算法结果CSV格式说明

all_pairs_shortest_paths

本地，OBS

# runtime: {runtime}

# paths_number: {paths_number}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# batch_paths:

每行为1对pair的多条路，格式：

{sourceID},{targetID},"[[{sourceID},{v1},...,{targetID}],...]"

# runtime: 4.411

# paths_number: 20

# data_total_size: 25

# data_return_size: 25

# data_offset: 0

# batch_paths:

"121","66","[["121","25","66"]]"

all_shortest_paths

本地，OBS

# runtime: {runtime}

# source: {source}

# target: {target}

# paths_number: {paths_number}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# paths:

每行为一条路，格式：

{sourceID},{vertexID1},...,{targetID}

# runtime: 0.207

# source: 121

# target: 66

# paths_number: 2

# data_total_size: 2

# data_return_size: 2

# data_offset: 0

# paths:

121,7,66

121,25,66

all_shortest_paths_of_vertex_sets

本地，OBS

# runtime: {runtime}

# source: {source}

# target: {target}

# paths_number: {paths_number}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# paths:

每行为一条路，格式：

{sourceID},{vertexID1},...,{targetID}

# runtime: 2.772

# sources: 48,129,34,36

# targets: 46,66,101

# paths_number: 15

# data_total_size: 15

# data_return_size: 15

# data_offset: 0

# paths:

36,72,101

36,59,46

36,73,46

betweenness

本地，OBS

# runtime: {runtime}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# betweenness:

{vertexID},{betweenness}

# runtime: 1.593

# data_total_size: 32

# data_return_size: 32

# data_offset: 0

# betweenness:

79,20.697222222222223

80,12.290584415584414

81,1.5

bigclam

本地，OBS

# runtime: {runtime}

# community_num: {community_num}

# log_likelihood: {log_likelihood}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# communities:

{vertexID}, {community}

# runtime: 2.754

# community_num: 1

# log_likelihood: -5593.4549824494925

# data_total_size: 32

# data_return_size: 32

# data_offset: 0

# communities:

6,0

13,0

cesna

本地，OBS

# runtime: {runtime}

# community_num: {community_num}

# log_likelihood: {log_likelihood}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# communities:

{vertexID}, {community}

# runtime: 40114.213

# community_num

# log_likelihood

# data_total_size: 1344

# data_return_size: 1344

# data_offset: 0

# communities:

3850,3

3858,3

3866,3

closeness

本地，OBS

# runtime: {runtime}

# source: {source}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# closeness:

{closeness}

# runtime: 0.394

# source: 12

# data_total_size: 1

# data_return_size: 1

# data_offset: 0

# closeness:

0.5087719298245614

cluster_coefficient (statistic = true)

本地，OBS

# runtime: {runtime}

# cluster_coefficient: {cluster_coefficient}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# vertex_cluster_coefficient:

