### Table 7: Graph method threshold test

2006

"... In PAGE 38: ...xperiments, that is 3.3 and 3.4. The results are shown in Table7 . As in the case of the histogram method threshold, the graph method threshold influences only its relevant hybrid, GT.... ..."

### Table II. Connected component sizes in email graph, threshold 30

2003

Cited by 72

### Table 4: Performance of the algorithm for the threshold graph TH(n).

in An efficient implementation of a quasi-polynomial algorithm for generating hypergraph transversals

2003

Cited by 4

### Table 2: Experimental evaluation on Threshold, Self-Dual Threshold, and Self- Dual Fano-Plane graphs.

Cited by 3

### Table 1: All graphs on three vertices are threshold graphs. We give one representative a for every set.

2005

"... In PAGE 2: ...irst few values in this sequence are 1, 2, 8, 46, . . .. The first term which is not equal to n! is A3 = 8. Indeed, the examples of Table1 prove that all 23 subsets Sa of... ..."

### Table 1: All graphs on three vertices are threshold graphs. We give one representative a for every set.

2005

"... In PAGE 2: ...irst few values in this sequence are 1, 2, 8, 46, . . .. The first term which is not equal to n! is A3 = 8. Indeed, the examples of Table1 prove that all 23 subsets Sa of... ..."

### Table 2 The comparison between graphs generated using PC+GGM (with varying threshold s) and PC on four randomly chosen components with size 10, 20, 47, and 84, respectively

2006

"... In PAGE 7: ....3.1. Comparison with PC method Table2 shows the comparison between graphs gener- ated using our PC + GGM method and graphs generated using the original PC method. To compare the similarity of two graphs, we use the traditional measures: sensitiv- ity and specificity.... ..."

### Table 1: Performance of Algo 1 for random graphs

"... In PAGE 12: ....2, 0.3, and 0.5. For each value of n and P ve instances were generated. Table1 shows in the third column the number of times our algorithm (Algo 1) is able to nd a proven optimal solution within a limit of 2000 nodes. Column 4 reports the number of times the tree optimal heuristic of Morton and Dharan (MDE) [13] nds an optimal solution.... ..."

### Table 1: Random graphs

1995

"... In PAGE 23: ... ratio of the total cost of the parallel algorithm, and the cost of the sequential algorithm. For example, consider Table1 , the table for the linear transitive closure query on random graphs.... ..."

Cited by 4

### Table 3: Random Graphs

1994

"... In PAGE 8: ... If we de ne the den- sity of a graph G as the number of edges of G = (V; E) over the number of edges of the complete graph with jV j nodes, then for this class of random graphs the density is very close to p. In Table3 we compare our algorithm (column `MS apos;) with the fastest algorithms known so far, due to Carraghan and Pardalos [9], Balas and Xue [26], Babel [2]. The rst two alghorithms were run on a SUN4/260 workstation, and the results are shown in the columns labelled by `CP apos; and `BX apos;.... ..."

Cited by 11