Merge remote-tracking branch 'origin/composite' into composite

GiulioRossetti · Jan 25, 2018 · c8823b1 · c8823b1
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diff --git a/docs/reference/models/dynamics/dSI.rst b/docs/reference/models/dynamics/dSI.rst
@@ -10,7 +10,7 @@ The model is instantiated on a graph having a non-empty set of infected nodes.
 
 SI assumes that if, during a generic iteration, a susceptible node comes into contact with an infected one, it becomes infected with probability β: once a node becomes infected, it stays infected (the only transition allowed is S→I).
 
-The dSI implementation assumes that the process occurs on a directed/undirected dynamic network.
+The dSI implementation assumes that the process occurs on a directed/undirected dynamic network; this model was introduced by Milli et al. in 2018 [#]_.
 
 --------
 Statuses
@@ -106,5 +106,5 @@ In the code below is shown an example of instantiation and execution of an DynSI
     # Simulation interaction graph based execution
     iterations = model.execute_iterations()
 
-
-.. [#] W. O. Kermack and A. McKendrick, “A Contribution to the Mathematical Theory of Epidemics,” Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, vol. 115, no. 772, pp. 700–721, Aug. 1927.
+.. [#] W. O. Kermack and A. McKendrick, “A Contribution to the Mathematical Theory of Epidemics,” Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, vol. 115, no. 772, pp. 700–721, Aug. 1927.
+.. [#] Letizia Milli, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi. “Diffusive Phenomena in Dynamic Networks: a data-driven study”. Accepted to International Conference on Complex Networks (CompleNet), 2018, Boston.
diff --git a/docs/reference/models/dynamics/dSIR.rst b/docs/reference/models/dynamics/dSIR.rst
@@ -10,7 +10,7 @@ The model is instantiated on a graph having a non-empty set of infected nodes.
 
 SIR assumes that if, during a generic iteration, a susceptible node comes into contact with an infected one, it becomes infected with probability beta, than it can be switch to removed with probability gamma (the only transition allowed are S→I→R).
 
-The dSIR implementation assumes that the process occurs on a directed/undirected dynamic network.
+The dSIR implementation assumes that the process occurs on a directed/undirected dynamic network; this model was introduced by Milli et al. in 2018 [#]_.
 
 --------
 Statuses
@@ -110,4 +110,5 @@ In the code below is shown an example of instantiation and execution of an DynSI
     iterations = model.execute_iterations()
 
 
-.. [#] W. O. Kermack and A. McKendrick, “A Contribution to the Mathematical Theory of Epidemics,” Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, vol. 115, no. 772, pp. 700–721, Aug. 1927
+.. [#] W. O. Kermack and A. McKendrick, “A Contribution to the Mathematical Theory of Epidemics,” Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, vol. 115, no. 772, pp. 700–721, Aug. 1927
+.. [#] Letizia Milli, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi. “Diffusive Phenomena in Dynamic Networks: a data-driven study”. Accepted to International Conference on Complex Networks (CompleNet), 2018, Boston.
diff --git a/docs/reference/models/epidemics/GeneralisedThreshold.rst b/docs/reference/models/epidemics/GeneralisedThreshold.rst
@@ -2,10 +2,21 @@
 Generalised Threshold
 *********************
 
+The Generalised Threshold model was introduced in 2017 by Török and Kertesz [#]_.
+
+In this model, during an epidemics, a node is allowed to change its status from **Susceptible** to **Infected**.
+
+The model is instantiated on a graph having a non-empty set of infected nodes.
+
+The model is defined as follows:
+
+1. At time *t* nodes become Infected with rate *mu* *t*/*tau*
+2. Nodes for which the ratio of the active friends dropped below the threshold are moved to the Infected queue
+3. Nodes in the Infected queue become infected with rate *tau*. If this happens check all its friends for threshold
+
 --------
 Statuses
 --------
-
 During the simulation a node can experience the following statuses:
 
 ===========  ====
@@ -19,15 +30,13 @@ Infected     1
 Parameters
 ----------
 
-=========  =====  ===============  =======  =========  ========================
+=========  =====  ===============  =======  =========  =======================
 Name       Type   Value Type       Default  Mandatory  Description
-=========  =====  ===============  =======  =========  ========================
-threshold  Node   float in [0, 1]  0.1      False      Individual threshold
-tau        Model  int                       True       Adoption threshold rate
-mu         Model  int                       True       Exogenous timescale
-=========  =====  ===============  =======  =========  ========================
-
-
+=========  =====  ===============  =======  =========  =======================
+threshold  Node   float in [0, 1]   0.1      False     Individual threshold
+tau		   Model  int                        True      Adoption threshold rate
+mu         Model  int                        True      Exogenous timescale
+=========  =====  ===============  =======  =========  =======================
 
