Due Date: 18.02.2015 23:59 <br > Late submission policy: -0.2 points per day <br >
Please send your reports to mailto:leonid.e.zhukov@gmail.com and mailto:shestakoffandrey@gmail.com with message subject of the following structure:<br > [HSE Networks 2015] {LastName} {First Name} HA*{Number}*
Support your computations with figures and comments. <br > If you are using IPython Notebook you may use this file as a starting point of your report.<br > <br >
Consider Barabasi and Albert dynamical grow model. Two main ingredients of this model are network growing and prefferential attachment. Implement two restricted B&A-based models: <br >
Model A <br > Lack of prefferential attachment, that is at each time-step form edges uniformly at random while network keeps growing.
Model B <br > Lack of growing, that is fix total number of nodes, on each time-step randomly choose one and form edges with prefferential attachment. <br >
Analyse results with respect to various parameter settings
Consider the following "Vertex copying model" of growing network.
At every time step a random vertex from already existing vertices is selected and duplicated together with all edges, such that every edge of the vertex
Starting state is defined by some small number of randomly connected vertices.
The model can generate both directed and undirected networks.
Analyse results with respect to various parameter settings