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Table 5 Gap between the best AUC value and the AUC of the algorithm corresponding to the best measure value

From: Unsupervised evaluation of multiple node ranks by reconstructing local structures

  

Structural Measures

Local Structure Measures

 

Network

Examples

Cond/nce

Density

Modularity

LinkCC

LinkAUC

HopAUC

NDCG

Amazon

0.1%

24%

23%

23%

0%

0%

0%

0%

Amazon

1%

20%

6%

12%

6%

0%

0%

3%

Amazon

10%

9%

4%

6%

7%

1%

2%

0%

Amazon

25%

5%

4%

27%

0%

2%

2%

1%

Amazon

50%

17%

3%

3%

3%

2%

2%

1%

DBLP

0.1%

11%

10%

6%

9%

6%

6%

0%

DBLP

1%

4%

11%

1%

15%

0%

0%

0%

DBLP

10%

1%

5%

1%

2%

1%

1%

5%

DBLP

25%

0%

2%

0%

2%

0%

1%

2%

DBLP

50%

1%

2%

1%

1%

1%

1%

0%

BlockModel

0.1%

33%

12%

33%

33%

5%

5%

0%

BlockModel

1%

29%

7%

0%

36%

6%

6%

0%

BlockModel

10%

33%

8%

4%

40%

13%

5%

0%

BlockModel

25%

35%

5%

9%

43%

10%

10%

0%

BlockModel

50%

36%

4%

1%

42%

9%

1%

1%

CiteSeer

0.1%

4%

1%

0%

1%

1%

1%

1%

CiteSeer

1%

1%

0%

1%

4%

3%

4%

2%

CiteSeer

10%

2%

2%

2%

1%

2%

2%

0%

CiteSeer

25%

3%

2%

3%

3%

3%

3%

1%

CiteSeer

50%

3%

4%

3%

3%

3%

3%

1%

PubMed

0.1%

9%

13%

2%

22%

19%

18%

0%

PubMed

1%

7%

9%

7%

21%

12%

7%

1%

PubMed

10%

4%

6%

4%

4%

9%

9%

0%

PubMed

25%

4%

8%

4%

27%

4%

4%

0%

PubMed

50%

5%

11%

5%

27%

4%

3%

0%

Number of gaps ≤5%

11

13

18

12

17

20

25

Number of gaps ≤7%

12

16

22

14

19

23

25

  1. Gaps closer to zero mean that the AUC found when selecting the algorithm optimizing the respective measure is close to the max AUC between all algorithms. The smallest gap among unsupervised measures in each experiment setting is bolded