### Table 1: Results for bipartite graphs with |Vi| vertices per bipartition and |E| edges.

2003

"... In PAGE 10: ...158 Graphbase [11] that were used in the experiments of Mutzel [13, 14]2. The results of our experiments are shown alongside the results of Mutzel [14] in Table1 . Each row in the table corresponds to the average values from applying the algorithm to 100 different graphs3.... In PAGE 10: ... It is, however, meaningful to compare the shapes of the |E| versus running time graphs. In the first 17 rows of Table1 , we see that the FPT implementation is quite efficient up to |E| = 55, finding exact solutions to all input graphs. After |E| = 55, the FPT implementation is able to obtain exact solutions to only a few input graphs for the maximum time of 600 seconds (10 minutes) per graph.... In PAGE 10: ... 2We note that Theorem1 does not require the input graph G to be bipartite; consequently, our implementation is not limited to bipartite graphs. 3The graphs for the experiments corresponding to the first 17 rows of Table1 can be repro- duced using the Stanford Graphbase [11]. We first generate 1700 random integers beginning with seed 5841.... ..."

Cited by 4

### Table 1: A summary of algorithms for the non-bipartite matching problem. The quan- tities n and m respectively denote the number of vertices and edges in the graph.

"... In PAGE 1: ...1 Matching algorithms The literature for non-bipartite matching algorithms is quite lengthy. Table1 provides a brief summary; further discussion can be found in [48, x24.4].... ..."

### Table 7. Time needed to find the best matching.

"... In PAGE 4: ... In general, it cannot handle complete graphs with more than 10 nodes. In contrast, our algorithm can easily deal with graphs whose sizes exceed 10 nodes: Table7 shows the time needed to find the best matching with K=5.... ..."

### Table 6: NMI scores of the algorithms on bi-partite graphs

"... In PAGE 9: ... 6.2 Results and Discussion Table6 shows the NMI scores of the nine algorithms on the bi-partite graphs. For the BP-b1 graph, all the algo- rithms provide perfect NMI score, since the graphs are gen- erated with very clear structures, which can be seen from the parameter matrix in Table 2.... ..."

### Table 2. Results on 5 multivalent matching problems. For each problem and for each algorithm, the table displays the average similarity (Sim), the average number of moves (Mv) and the average CPU time in seconds (T) needed to find the best solution.

2005

"... In PAGE 10: ... Each problem named hom-vN-eM is composed of a cou- ple of non labeled graphs such that the first graph has N vertices and M edges (randomly generated) and the second graph is obtained by randomly removing 6 vertices and their incident egdes of the first graph, and then randomly splitting 5 vertices and their incident edges. Table2 shows the results obtained by ANT-GM and RTS on these multivalent matching problems. On this table, one can note that similarities computed by ANT-GM are slightly worse than those computed by RTS.... ..."

Cited by 2

### Table 2: List of Instrumental Indicators that Substantiated the Pattern Matching Findings

"... In PAGE 4: ... The researchers adopted short working definitions for each implementation approach and instrumental indicators as a strategy to clarify the exact parameters used in the analysis of empirical evidence. Table2 summarises the instrumental indicators collected in the case studies. Some of the heuristic implementation approaches were quite straightforward to identify in the practice of the case studies.... ..."

### Table 2 Algorithms for graph matching

1999

"... In PAGE 10: ... The experimental conditions are summarised in Table 2. Each of the algorithms listed in Table2 , except HC, was run 100 times. Since HC is deterministic, it was only run once per graph.... ..."

### Table 1: The number of bipartite Steinhaus graphs for small n small table. This contrasts sharply with the fact that we can now compute b(n) for large values of n, and even enumerate all bipartite Steinhaus graphs for every reasonable value of n with minimal computational e ort. Looking at this table of initial values of b(n), one might come up with the following conjecture: b(n) seems to be 2(n ? 1) plus some nonnegative noise which follows no obvious pattern. One does not expect the existence of a simple expression for b(n) or even a fast algorithm to compute it. As a consequence of the Main Theorem, we can show that all such conjectures are wrong. Let n0 = n ? 1 be the length of the sequences D, and let k be de ned by 2k n0 lt; 2k+1. We di erentiate between two types of bipartite Steinhaus graphs: those which are subgraphs of the in nite bipartite Steinhaus graphs and those which are not. We can count the number of bipartite Steinhaus graphs of n vertices which are subgraphs of in nite bipartite Steinhaus graphs as follows: There is one Steinhaus graph without any ones in the sequence D.

"... In PAGE 15: ... The second inequality has been obtained from the recurrence relation b(n + 1) 2b(n) and the computation of b(n) for n 24 by exhaustive search. Table1 [5] shows b(n) for a few more values of n. It is interesting to note that before our... ..."

### Table 1. The proposed fast motion estimation algorithm.

"... In PAGE 2: ... In the following stages, CX BP C2 are used for computing BW CX . The proposed multiresolution motion estimation method algo- rithm is shown is Table1... In PAGE 4: ...0000 4. EXPERIMENTAL RESULTS The two-dimensional version of algorithm of Table1 was used for finding the best match for each block in each frame of video se- quences, from a search conducted in its neighborhood in the pre- vious frame. For the first 30 frames of the gray-scale, 8 bit-per-pixel, BFBIBCA2 BEBKBK , salesman video sequence, with blocks of size BDBI A2 BDBI,and search area of BFBF A2 BFBF (W=16), and D4 BPBE, the proposed method gives an speed up of more than 36 compared to a full search.... ..."