 The initial infection status can be defined via:
 
@@ -100,4 +109,5 @@ In the code below is shown an example of instantiation and execution of a Thresh
     # Simulation execution
     iterations = model.iteration_bunch(200)
 
+.. [#] János Török and János Kertész “Cascading collapse of online social networks” Scientific reports, vol. 7 no. 1, 2017 
 
diff --git a/docs/reference/models/epidemics/Profile.rst b/docs/reference/models/epidemics/Profile.rst
@@ -1,6 +1,8 @@
 *******
 Profile
 *******
+The Profile model was introduced in 2017 by Milli et al. [#]_.
+
 
 The Profile model assumes that the diffusion process is only apparent; each node decides to adopt or not a given behavior – once known its existence – only on the basis of its own interests. 
 
@@ -110,3 +112,5 @@ In the code below is shown an example of instantiation and execution of a Profil
 
     # Simulation execution
     iterations = model.iteration_bunch(200)
+    
+.. [#] Letizia Milli, Giulio Rossetti, Dino Pedreschi, Fosca Giannotti, “Information Diffusion in Complex Networks: The Active/Passive Conundrum,” Proceedings of International Conference on Complex Networks and their Applications, (pp. 305-313). Springer, Cham. 2017
diff --git a/docs/reference/models/epidemics/ProfileThreshold.rst b/docs/reference/models/epidemics/ProfileThreshold.rst
@@ -1,6 +1,7 @@
 *****************
 Profile Threshold
 *****************
+The Profile-Threshold model was introduced in 2017 by Milli et al. [#]_.
 
 The Profile-Threshold model assumes the existence of node profiles that act as preferential schemas for individual tastes but relax the constraints imposed by the Profile model by letting nodes influenceable via peer pressure mechanisms. 
 
@@ -109,3 +110,5 @@ In the code below is shown an example of instantiation and execution of a Profil
 
     # Simulation execution
     iterations = model.iteration_bunch(200)
+    
+.. [#] Letizia Milli, Giulio Rossetti, Dino Pedreschi, Fosca Giannotti, “Information Diffusion in Complex Networks: The Active/Passive Conundrum,” Proceedings of International Conference on Complex Networks and their Applications, (pp. 305-313). Springer, Cham. 2017
diff --git a/docs/reference/models/epidemics/SWIR.rst b/docs/reference/models/epidemics/SWIR.rst
@@ -1,7 +1,13 @@
 ****
 SWIR
 ****
+The SWIR model was introduced in 2017 by Lee et al. [#]_.
+
+In this model, during the epidemics, a node is allowed to change its status from **Susceptible** (S) to **Weakened** (W) or **Infected** (I), then to **Removed** (R).
 
+The model is instantiated on a graph having a non-empty set of infected nodes.
+
+At time *t* a node in the state I is selected randomly and the states of all neighbors are checked one by one. If the state of a neighbor is S then this state changes either i) to I with probability *kappa* or ii) to W with probability *mu*. If the state of a neighbor is W then the state W changes to I with probability *nu*. We repeat the above process for all nodes in state I and then changes to R for each associated node. 
 
 --------
 Statuses
@@ -62,7 +68,7 @@ Describe
 ^^^^^^^^^^^^^^^^^^
 Execute Simulation
 ^^^^^^^^^^^^^^^^^^
-.. automethod:: ndlib.models.epidemics.SWIRModel.SWIRModel.iteration(self)
+.. automethod:: ndlib.models.epidemics.SWIRModel.SEIRModel.iteration(self)
 .. automethod:: ndlib.models.epidemics.SWIRModel.SWIRModel.iteration_bunch(self, bunch_size)
 
 
@@ -96,3 +102,4 @@ In the code below is shown an example of instantiation and execution of an SEIR
     iterations = model.iteration_bunch(200)
 
 
+.. [#] D. Lee, W. Choi, J. Kertész, B. Kahng. “Universal mechanism for hybrid percolation transitions”. Scientific Reports, vol. 7(1), 5723, 2017.