### Table 1. Size of independent sets found in 3-Regular graphs

"... In PAGE 8: ....p.1., which is asymptotically larger than the independent set constructed by Random (the asymptotics for Random can be found in [2]). The data in Table1 show that the lower bound on the performance of Greedy is tight. Each entry is an average over 1000 randomly generated graphs.... ..."

### Table 1. Size of independent sets found in 3-Regular graphs

"... In PAGE 8: ....p.1., which is asymptotically larger than the independent set constructed by Random (the asymptotics for Random can be found in [2]). The data in Table1 show that the lower bound on the performance of Greedy is tight. Each entry is an average over 1000 randomly generated graphs.... ..."

### Table 1. Size of independent sets found in 3-Regular graphs

"... In PAGE 8: ....p.1., which is asymptotically larger than the independent set constructed by Random (the asymptotics for Random can be found in [2]). The data in Table1 show that the lower bound on the performance of Greedy is tight. Each entry is an average over 1000 randomly generated graphs.... ..."

### Table 1: SAT-encoded Graph Coloring test-sets ( at random graphs). All in- stances are satisflable.

### Table 5: Count of reconstruction numbers within classes of graphs

2004

"... In PAGE 34: ...Table5 shows the distribution of reconstruction numbers among a few classes of graphs. The distributions for bipartite graphs and regular graphs further validate previously proposed ideas.... ..."

Cited by 4

### Table 1. Graph-Coloring Model Parameter

"... In PAGE 2: ... In each cycle, independent of whether the color decision process is activated or not, the node may randomly select any color with a probability specified in the Reset Probability (RP) parameter. Table1 lists all available model parameters that may be var- ied in the exploration of the emergent system dynamics. In the following section three we present a software infrastructure that supports a systematic exploration of this parameter space.... In PAGE 3: ... 4.1 Parameter Space Table1 lists the parameters of our agent model of distributed graph coloring. These parameters may be grouped into problem parameters, solution parameters, and environmental parameters.... ..."

### Table 1. Random Graphs: Leighton graphs, Gn;p Graphs, Ge- ometric graphs, at graphs.

1993

"... In PAGE 12: ....1.1. Leighton Graphs. (See Table1 .) Hybrid outperformed the simpler RLF and Saturation heuristics on all Leighton graphs.... In PAGE 14: ....1.4. Flat Graphs. (See Table1 .) Again, Hybrid did not come close to the optimal colorings on these graphs, although it found better colorings than RLF and Saturation.... In PAGE 14: ....1.4. Flat Graphs. (See Table 1.) Again, Hybrid did not come close to the optimal colorings on these graphs, although it found better colorings than RLF and Saturation. When given an initial target corresponding to k, instead of having Hybrid start from an initial coloring and steadily decrease the number of colors, Hybrid successfully found the optimal coloring of two of the 300 vertex graphs ( Table1 presents results achieved by setting the target automatically so these optimal results do not appear). 4.... ..."

Cited by 19

### Table 1: Experimental results for the regular graph \misp102 quot; with n = 102 vertices and ve random graphs with edge density d = 0:1 (\misp100-01 quot;), d = 0:2 (\misp100-02 quot;), d = 0:3 (\misp100-03 quot;), d = 0:4 (\misp100-04 quot;) and d = 0:5 (\misp100-05 quot;). An independent set size k = 45 was chosen for the random graphs, but for the graph with d = 0:1 the genetic algorithm identi ed a larger independent set.

1994

Cited by 19