{"id":20723634,"url":"https://github.com/networks-learning/coevolution","last_synced_at":"2025-08-12T01:03:34.815Z","repository":{"id":150974483,"uuid":"48971495","full_name":"Networks-Learning/Coevolution","owner":"Networks-Learning","description":"Code for \"COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution\", NIPS 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Coevolution\nThis repository contains the codes for the paper **\"Coevolve: A joint point process model for information diffusion and network co-evolution.\"** \nMehrdad Farajtabar, Yichen Wang, Manuel Gomez-Rodriguez, Shuang Li, Hongyuan Zha, and Le Song.\nIn Advances in Neural Information Processing Systems, pp. 1945-1953. 2015.\n\n## COMPILE\nTo compile run the follwoings: \u003cbr\u003e\n\u003e g++ -c lib/rng.cpp \u003cbr\u003e\n\u003e g++ -o coevolution main.cpp rng.o\n\nThen the excutable file \"coeovlution\" is ready for use.\n\n## RUN\nTo run with complete  use the following command:\n\u003e ./coevolution -N 100 -T 100 -sp 0.004 -finSp 0 -ofn trace.txt -cfn cas.txt -mfn model.txt -wl 0 -mu 0.0001 -alpha 0.5 -eta 0.5 -beta 0.5 -rnd 0 -w_phi 1 -w_kap 1 2\u003e log.txt\n\n\n\n## INPUT\nThe parameters are: \u003cbr\u003e\n- N:\tNumber of nodes\n- T:\tTime limit of the simulation \n- sp: Sparsity of limit of the simulation\n- finSp: Finishing with sparsity limit (finsSp=1) or with time limit (finsSp=0)\n- ofn: Name of output file containing the trace of activities\n- cfn: Name of cascade file containing the statstics of casaces\n- mfn: Name of model file containing the parameters of model and simulation\n- wl: If wl=1 then log file is created.\n- mu: Model parameter for mean of baseline (exogenous) rate for link ceration (c.f. paper)\n- alpha: Model parameter for mean of excitory coefficient (indogenous) for link creation (c.f. paper)\n- eta: Model parameter for mean of baseline (exogenous) rate for retweet (c.f. paper)\n- beta: Model parameter for mean of excitory coefficient (indogenous) for retweet (c.f. paper)\n- rnd: If this is set to 1 then the model parameters are set unformly at random with mean specified as above otherwise they are exactly equal to the value specified\n- w_phi: The decaying kernel coefficient for link creation\n- w_kap: the decaying kernel coefficient for retweet\n\n## OUTPUT\nDepending on the input specificaiton you will get up to 4 output files.\n- Ouput File (specified by ofn): It contains detailed traces of (link and retweet) events ordered by time of happening. There will be 4 or 5 numbers in each line specified by the following heading:\n\ttype\ttime\tsrc\t\tdst\t\tparent\n\t* type: 0 denotes a retweet event and 1 denotes a link event.\n\t* time: Time of event\n\t* src: The source node to be retweeted or linked to\n\t* dst: The node who establishes the link or retweets\n\t* parent: Exists only for retweet events. It is -1 for the retweets that orginated exgonouesly (actually a tweet) and is set to the number of the event which this tweet is a reshare(retweet) of that one. \u003cbr\u003e\n- Cascade File (specified by cfn): It contains the statistics of the cascades. More especially, it contains 3 records of data:\n \t* Cascade Type: The i-th number in this row contains the number of cascades of type i (Refer to the paper for a specificaton of cascade types)\n\t* Caccade Depth: The i-th number in this row contains the number of cascades with depth i\n\t* Cascade Size: The i-th number in this rwo contains the number of cascades of size i (number of nodes in the cascade)\n- Model File (specified by mfn): Contains the parameters of model and simulaiton,\n\t\tT\tN\tsp  w_phi\tw_kap\t\n\tas specified above.\n\tAlso, then in  N lines it has mu,alpha,eta,beta per node.\n- Log File (written when wl=1 and is log.txt): contains a log file of what happens. It will be helpful for develpment.\n\t\n## QUESTIONS\nFor any question please contact Mehrdad Farajtabar (mehrdad@gatech.edu)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnetworks-learning%2Fcoevolution","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnetworks-learning%2Fcoevolution","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnetworks-learning%2Fcoevolution/lists"}