je-suis-tm / graph-theory

Julia and Python complex system applications in ecology, epidemiology, sociology, economics & finance; network science models including Bianconi-Barabási, Barabási-Albert, Watts-Strogatz, Waxman Model & Erdős-Rényi; graph theory algorithms involving Gillespie, Bron Kerbosch, Ramsey, Bellman Ford, A*, Kruskal, Borůvka, Prim, Dijkstra, DSatur, Randomized Distributed, Vizing, Topological Sort, DFS, BFS
https://je-suis-tm.github.io/graph-theory
Apache License 2.0
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project #30

Closed tejareddy97 closed 2 years ago

tejareddy97 commented 2 years ago

hi using sir model can u able to find the impact of superspreaders how the infection is spreading on random networks u can mail me reagarding that on gsreddy2297@gmail.com

je-suis-tm commented 2 years ago

Hi, in terms of impact, could you be a bit more specific? Do you refer to temporal impact on sir model? Also superspreaders change the topological structure of random networks. A power-law distribution could be considered as a network model where superspreaders (a vertex with extremely high degree) exist. The comparison between Erdos-Renyi and Barabasi-Albert is included in the project. You can get the "impact" from the comparison.

tejareddy97 commented 2 years ago

Like if there is one person as superspreader then how the disease is spreading from him to the people he is in close contact and from them how it's spreading

On Sat, 27 Aug 2022, 4:09 pm T.M., @.***> wrote:

Hi, in terms of impact, could you be a bit more specific? Do you refer to temporal impact on sir model? Also superspreaders change the topological structure of random networks. A power-law distribution could be considered as a network model where superspreaders (a vertex with extremely high degree) exist. The comparison between Erdos-Renyi and Barabasi-Albert is included in the project. You can get the "impact" from the comparison.

— Reply to this email directly, view it on GitHub https://github.com/je-suis-tm/graph-theory/issues/30#issuecomment-1229210243, or unsubscribe https://github.com/notifications/unsubscribe-auth/ATYVT7PF3M4362QKAZLWSO3V3IVS5ANCNFSM57JMBRHA . You are receiving this because you authored the thread.Message ID: @.***>

tejareddy97 commented 2 years ago

If one person in the network is a superspreader then how he is spreading to its neighbours and also we will be changing the probability of the connection so that the connections will be less

On Sat, 27 Aug 2022, 4:53 pm teja reddy, @.***> wrote:

Like if there is one person as superspreader then how the disease is spreading from him to the people he is in close contact and from them how it's spreading

On Sat, 27 Aug 2022, 4:09 pm T.M., @.***> wrote:

Hi, in terms of impact, could you be a bit more specific? Do you refer to temporal impact on sir model? Also superspreaders change the topological structure of random networks. A power-law distribution could be considered as a network model where superspreaders (a vertex with extremely high degree) exist. The comparison between Erdos-Renyi and Barabasi-Albert is included in the project. You can get the "impact" from the comparison.

— Reply to this email directly, view it on GitHub https://github.com/je-suis-tm/graph-theory/issues/30#issuecomment-1229210243, or unsubscribe https://github.com/notifications/unsubscribe-auth/ATYVT7PF3M4362QKAZLWSO3V3IVS5ANCNFSM57JMBRHA . You are receiving this because you authored the thread.Message ID: @.***>

je-suis-tm commented 2 years ago

I am not sure if you are familiar with the concept of power law distribution. Basically a superspreader is a vertex with high degree and it can be modelled in Barabási–Albert model. A superspreader is at the right tail of degree distribution. To evaluate the individual vertex, I am afraid you have to use degree-based approximation or pair approximation and it will be very computationally expensive. In terms of changing the probability of the connections, it's only a matter of tweaking the hyperparameters. In our BA model, you can modify the upper limit of maximum degree and the lower limit of minimum degree.