# 算法结果TXT格式说明 表1算法结果的txt格式 | 算法 | 支持程度 | header | content | e.g. | |:---|:---|:---|:---|:---| | 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: | ![](https://support.huaweicloud.com/api-ges/public_sys-resources/note_3.0-zh-cn.png) 算法结果失败返回示例： ``` Http Status Code: 400 { "errorMessage": "Unsupported output file format", "errorCode": "GES.8301" } ```