## 5 Experiments and Discussions
### Experimental Results and Analysis
#### 5.4.4 Parameter Sensitivity Analysis  
In this section, we focus on the effects of the parameters \(\lambda\) and \(\Delta\) on the LNSSCL algorithm. \(\lambda\) represents the probability of a particle performing preferential walk, regulating the balance between random and preferential walking. When the parameter \(\lambda\) takes a relatively low value, the particles are more inclined to wander randomly, visit new nodes, and expand the community range, but are prone to the problem of too much randomness. When the parameter \(\lambda\) takes a relatively high value, the particle is more  
Figure 4: Experimental results of NMI under synthetic networks: (**a**) experimental results of LFR1 network where \(n\) = 1000; (**b**) experimental results of LFR2 network where \(n\) = 4000.  
inclined to preferentially wander, frequently visit the nodes that have been controlled, and consolidate the community scope, but it is easy to fall into the localized area and it cannot visit new nodes. From Figure 5, it can be seen that the parameter \(\lambda\) can achieve the best community detection performance by appropriately increasing the value of the parameter when balancing random wandering and preferential wandering (\(\lambda=0.5\)). For the Polbooks, Football, and Jazz datasets, the best performance is achieved when the value of \(\lambda\) is 0.6, while the Email dataset takes the value of \(\lambda\) as 0.5.  
The parameter \(\Delta\) is responsible for updating the particle's energy value. When \(\Delta\) is very small, the particle is not penalized enough by the energy it receives for visiting nodes that are not under its control, so the particle's energy will not be depleted during its wanderings. The particle will frequently visit nodes that should belong to the nodes to which the competing particles belong and enter the core of other communities. As a result, all nodes in the network will be in constant competition, unable to establish and consolidate community boundaries, and the final community detection will be less effective. When \(\Delta\) is very large, the particle will simply run out of energy once it visits a node controlled by a competing particle. The particle will frequently enter the resurrection phase and is not expected to move away from its initial position to take control of other nodes. As can be seen in Figure 6, a parameter \(\Delta\) of 0.3 achieves the best performance on all four datasets of the experiment. The different values of the parameter \(\Delta\) have roughly the same trend in affecting the performance on different datasets in the experiment.  
Figure 5: Parameter sensitivity analysis for \(\lambda\): (**a**) experimental results of Polbooks; (**b**) experimental results of Football; (**c**) experimental results of Jazz; (**d**) experimental results of Email.  
According to Figures 5 and 6, the algorithm is somewhat sensitive to the values of \(\lambda\) and \(\Delta\). Numerically, there is little fluctuation within a small interval of relative range. Based on the results of the parameter sensitivity analysis experiments, we set \(\lambda\) to 0.6 and \(\Delta\) to 0.3 for experiments on other datasets.