1,485
Views
4
CrossRef citations to date
0
Altmetric
Research Article

An Improved Grey Wolves Optimization Algorithm for Dynamic Community Detection and Data Clustering

, &
Article: 2012000 | Received 29 Sep 2020, Accepted 23 Nov 2021, Published online: 31 Dec 2021

Figures & data

Figure 1. A sample of a network with five community structure.

Figure 1. A sample of a network with five community structure.

Figure 2. Pseudo-code of IGWO-LP algorithm.

Figure 2. Pseudo-code of IGWO-LP algorithm.

Figure 3. Pseudo-code of improved label propagation algorithm.

Figure 3. Pseudo-code of improved label propagation algorithm.

Figure 4. Flowchart of the IGWO-LP algorithm.

Figure 4. Flowchart of the IGWO-LP algorithm.

Table 1. The parameters of the algorithm

Figure 5. NMI results on SYN-FIX dataset (z = 3).

Figure 5. NMI results on SYN-FIX dataset (z = 3).

Figure 6. NMI results on SYN-FIX dataset (z = 5).

Figure 6. NMI results on SYN-FIX dataset (z = 5).

Figure 7. NMI results on SYN-VAR dataset (z = 3).

Figure 7. NMI results on SYN-VAR dataset (z = 3).

Figure 8. NMI results on SYN-VAR dataset (z = 5).

Figure 8. NMI results on SYN-VAR dataset (z = 5).

Figure 9. The NMI result of cell phone calls dataset.

Figure 9. The NMI result of cell phone calls dataset.

Figure 10. The NMI result of enron mail dataset.

Figure 10. The NMI result of enron mail dataset.

Figure 11. The NMI result of football dataset.

Figure 11. The NMI result of football dataset.

Table 2. The NMI results for all datasets

Figure 12. Comparing the results of algorithms using NMI criterion.

Figure 12. Comparing the results of algorithms using NMI criterion.

Table 3. Results of benchmark functions (F1 to F23)

Table 4. Properties of benchmark datasets

Table 5. Intra-cluster distances of each algorithm on UCI datasets

Table 6. Average error rate of the algorithms in percentage