{"id":20723605,"url":"https://github.com/networks-learning/infopath","last_synced_at":"2025-12-12T08:01:33.696Z","repository":{"id":6093972,"uuid":"7320983","full_name":"Networks-Learning/infopath","owner":"Networks-Learning","description":"Infopath C++/SNAP implementation","archived":false,"fork":false,"pushed_at":"2012-12-25T22:51:15.000Z","size":756,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-17T23:18:14.639Z","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":"2012-12-25T22:26:32.000Z","updated_at":"2022-11-08T01:42:55.000Z","dependencies_parsed_at":"2022-08-27T11:31:31.977Z","dependency_job_id":null,"html_url":"https://github.com/Networks-Learning/infopath","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%2Finfopath","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Networks-Learning%2Finfopath/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Networks-Learning%2Finfopath/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Networks-Learning%2Finfopath/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Networks-Learning","download_url":"https://codeload.github.com/Networks-Learning/infopath/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242997997,"owners_count":20219273,"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":"2024-11-17T04:09:13.031Z","updated_at":"2025-12-12T08:01:28.656Z","avatar_url":"https://github.com/Networks-Learning.png","language":"C++","readme":"========================================================================\r\n    Structure and Dynamics of Information Pathways in On-line Media    \r\n========================================================================\r\n\r\nDiffusion of information, spread of rumors and infectious diseases are all\r\ninstances of stochastic processes that occur over the edges of an underlying\r\nnetwork. Many times networks over which contagions spread are unobserved and \r\nneed to be inferred from the diffusion data. Moreover, such networks are often \r\ndynamic and change over time.\r\n\r\nWe have developed an on-line algorithm that relies on stochastic gradient\r\ndescent to efficiently infer dynamic networks based on information diffusion \r\ndata. We assume there is an unobserved dynamic network that changes over time,\r\nwhile we observe the results of a dynamic process spreading over the edges\r\nof the network. The task then is to infer the edges and the dynamics of the\r\nunderlying network.\r\n\r\nFor more information about the procedure see:\r\n  Structure and Dynamics of Information Pathways in On-line Media\r\n  Manuel Gomez-Rodriguez, Jure Leskovec and Bernhard Schölkopf\r\n  http://www.stanford.edu/~manuelgr/dynamic/\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\nThe code should also work in Windows but you will need to edit the Makefile.\r\n'make opt' and 'make opt_linux' compile the optimized (fast) version of the code.\r\n\r\nUsage:\r\n\r\nInfer the network given a text file with cascades (nodes and timestamps):\r\n\r\n./infer -i:cascades.txt\r\n\r\nAll arguments are shown any time ./infer is run.\r\n\r\n/////////////////////////////////////////////////////////////////////////////\r\nFormat input cascades:\r\n\r\nThe cascades 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 cascade. The format of every line is \u003ccascade id\u003e;\u003cid\u003e,\u003ctimestamp\u003e,\u003cid\u003e,\u003ctimestamp\u003e,\u003cid\u003e,\u003ctimestamp\u003e...\r\n\r\n/////////////////////////////////////////////////////////////////////////////\r\nAdditional Tool:\r\n\r\nIn addition, generate_nets is also provided. It allows to build time-varying Kronecker and Forest-Fire networks and generate cascades\r\nwith exponential, powerlaw and rayleigh transmission models. Please, run without any argument to see how to use them.","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnetworks-learning%2Finfopath","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnetworks-learning%2Finfopath","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnetworks-learning%2Finfopath/lists"}