def iteration(self, node_status=True):
        """
        Execute a single model iteration

        :return: Iteration_id, Incremental node status (dictionary node->status)
        """
        self.clean_initial_status(self.available_statuses.values())

        actual_status = {node: nstatus for node, nstatus in future.utils.iteritems(self.status)}

        # streaming
        if self.stream_execution:
            u, v = list(self.graph.edges())[0]
            u_status = self.status[u]
            v_status = self.status[v]

            if u_status == 1 and v_status == 0:
                p = np.random.random_sample()
                if p < self.params['model']['beta']:
                    actual_status[v] = 1

            if v_status == 1 and u_status == 0:
                p = np.random.random_sample()
                if p < self.params['model']['beta']:
                    actual_status[u] = 1

            delta, node_count, status_delta = self.status_delta(actual_status)
            self.status = actual_status
            self.actual_iteration += 1

            if node_status:
                return {"iteration": self.actual_iteration - 1, "status": delta.copy(),
                        "node_count": node_count.copy(), "status_delta": status_delta.copy()}
            else:
                return {"iteration": self.actual_iteration - 1, "status": {},
                        "node_count": node_count.copy(), "status_delta": status_delta.copy()}
        # snapshot
        else:

            if self.actual_iteration == 0:
                self.actual_iteration += 1
                delta, node_count, status_delta = self.status_delta(actual_status)
                if node_status:
                    return {"iteration": 0, "status": actual_status.copy(),
                            "node_count": node_count.copy(), "status_delta": status_delta.copy()}
                else:
                    return {"iteration": 0, "status": {},
                            "node_count": node_count.copy(), "status_delta": status_delta.copy()}

            for u in self.graph.nodes():
                u_status = self.status[u]
                eventp = np.random.random_sample()
                neighbors = self.graph.neighbors(u)
                if isinstance(self.graph, nx.DiGraph):
                    neighbors = self.graph.predecessors(u)

                if u_status == 0:
                    infected_neighbors = len([v for v in neighbors if self.status[v] == 1])
                    if eventp < self.params['model']['beta'] * infected_neighbors:
                        actual_status[u] = 1

        delta, node_count, status_delta = self.status_delta(actual_status)
        self.status = actual_status
        self.actual_iteration += 1

        if node_status:
            return {"iteration": self.actual_iteration - 1, "status": delta.copy(),
                    "node_count": node_count.copy(), "status_delta": status_delta.copy()}
        else:
            return {"iteration": self.actual_iteration - 1, "status": {},
                    "node_count": node_count.copy(), "status_delta": status_delta.copy()}
Execute a single model iteration

        :return: Iteration_id, Incremental node status (dictionary node->status)