### Table 1: Edge weights for graph G in Figure 1.

"... In PAGE 6: ... Why should author x obtain more rank than author y from a particular citing author only for the reason that he/she has written more publications? Briefly, we set b to zero whenever c is zero. Example Table1 shows edge weights for graph G in Figure 1. The coefficients f, g, h, and hd are zero when c is zero as mentioned in the paragraph above, but their non-zero variants are also presented in parentheses for illustration.... In PAGE 21: ... The newest citing paper is from 2001 as pointed out above. Prediction We show the top 40 authors for each ranking method in Table 8, Table 9, Table1 0, and Table 11 in an appendix.... In PAGE 29: ...29 Table1 0: Top 40 DBLP authors for each ranking (part 3). b c d 1 Michael Stonebraker Michael Stonebraker Michael Stonebraker 2 Jim Gray Jim Gray Jim Gray 3 David J.... In PAGE 30: ...30 Table1 1: Top 40 DBLP authors for each ranking (part 4). e f g 1 Michael Stonebraker Jim Gray E.... ..."

### Table 1. CPU Times Required on an IBM RS/6000-580 Workstation To Generate the Automorphism Groups of Edge-Weighted Graphs in Figure 4

1994

"... In PAGE 4: ... We show eight edge-weighted graphs labeled I-VI11 in Figure 4 for com- putational consideration. Table1 shows the number of permutations in the auto- morphism groups of the edge-weighted graphs in Figure 4 as generated by our computer together with their CPU times. As seen from Table 1, four of the eight graphs in Figure 4 took less than a second of CPU time on an IBM RS 6000-580 workstation.... In PAGE 4: ... Table 1 shows the number of permutations in the auto- morphism groups of the edge-weighted graphs in Figure 4 as generated by our computer together with their CPU times. As seen from Table1 , four of the eight graphs in Figure 4 took less than a second of CPU time on an IBM RS 6000-580 workstation. Some of the graphs shown in Figure 4 were considered to be quot;pathological quot; in the literat~re.... ..."

### Table 1. Edge weights for the graph construction, G is the graph, N is the neighborhood system.

### Table 3.1: Graph Edge-Weights. From Boykov et al. Edge Weight For

2000

### Table 6: Deviations from Optimality in Percent: K-Card Subgraph with Edge Weights (Graphs and Grid Graphs with 30 Nodes)

1997

Cited by 9

### Table 1: Assigning edge weights for the graph cuts problem. Notation is as enumerated in Section 4 and equations 2,3,4. This table is taken from [BJ01] and is mentioned here for completeness.

"... In PAGE 5: ...This is achieved by assigning appropriate weights to the edges of the graph and finding a minimum-weight edge cut of the graph such that the two special nodes are in sepa- rate components. The edge weights are assigned as enumer- ated in Table1 . Our algorithm performs segmentation in two passes: in the first pass it performs image segmentation us- ing GI and in the second pass it performs 3D segmentation by projecting the clusters created by the over-segmentation (Section 5).... ..."

### Table 17: 100,000 Node Delaunay Graphs with Random Edge Weights (IBM 590, seconds)

1999

"... In PAGE 16: ... To give a comparison with non-geometric instances, in Table 16 we report times on random Delanuay graphs where the integer edge weights are chosen at random (uniformly) from the interval 0-9,999. In Table17 , we give an indication of the... ..."

Cited by 43

### Table 2 Comparison of the GIST andGMST on three diVTerent graphs a

"... In PAGE 14: ... A simple example will serve to illustrate this point. Table2 compares the weight andtortuosity of trees generated on several underlying graphs. The FFrst data set is for the graph used in generating Fig.... In PAGE 15: ...rom a Gaussian distribution with mean 0.5. The Gaussian distribution was scaled such that the standard deviation matched that of a uniform distribution between 0 and 1, with values below 0 or above 1 set to the appropriate extreme. The statistical properties of the GIST andGMST on this graph are listedin Table2 as Graph 3. The results are very similar to those for a graph with uniformly distributed edge weights.... ..."

### Table 16: Delaunay Graphs with (0-9,999) Edge Weights (IBM 590, seconds)

1999

Cited by 43

### Table 1: The rst line of results corresponds to solving both MC relax- ations for a 5-cycle with unit edge-weights; the others come from randomly generated weighted graphs.

1999

Cited by 15