Neighborhood discovery via augmented network community structure

Bramson, Aaron

doi:10.1007/s41109-022-00481-z

Applied Network Science

Table 3 Community detection accuracy results for each parameter combination

From: Neighborhood discovery via augmented network community structure

	Weight	Subgraph extent	Variable set	AMI	Rand Index	Proportion missed	Extra communities
0			unweighted	0.864	0.662	0.124	0.273**
1	\(\omega _1\)	200	allVars	0.942**	0.885**	0.025***	0.318
2	\(\omega _1\)	200	unCorr	0.933	0.835	0.027**	0.182***
3	\(\omega _1\)	200	netOnly	0.919	0.785	0.043*	0.318
4	\(\omega _1\)	200	networkSome	0.936*	0.871*	0.059	0.318
5	\(\omega _1\)	200	roadFeatures	0.943***	0.896***	0.044	0.273**
6	\(\omega _1\)	300	allVars	0.908	0.805	0.068	0.500
7	\(\omega _1\)	300	unCorr	0.854	0.638	0.079	0.364
8	\(\omega _1\)	300	netOnly	0.862	0.658	0.075	0.409
9	\(\omega _1\)	300	networkSome	0.872	0.748	0.101	0.545
10	\(\omega _1\)	300	roadFeatures	0.927	0.839	0.071	0.318
11	\(\omega _1\)	400	allVars	0.889	0.771	0.088	0.545
12	\(\omega _1\)	400	unCorr	0.898	0.787	0.101	0.636
13	\(\omega _1\)	400	netOnly	0.902	0.802	0.086	0.500
14	\(\omega _1\)	400	networkSome	0.894	0.771	0.106	0.455
15	\(\omega _1\)	400	roadFeatures	0.896	0.761	0.071	0.364
16	\(\omega _2\)	200	allVars	0.927	0.852	0.085	0.545
17	\(\omega _2\)	200	unCorr	0.919	0.838	0.093	0.591
18	\(\omega _2\)	200	netOnly	0.920	0.848	0.098	0.636
19	\(\omega _2\)	200	networkSome	0.929	0.838	0.072	0.455
20	\(\omega _2\)	200	roadFeatures	0.924	0.840	0.078	0.364
21	\(\omega _2\)	300	allVars	0.860	0.671	0.152	0.682
22	\(\omega _2\)	300	unCorr	0.874	0.703	0.186	0.682
23	\(\omega _2\)	300	netOnly	0.865	0.718	0.172	0.682
24	\(\omega _2\)	300	networkSome	0.869	0.736	0.143	0.727
25	\(\omega _2\)	300	roadFeatures	0.883	0.671	0.112	0.545
26	\(\omega _2\)	400	allVars	0.881	0.713	0.167	0.727
27	\(\omega _2\)	400	unCorr	0.886	0.728	0.122	0.727
28	\(\omega _2\)	400	netOnly	0.886	0.749	0.129	0.591
29	\(\omega _2\)	400	networkSome	0.888	0.770	0.120	0.636
30	\(\omega _2\)	400	roadFeatures	0.906	0.811	0.096	0.545

*** indicates the best results, ** the second best, and * the third best result

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