r\n  \r\nIn order to compile on MacOS: 'make' OR 'make opt'.\r\nIn order to compile in Linux: 'make linux' OR 'make opt_linux'. \r\nYou will need to install gfortran to make it work.\r\n\r\nFor networks operations, our code uses the library SNAP (http://snap.stanford.edu), developed by Jure Leskovec.\r\nFor computing dominator trees, we hacked the code at www.cs.princeton.edu/~rwerneck/dominators/,\r\ndeveloped by L. Georgiadis, R. E. Tarjan, and R. F. Werneck. \r\nFor computing matrix exponentials, we hacked the Expokit (http://www.maths.uq.edu.au/expokit), developed by R. B. Sidje\u003c/a\u003e.\r\n\r\n/////////////////////////////////////////////////////////////////////////////\r\nParameters:\r\n\r\n   -n:Input ground-truth network (one file) (default:'example-network.txt')\r\n   -o:Output file name(s) prefix (default:'network')\r\n   -s:Number of sources (default:1)\r\n   -t:Time window (default:10)\r\n   -e:Matrix exponential. 0:Parlett, 1:Krylov, 2:Hessenberg, 3:Krylov(big)+Hessenberg(small) (default:3)\r\n   -mr:Maximum length for minimum reachable path from source(s) (default:-1, no maximum length)\r\n   -mp:Maximum length for diffusion path from source(s) (default:-1, no maximum length)\r\n   -b:Baselines\r\n    0:INFLUMAX, 1:random sources, 2:outdegree, 3:complete search, 4:given source list, 5:given source file (default:0)\r\n   -ir:Repetitions random sources selection if -b:1 (default:100)\r\n   -ls:List of sources (id1;id2;id3) if -b:4 or source filename if -b:5 (default:'')\r\n\r\n\r\n/////////////////////////////////////////////////////////////////////////////\r\nUsage:\r\n\r\n./influmax -i:example_network.txt\r\n\r\nIt outputs two text files: influence-average-*.txt and influence-info-*.txt, where * is the argument given with -o. The format of \r\nthe output files is described in the files themselves.\r\n\r\n/////////////////////////////////////////////////////////////////////////////\r\n\r\nFormat gound truth:\r\n\r\nThe ground truth input file should have two blocks separated by a blank line\r\n- A first block with a line per node. The format of every line is \u003cid\u003e,\u003cname\u003e\r\n- A second block with a line per directed edge. The format of every line is \u003cid1\u003e,\u003cid2\u003e,\u003calpha value\u003e\r\n\r\n/////////////////////////////////////////////////////////////////////////////\r\nAdditional Tool:\r\n\r\nIn addition, we provide:\r\n- generate_nets: It allows to build Kronecker and Forest-Fire networks. Please, run \r\nwithout any argument to find out how to use it.\r\n- find_sets: It computes self-dominant sets given a network. Run without arguments to find out how to use it.\r\n- dominator_trees: It computes dominator trees given source set and sink node. Run without arguments to find out how to use it.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnetworks-learning%2Finflumax","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnetworks-learning%2Finflumax","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnetworks-learning%2Finflumax/lists"}