Network_attack_propagation/network_attack_propagation.py

import random
from statistics import mean

import matplotlib.pyplot as plt
import networkx as nx
from matplotlib import animation


class Node:
    def __init__(self, is_infected=False):
        self.id = random.randint(1, 2000000)
        self.is_infected = is_infected

    def as_tuple(self):
        return self.id, self.is_infected

    def __repr__(self):
        return f'id: {self.id}, infected: {self.is_infected}'


class Edge:
    def __init__(self, node_a: Node, node_b: Node, weight: float):
        self.node_a = node_a
        self.node_b = node_b
        self.weight = weight

    def as_tuple(self):
        return self.node_a, self.node_b, {'weight': self.weight}


class Graph:
    def __init__(self):
        self.edges = []

    def add_edge(self, edge: Edge):
        self.edges.append(edge)

    def add_edges(self, edges: [Edge]):
        [self.edges.append(e) for e in edges]

    def get_nodes(self) -> [Node]:
        nodes = set()
        for edge in self.edges:
            nodes.add(edge.node_a)
            nodes.add(edge.node_b)
        return nodes


def update(num, layout, g_repr, ax, our_graph: Graph):
    """
    This function is called every 'step', so if you wish to update the graph, do it here
    """
    ax.clear()

    for n in our_graph.get_nodes():
        n.is_infected = bool(random.getrandbits(1))

    colors = ['red' if n.is_infected else 'blue' for n in g_repr]
    nx.draw_networkx(g_repr, ax=ax, pos=layout, node_color=colors, with_labels=False)


def do_graph_animation(output_file_name: str, in_graph: Graph, frame_count: int):
    g_repr = nx.Graph()
    # Convert our graph class into tuples understood by networkx
    g_repr.add_edges_from([e.as_tuple() for e in in_graph.edges])

    layout = nx.spring_layout(g_repr)
    fig, ax = plt.subplots()

    anim = animation.FuncAnimation(fig, update, frames=frame_count, fargs=(layout, g_repr, ax, in_graph))
    anim.save(output_file_name)
    plt.show()


def bus_network(n=30) -> Graph:
    network = Graph()
    nodes = [Node() for _ in range(n)]
    edges = [Edge(nodes[i], nodes[i + 1], 1.0) for i in range(n - 1)]

    network.add_edges(edges)
    return network


def rank_avg(edges, digits=2):
    ranks = {}
    for e in edges:
        ranks[e.node_a] = ranks.get(e.node_a, 0) + 1
        ranks[e.node_b] = ranks.get(e.node_b, 0) + 1
    return round(mean(ranks.values()), digits)


def star_network(cluster_count=5, starsize=6) -> tuple[Graph, float]:
    node_count = cluster_count + cluster_count * starsize + 1
    nodes = [Node() for _ in range(node_count)]

    edges = []
    for x in range(cluster_count):
        center_node = x * starsize + x
        edges += [Edge(nodes[center_node], nodes[i], 1.0) for i in range(center_node + 1, center_node + starsize + 1)]
        edges.append(Edge(nodes[-1], nodes[center_node], 1.0))

    network = Graph()
    network.add_edges(edges)

    return network, rank_avg(edges)


def main():
    network = Graph()
    nodes = [Node(True), Node(), Node(), Node(True), Node()]

    network.add_edges([
        Edge(nodes[1], nodes[0], 0.02),
        Edge(nodes[1], nodes[2], 0.2),
        Edge(nodes[2], nodes[0], 0.7),
        Edge(nodes[3], nodes[2], 0.2),
        Edge(nodes[3], nodes[1], 0.2),
        Edge(nodes[4], nodes[3], 0.2)
    ])

    do_graph_animation('test.gif', network, 5)

    bus = bus_network()
    do_graph_animation('bus.gif', bus, 5)

    star, star_avg_rank = star_network()
    do_graph_animation('star.gif', star, 5)


if __name__ == "__main__":
    main()