# Directed Network Disassembly Method Based on Non-Backtracking Matrix
## 5 Experimental Results  
In order to verify the applicability of the DIR method in directed networks, it is used in artificial directed ER networks, BA networks and real networks, and the disassembly results are compared with two commonly used methods (GND algorithm and Min-Sum method). The dataset of Table 1 is selected in the experiment, and the experimental comparison is carried out in different artificial directed networks and large-scale real networks (for the convenience of comparison, the disassembly scale and disassembly cost in this paper are both proportional). Some scholars, e.g., [PERSON] [14] have proved that the GND method has a higher disassembly efficiency than other algorithms such as Min-Sum and information transfer in undirected networks when the network disassembly cost is the unit cost (number of nodes) and non-unit cost (based on node degree). When the disassembly scale is the same, the \begin{table}
\begin{tabular}{c c c c c} \hline
**Network Name** & **Number of Nodes \(n\)** & **Number of Edges \(m\)** & **Node Connection Probability \(p\)** & **Average Degree** \\ \hline ER random network & 1000 & approximately equal to 10,000 & 0.01 & 10 \\ BA random network & 1000 & approximately equal to 10,000 & 10 \\ Email-EU-core network & 1005 & 24,929 & 24.80 \\ Weki-vote network & 8297 & 103,689 & 12.50 \\ \hline \end{tabular}
\end{table} 
Table 1: Network dataset. GND method has a lower disassembly cost than other algorithms, and the GND method can destroy the network structure with a smaller disassembly cost. This method has better performance than other algorithms in the disassembly of undirected networks. The DIR method is compared with the GND method and Min-Sum method, considering that the disassembly cost is the unit cost (i.e., the number of nodes). Figures 2 and 3 are the disassembly results of different methods in different directed networks, where the corresponding curves of disassembly scale and disassembly cost are provided. The ordinate disassembly scale is the proportion of the number of nodes in the largest (strongly) connected subgraph, and the abscissa disassembly cost is the proportion of the number of nodes in the smallest disassembly set when disassembly is at the scale shown in the ordinate. As shown in Figure 2, in a dense ER random network, when the required disassembly size is less than 0.25, the disassembly cost of the DIR method is smaller than that of the GND and Min-Sum methods; in the artificial BA directed network with an average degree of 10, the disassembly cost of the DIR method is significantly lower than that of the other two methods. Additionally, in the relatively sparse real directed network (Figure 2), when the network is disassembled to the same specified scale, the DIR method has a lower cost than other methods. The reason for the difference is that methods such as GND and Min-Sum take the largest connected subgraph in the network as the disassembly subject. The DIR method takes the largest strongly connected subset of the network as the disassembly subject. When the network is dense enough, the size of the largest strongly connected subgraph and the connected subgraph in the directed network is not hugely different; however, in the relatively sparse network (such as the Weki network), the cost of applying the maximum strongly connected subgraph of the directed network for disassembly is significantly lower than that of the GND and Min-Sum methods. The experiments show that the DIR method has the advantage of lower cost in directed network disassembly, which shows the efficiency of the DIR method in directed network disassembly. Next, we explore the impact of different disassembly methods on the network structure. By applying different disassembly methods to different networks and comparing the clustering coefficient [26] and assortativity coefficient [27] of the disassembly process network, the superiority of the DIR method to retain the network structure information to a great extent is proved.