Fix bugs in SEIRct, SEISct, SWIR, HK, WHK, Profile, and ProfileThreshold models

[Copilot]

Seven diffusion models contained logic bugs affecting probabilistic correctness, state tracking, and update semantics. Fixes span time-tracking resets, misused random samples, an indentation bug that silently dropped all-but-one update, inverted probability conditions, and asynchronous-vs-synchronous opinion update inconsistencies.

Changes

SEIR_ct_Model.py & SEIS_ct_Model.py

• self.progress_I was reinitialised to 0 on every iteration, clobbering the recorded infection timestamp for all currently-infected nodes. Now initialised only at actual_iteration == 0.
• In SEISctModel, newly exposed nodes recorded self.progress[u] = 0 regardless of when exposure occurred. Changed to self.progress[u] = self.actual_iteration.

SWIRModel.py

• A single eventp drawn once per node was reused across all its neighbours' independent probability checks. Moved eventp = np.random.random_sample() to just before each individual condition.

HKModel.py

• Critical: actual_status[n1] = new_op was outside the for loop — only the last randomly-selected node's opinion was ever written. Fixed indentation.
• Neighbour opinion reads used actual_status (in-flight values), producing unintended within-step cascades. Changed to self.status for synchronous (Jacobi-style) updates.
• Return value changed from delta (only changed nodes) to self.status.copy() (full opinion vector), consistent with continuous-state model conventions.

WHKModel.py

• Same actual_status → self.status fix for synchronous neighbour reads; the indentation and return-value issues were already correct here.

ProfileModel.py & ProfileThresholdModel.py

• Blocked nodes (status −1) were summed into infected, producing negative counts and corrupting the infected > 0 guard. Now excluded: infected += status[v] if status[v] != -1 else 0.
• Adoption/blocking probability conditions were inverted relative to the documented semantics:

# Before: adopts with probability (1 - profile)
if eventp >= self.params["nodes"]["profile"][u]:
    actual_status[u] = 1

# After: adopts with probability profile
if eventp < self.params["nodes"]["profile"][u]:
    actual_status[u] = 1

Same inversion fixed for the blocked parameter.

Original prompt

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This section details on the original issue you should resolve

<issue_title>Bugs in SEIRct, SEISct, SWIR, HK, WHK, Profile and ProfileThreshold models</issue_title>
<issue_description>Describe the bug

Bugs in SEIRct, SEISct, SWIR, HK, WHK, Profile and ProfileThreshold models

To Reproduce

Steps to reproduce the behavior:

• NDlib version: 5.1.1
• Operating System: Any
• Python version: Any
• Version(s) of NDlib required libraries: Any

Expected behavior

1. In the file ndlib/models/epidemics/SEIR_ct_Model.py,
   the function iteration update the self.progress_I of all Inf nodes as 0:

   # start from line 45
   def iteration(self, node_status=True):
       self.clean_initial_status(self.available_statuses.values())
   
       actual_status = {
           node: nstatus for node, nstatus in future.utils.iteritems(self.status)
       }
       self.progress_I = {
           node: 0 for node in actual_status if actual_status[node] == 1
       }
   
       if self.actual_iteration == 0:
           self.actual_iteration += 1
           delta, node_count, status_delta = self.status_delta(actual_status)
           # ...

   I think self.progress_I only can be initiated when self.actual_iteration == 0

   def iteration(self, node_status=True):
       self.clean_initial_status(self.available_statuses.values())
   
       actual_status = {
           node: nstatus for node, nstatus in future.utils.iteritems(self.status)
       }
       
       if self.actual_iteration == 0:
           self.progress_I = {
               node: 0 for node in actual_status if actual_status[node] == 1
           }
           self.actual_iteration += 1
           delta, node_count, status_delta = self.status_delta(actual_status)
           # ...

2. The first problem of ndlib/models/epidemics/SEIS_ct_Model.py is the same as above.
   The second problem is that self.progress of new Exposed nodes always be 0.

   def iteration(self, node_status=True):
       # ...
               # start form line 99
               if self.params["model"]["tp_rate"] == 1:
                   if eventp < 1 - (1 - self.params["model"]["beta"]) ** len(
                       infected_neighbors
                   ):
                       actual_status[u] = 2  # Exposed
                       self.progress[u] = 0
               else:
                   if eventp < self.params["model"]["beta"] * triggered:
                       actual_status[u] = 2  # Exposed
                       self.progress[u] = 0

   I think self.progress of new Exposed nodes should be updated by self.actual_iteration:

   def iteration(self, node_status=True):
       # ...
               # start form line 99
               if self.params["model"]["tp_rate"] == 1:
                   if eventp < 1 - (1 - self.params["model"]["beta"]) ** len(
                       infected_neighbors
                   ):
                       actual_status[u] = 2  # Exposed
                       self.progress[u] = self.actual_iteration
               else:
                   if eventp < self.params["model"]["beta"] * triggered:
                       actual_status[u] = 2  # Exposed
                       self.progress[u] = self.actual_iteration

3. In the file ndlib/models/epidemics/SWIRModel.py, the random value eventp is not updated in time

   def iteration(self, node_status=True):
       # ...
       # start from line 65 
       for u in self.graph.nodes:
   
           u_status = self.status[u]
           eventp = np.random.random_sample()
           neighbors = self.graph.neighbors(u)
           if self.graph.directed:
               neighbors = self.graph.predecessors(u)
   
           if u_status == 1:  # Infected
               for neighbor in neighbors:
                   if self.status[neighbor] == 0:  # Susceptible
                       if eventp < self.params["model"]["kappa"]:
                           actual_status[neighbor] = 1  # Infected
                       else:
                           eventp = np.random.random_sample()
                           if eventp < self.params["model"]["mu"]:
                               actual_status[neighbor] = 2  # Weakened
                   elif self.status[neighbor] == 2:  # Weakened
                       if eventp < self.params["model"]["nu"]:
                           actual_status[neighbor] = 1  # Infected
               actual_status[u] = 3  # Removed

   eventp should be updated for each judgment

   def iteration(self, node_status=True):
       # ...
       for u in self.graph.nodes:
   
           u_status = self.status[u]
           neighbors = self.graph.neighbors(u)
           if self.graph.directed:
               neighbors = self.graph.predecessors(u)
   
          ...

• Fixes Bugs in SEIRct, SEISct, SWIR, HK, WHK, Profile and ProfileThreshold models #263

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