Table 5.

SSE Comparisons for Weighted Networks

		SSE Results				Modularity
Network	K	Ward	K-means	Difference	% Difference	Ward	K-means
Obama	2	0.00063952	0.00063952	0.00000000	0.00%	0.0887	0.0887
	3	0.00025376	0.00025376	0.00000000	0.00%	0.1475	0.1475
	4	0.00005760	0.00005760	0.00000000	0.00%	0.1342	0.1342
	5	0.00003550	0.00003550	0.00000000	0.00%	0.0768	0.0768
Han	2	0.01897008	0.01897008	0.00000000	0.00%	0.2738	0.2738
	3	0.00788391	0.00788391	0.00000000	0.00%	0.3019	0.3019
	4	0.00621154	0.00598555	0.00022599	3.78%	0.2457	0.2667
	5	0.00471093	0.00448496	0.00022597	5.04%	0.2022	0.2231
Empathy	2	0.12560777	0.12560777	0.00000000	0.00%	0.2390	0.2390
	3	0.07100972	0.07100972	0.00000000	0.00%	0.3833	0.3833
	4	0.04466778	0.03951395	0.00515383	13.04%	0.4163	0.4005
	5	0.03497292	0.02981909	0.00515383	17.28%	0.4092	0.3933
	6	0.02585761	0.02271613	0.00314148	13.83%	0.3762	0.3558
Emotion	2	0.08683991	0.08229943	0.00454048	5.52%	0.0866	0.1873
	3	0.05786092	0.05521453	0.00264639	4.79%	0.2216	0.2616
	4	0.04044612	0.03838701	0.00205911	5.36%	0.2735	0.2907
	5	0.03339145	0.03147526	0.00191619	6.09%	0.2855	0.2985
	6	0.02689232	0.02622428	0.00066804	2.55%	0.3035	0.2934
	7	0.02304063	0.02152170	0.00151893	7.06%	0.3240	0.3105
	8	0.01996470	0.01844577	0.00151893	8.23%	0.3022	0.2887

Note. The SSE results obtained using the walktrap algorithm with both Ward’s method and a multistart K-means clustering are reported, as is the difference in the SSE values (Ward minus K-means) and the percentage difference. Modularity scores associated with the Ward’s method and K-means clustering are also reported. For the Obama, Han, and Empathy networks, branch-and-bound was used to obtain globally-optimal K-means partitions. For the Emotion network, the multistart K-means heuristic was used.