{vertexID},{cluster_coefficient}

# runtime: 0.661

# cluster_coefficient: 0.13517429595852912

# data_total_size: 32

# data_return_size: 32

# data_offset: 0

# vertex_cluster_coefficient:

common_neighbors_of_vertex_sets

本地，OBS

# runtime: {runtime}

# common_neighbors: {common_neighbors}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# vertices:

{vertexID}

# runtime: 0.42

# common_neighbors: 26

# data_total_size: 26

# data_return_size: 26

# data_offset: 0

# vertices:

103

138

98

connected_component

本地，OBS

# runtime: {runtime}

# community_num: {community_num}

# Max_WCC_size: {Max_WCC_size}

# Max_WCC_id: {Max_WCC_id}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# community:

{vertexID},{community}

# runtime: 0.263

# community_num: 1

# Max_WCC_size

# Max_WCC_id

# data_total_size: 32

# data_return_size: 32

# data_offset: 0

# community:

2,0

6,0

13,0

edge_betweenness

本地，OBS

# runtime: {runtime}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# edge_betweenness:

{sourceID},{targetID},{edge_betweenness}

# runtime: 153.006

# data_total_size: 311

# data_return_size: 311

# data_offset: 0

# edge_betweenness:

51,20,1.3333333333333333

51,33,7.192099567099566

51,10,3.4761904761904763

infomap

本地，OBS

# runtime: {runtime}

# min_code_length: {min_code_length}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# community:

{vertexID},{community}

# runtime: 98.158

# min_code_length: 6.2680095519443135

# data_total_size: 32

# data_return_size: 32

# data_offset: 0

# community:

2,20000000055

6,20000000050

13,20000000014

k_hop

本地，OBS

# runtime: {runtime}

# source: {source}

# k: {k}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# vertices:

{vertexID}

# runtime: 0.442

# source: 76

# k: 6

# data_total_size: 32

# data_return_size: 32

# data_offset: 0

# vertices:

2

6

13

kcore

本地，OBS

# runtime: {runtime}

# kmax: {kmax}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# coreness:

{vertexID},{coreness}

# runtime: 10.882

# kmax: 15

# data_total_size: 32

# data_return_size: 32

# data_offset: 0

# coreness:

2,14

6,15

13,15

label_propagation

本地，OBS

# runtime: {runtime}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# community:

{vertexID},{community}

# runtime: 2.624

# data_total_size: 32

# data_return_size: 32

# data_offset: 0

# community:

2,10000000024

6,10000000024

13,10000000024

link_prediction

本地，OBS

# runtime: {runtime}

# source: {source}

# target: {target}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# link_prediction:

{link_prediction}

# runtime: 0

# source: 123

# target: 43

# data_total_size: 1

# data_return_size: 1

# data_offset: 0

# link_prediction:

0.07017543859649122

louvain

本地，OBS

# runtime: {runtime}

# modularity: {modularity}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# community:

{vertexID},{community}

# runtime: 45.835

# modularity: 0.16375671670152867

# data_total_size: 32

# data_return_size: 32

# data_offset: 0

# community:

2,20000000062

6,20000000050

13,20000000050

n_paths

本地，OBS

# runtime: {runtime}

# source: {source}

# target: {target}

# paths_number: {paths_number}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# paths:

每行为一条路，格式：

{sourceID},{vertexID1},...,{targetID}

# runtime: 8.025

# source: 123

# target: 87

# paths_number: 100

# data_total_size: 100

# data_return_size: 100

# data_offset: 0

# paths:

123,21,87

123,13,87

123,32,87

od_betweenness

本地，OBS

# runtime: {runtime}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# edge_betweenness:

{sourceID},{targetID},{edge_betweenness}

# runtime: 1.391

# data_total_size: 311

# data_return_size: 311

# data_offset: 0

# edge_betweenness:

51,20,0

51,33,0

51,10,0

pagerank

本地，OBS

# runtime: {runtime}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# pagerank:

{vertexID},{pagerank}

# runtime: 4.044

# data_total_size: 32

# data_return_size: 32

# data_offset: 0

# pagerank:

2,0.007888904051903298

6,0.013215863692849642

13,0.01860530199450448

personalrank

本地，OBS

# runtime: {runtime}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# personalrank:

{vertexID},{personalrank}

# runtime: 2.326

# source: 46

# data_total_size: 49

# data_return_size: 49

# data_offset: 0

# personalrank：

0,0.0021350905350732297

1,0.004591151406893241

shortest_path

本地，OBS

# runtime: {runtime}

# source: {source}

# target: {target}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# path:

每行为一条路，格式：

{sourceID},{vertexID1},...,{targetID}

# runtime: 0.308

# source: 123

# target: 5

# data_total_size: 1

# data_return_size: 1

# data_offset: 0

# path:

123,10,137,5

shortest_path_of_vertex_sets

本地，OBS

# runtime: {runtime}

# source: {source}

# target: {target}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# path:

每行为一条路，格式：

{sourceID},{vertexID1},...,{targetID}

# runtime: 1.832

# source: 24

# target: 121

# data_total_size: 1

# data_return_size: 1

# data_offset: 0

# path:

24,121

single_vertex_circles_detection

本地，OBS

# runtime: {runtime}

# source: {source}

# min_circle_length: {min_circle_length}

# max_circle_length: {max_circle_length}

# limit_circle_number: {limit_circle_number}

# circle_number: {circle_number}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# circles:

每行为一条路，格式：

{sourceID},{vertexID1},...,{sourceID}

# runtime: 37.46

# source: 122

# target:

# min_circle_length: 3

# max_circle_length: 10

# limit_circle_number: 100

# circle_number: 100

# data_total_size: 100

# data_return_size: 100

# data_offset: 0

# circles:

122,82,79,76,65,122

122,125,135,77,65,122

122,82,114,96,65,122

sssp

本地，OBS

# runtime: {runtime}

# source: {source}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# distance:

{vertexID},{distance}

# runtime: 0.452

# source: 32

# data_total_size: 48

# data_return_size: 48

# data_offset: 0

# distance:

0,2

5,2

7,2

subgraph_matching

本地，OBS

# runtime: {runtime}

# pattern_graph: {pattern_graph}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# subgraphs:

每行为一个匹配的子图，格式：

{vertexID1},{vertexID2},...,{vertexIDn}

------ statistics = true-------

# runtime: 1.376

# pattern_graph: 2,3,1

# data_total_size: 1

# data_return_size: 1

# data_offset: 0

# subgraph_number:

1556

------ statistics = false-------

# runtime: 0.956

# pattern_graph: 2,3,1

# subgraph_number: 0

# data_total_size: 100

# data_return_size: 100

# data_offset: 0

# subgraphs:

0,51,126

0,51,131

0,126,113

topic_rank

本地，OBS

# runtime: {runtime}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# topicrank:

{vertexID},{topicrank}

# runtime: 1.11

# data_total_size: 32

# data_return_size: 32

# data_offset: 0

# topicrank:

2,0.00663068274092574

6,0.007278130208954746

13,0.007869137668788257

triangle_count (statistic = true)

本地，OBS

# runtime: {runtime}

# triangle_count: {triangle_count}

# data_total_size: {data_total_size}

# data_return_size: {data_return_size}

# data_offset: {data_offset}

# vertex_triangles:

{vertexID},{vertex_triangles}

# runtime: 0.491

# triangle_count: 1653

# data_total_size: 32

# data_return_size: 32

# data_offset: 0

# vertex_triangles: