### Table 6: Average per-graph compute-time for k-edge-deletion reconstruction numbers for graphs on 7 vertices and 5-19 edges

2007

### Table 2. Degrees of the Vertices in the NBA graph

2004

"... In PAGE 10: ...he degree of the vertex (i.e., the number of edged starting from it, or, the number of teammates) corresponding to Jordan is only 24. Table2 presents the number of vertices in the NBA graph corresponding to different intervals of the degree values. It would be reasonable to assume that if one picks a vertex with a high degree as the center of the NBA graph, the average distance in the graph corresponding to this vertex would be smaller than the average Jordan num- ber.... ..."

Cited by 1

### Table 1: Large random graphs of small degree.

"... In PAGE 13: ... The degrees considered range from 3 to 16, degrees typical in VLSI and processor mapping problems. The number of edges, vertices and average degree of the random graphs used in our experiments are given in Table1 . We used six graphs of average degree 4 and six graphs of average degree 8 to study the e#0Bect of increasing graph size on solution quality and running time.... In PAGE 34: ...0335 0.0480 Table1 1: Hypercube embeddings of 128-vertex, degree-7 geometric graphs. Comparison of Mob to SA.... ..."

### Table 5: Average run times over 3 graphs for each of three di erent sets of hand-designed strongly regular graphs (to identify as highly ambiguous). Size=number of nodes in each graph, Degree=degree of any node, and # Edges=no. of edges in each graph

"... In PAGE 23: ... We have also tested the algorithm with hand-designed strongly regular graphs [24] on which most existing algorithms arrive at incorrect decisions or take an exponential time. Our algorithm stopped and classi ed these graphs as highly ambiguous in a reasonable amount of time as Table5 shows. 4.... ..."

### Table 1: Average time for 2000 runs of each con guration of isomorphic randomly generated graphs. Size: number of nodes in each graph, Degree: maximum degree of any node, Edge %: percentage of possible edges (based on the size and degree) allowed, # Edges: number of edges decided by the random graph generator for the given percentage, Ambiguity %: percentage of graphs that are detected by the algorithm as highly ambiguous, and Avg time: is the average of the actual run times over 2000 runs.

"... In PAGE 20: ... We performed our tests on an SGI Indigo 2 workstation with a 200MHz MIPS R4400 processor and 64 MB of memory. Table1 shows the results of tests on isomorphic graphs and Table 2 shows the results of tests on non-isomorphic graphs. For each entry in the table (which represents a certain con guration of the generated graphs), we conducted 2000 tests on di erent edge-connectivity graphs and averaged the results.... In PAGE 20: ... For each entry in the table (which represents a certain con guration of the generated graphs), we conducted 2000 tests on di erent edge-connectivity graphs and averaged the results. Notice in Table1 that lower the maximum degree and the percentage of edges in a graph, the probability is higher for the random generator to produce highly ambiguous graphs. This is because in such con gurations, the level of randomness in the characteristics of nodes generated is restricted (especially since we require the graphs to be connected).... ..."

### Table B shows the measurement of the dependencies graph before refactoring. For each node, the degree is given. The degree of a node in an undirected graph is the number of edges incident to the node.

in Edited by:

2006

### Table 3. Chromosome associations and syntenies of the reconstructed mammalian ancestor

in References

2007

"... In PAGE 3: ... The results are shown in Table 1 (see also Supplemental material). The solutions ob- tained in the various runs are very consistent both in terms of the number of rearrangements on the edges of the recovered scenario (see Table 2) and of the observed chromosomal associations in the putative ancestors (see Table3 ). In the rest of this section, we present a detailed description of the results on run gene7, which allows for genes to be merged into the same block when there are up to two intervening genes per species.... In PAGE 8: ... Some human chromosome syntenies were systematically preserved in MA (13, 14, 20, 21, and X) and similarly, some chicken chromosome syntenies were found in MA for all runs (20, 21, 23, 24, and 27). Many of the discrepancies observed in Table3 can be explained by differences in the coverage of gene-based data versus sequence-based data. For instance, two chicken chromosome syntenies (22, 32) are only observed in sequence-based data mainly because they are covered by fewer blocks (1 block each in run 300K) (see Fig.... ..."

### Table 1: Test suite. For each graph, its name, number of vertices, number of edges, degree information (minimum/average/maximum), diameter and family.

2003

Cited by 6

### Table 1: Lower bounds for the total number of bends in drawings of m-edge c-connected graphs with maximum degree .

"... In PAGE 3: ... We use these lower bounds for the number of bends in complete graphs as the basis for the construction of in nite families of c-connected graphs of maximum degree with lower bounds on the number of bends for each member of the class. Table1 summarises the lower bounds proved in this paper for the total number of bends in 3-D orthogonal graph drawings. Upper bounds: A number of algorithms have been proposed for 3-D orthogonal graph drawing [5, 7, 9, 11, 15, 19, 28, 31] which explore the apparent tradeo between the number of bends and the bounding box volume (see [31]).... ..."

Cited by 5

### Table 1 presents the non replicated graphs details, showing the total number of nodes and terminal nodes, edges, and the connectivity degree (number of edges number of edges in a complete graph). Figures 6, 7, 8 and 9 present the non replicated test instances.

"... In PAGE 4: ... Table1 : Non replicated graphs details. We replicated the instances shown in Table 1 between 2 and 13 times, building more complex instances allowing studying the algorithms efficacy when the problems grow in size.... In PAGE 4: ...Table 1: Non replicated graphs details. We replicated the instances shown in Table1 between 2 and 13 times, building more complex instances allowing studying the algorithms efficacy when the problems grow in size. Details for the most complex replica generated (13 replicas) are shown in Table 2.... ..."