v1.0.0: Added scipy requirement, tested on previous Python versions down to 3.10, added the possibility to customize classes and pass them inside the InfluenceMaximization object

Author: lorenzobloise

.idea/misc.xml

@@ -32,6 +32,7 @@ NetMax was developed with Python 3.12 and requires the installation of the follo
 
 - **networkx** (version 3.3)
 - **numpy**
+- **scipy**
 - **tqdm**
 - **heapdict**
 
@@ -55,9 +56,9 @@ This framework wants to be a useful tool for all those people who study the prob
 - `r`: number of simulations to execute (default is 100)
 - `verbose`: if `True` sets the logging level to `INFO`, otherwise displays only the minimal information
 
-**Important**: `alg`, `diff_model`, `inf_prob` and `endorsement_policy` are `str` parameters, in order to prevent the user from directly importing and instantiating all the specific classes, which could have not been user-friendly.
-If the user, after reading the documentation, wants to customize some specific parameters, he can still change the corresponding attribute after the instantiation of the `InfluenceMaximization` object.
-To view all the keywords for these parameters, see the corresponding section.
+**Important**: `alg`, `diff_model`, `inf_prob` and `endorsement_policy` can be either `str` or class parameters:
+- If they are `str` parameters, they represent the `name` attribute of the corresponding class already present in the framework. This was done in order to prevent the user from directly importing and instantiating all the specific classes, which could have not been user-friendly. To view all the keywords for these parameters, see the corresponding section
+- Otherwise, they must extend the corresponding superclass depending on the parameters (`Algorithm` for `alg`, `DiffusionModel` for `diff_model`, `InfluenceProbability` for `inf_prob`, `EndorsementPolicy` for `endorsement_policy`). This way, the user can define his own custom classes
 
 After creating the `InfluenceMaximization` object, the user may call its `run()` method, which returns:
 

.idea/netmax.iml

@@ -0,0 +1,26 @@
+import copy
+
+class Agent(object):
+
+    def __init__(self, name: str, budget: int, id: int = -1):
+        """
+        This class models an agent.
+        :param name: The name of the agent.
+        :param budget: The budget of the agent.
+        :param id: The id of the agent.
+        """
+        self.name: str = name
+        self.budget: int = budget
+        self.seed: [int] = []
+        self.spread = 0
+        self.id: int = id
+
+    def __deepcopy__(self, memodict={}):
+        """
+        Makes a deep copy of the agent object.
+        """
+        new_agent = Agent(self.name, self.budget)
+        new_agent.seed = copy.deepcopy(self.seed)
+        new_agent.spread = self.spread
+        new_agent.id = self.id
+        return new_agent

README.md

@@ -0,0 +1,10 @@
+from .proxy_based.degdis import DegDis
+from .proxy_based.highest_out_degree import HighestOutDegree
+from .proxy_based.group_pr import Group_PR
+from .simulation_based.mcgreedy import MCGreedy
+from .simulation_based.celf import CELF
+from .simulation_based.celfpp import CELF_PP
+from .sketch_based.static_greedy import StaticGreedy
+from .sketch_based.ris import RIS
+from .sketch_based.tim import TIM
+from .sketch_based.timp import TIMp

__pycache__/utils.cpython-310.pyc

@@ -0,0 +1,42 @@
+import networkx as nx
+from netmax.agent import Agent
+
+class Algorithm:
+
+    def __init__(self, graph: nx.DiGraph, agents: list[Agent], curr_agent_id: int, budget, diff_model, r):
+        """
+        :param graph: networkx DiGraph
+        :param agents: list of Agent
+        :param curr_agent_id: int - index of the current agent
+        :param budget: int - budget of the current agent
+        :param diff_model: str - diffusion model
+        :param r: float - discount factor
+        """
+        self.graph = graph
+        self.agents = agents
+        self.curr_agent_id = curr_agent_id
+        self.budget = budget
+        self.diff_model = diff_model
+        self.r = r
+
+    def set_curr_agent(self, curr_agent_id):
+        """
+        Sets the current agent as the one passed.
+        :param curr_agent_id: index of the current agent.
+        """
+        self.curr_agent_id = curr_agent_id
+
+    def __in_some_seed_set__(self, v, agents):
+        """
+        Checks if a node is in some seed set.
+        :param v: the node to check.
+        :param agents: the 'agents' dictionary, which contain all the seed sets.
+        :return: True if the node is in some seed set, False otherwise.
+        """
+        for a in agents:
+            if v in a.seed:
+                return True
+        return False
+
+    def run(self):
+        raise NotImplementedError("This method must be implemented by subclasses")