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Among the mentioned algorithms, ''G-Tries'' is the fastest. But, the excessive use of memory is the drawback of this algorithm, which might limit the size of discoverable motifs by a personal computer with average memory.

Among the mentioned algorithms, ''G-Tries'' is the fastest. But, the excessive use of memory is the drawback of this algorithm, which might limit the size of discoverable motifs by a personal computer with average memory.

===Comparison===

===对比===

Tables and figure below show the results of running the mentioned algorithms on different standard networks. These results are taken from the corresponding sources,<ref name="omi1" /><ref name="kash1" /><ref name="rib1" /> thus they should be treated individually.

 

下面的表格和数据显示了在各种标准网络中运行上述算法所获得的结果。这些结果皆获取于各自相应的来源<ref name="omi1" /><ref name="kash1" /><ref name="rib1" /> ，因此需要独立地对待它们。

[[Image:Runtimes of algorithms.jpg|thumb|''Runtimes of Grochow–Kellis, mfinder, FANMOD, FPF and MODA for subgraphs from three nodes up to nine nodes''.<ref name="omi1" />]]

[[Image:Runtimes of algorithms.jpg|thumb|''Runtimes of Grochow–Kellis, mfinder, FANMOD, FPF and MODA for subgraphs from three nodes up to nine nodes''.<ref name="omi1" />]]

{|class="wikitable"

{|class="wikitable"

|+ Runtimes of Grochow–Kellis, FANMOD, and G-Trie for subgraphs from three nodes up to nine nodes on five different networks.<ref name="rib1" />

|+ Grochow–Kellis, FANMOD, 和 G-Trie 在5个不同网络上生成含3到9个节点子图所用的运行时间 <ref name="rib1" />

|-

|-

!rowspan="2"|Network

!rowspan="2"|网络

!rowspan="2"|Size

!rowspan="2"|子图大小

!colspan="3"|Census Original Network

!colspan="3"|原始网络数据

!colspan="3"|Average Census on Random Networks

!colspan="3"|随机网络平均数据

|-

|-

!FANMOD

!FANMOD

{|class="wikitable"

{|class="wikitable"

|+ Runtimes of mfinder, FANMOD, Mavisto and Kavosh for subgraphs from three nodes up to ten nodes on three different networks.<ref name="kash1" />

|+ mfinder, FANMOD, Mavisto 和 Kavosh 在3个不同网络上生成含3到10个节点子图所用的运行时间<ref name="kash1" />

|-

|-

!&nbsp;

!&nbsp;

!Size →

!子图大小→

!rowspan="2"|3

!rowspan="2"|3

!rowspan="2"|4

!rowspan="2"|4

!rowspan="2"|10

!rowspan="2"|10

|-

|-

!Networks ↓

!网络↓

!Algorithms ↓

!算法↓

|-

|-

|rowspan="4"|E. coli

|rowspan="4"|E. coli