Data sets | hb run time (s) | visone run time – quad Sim (s) | visone run time – tri Sim (s) | ||||||||||||
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Filename | File size (B) | No. of nodes | No. of edges | 1 | 2 | 3 | Avg | 1 | 2 | 3 | Avg | 1 | 2 | 3 | Avg |
random-1000-nodes.graphml | 341,365 | 1,000 | 5,002 | 0.25 | 0.25 | 0.25 | 0.25 | 2.0 | 1.7 | 1.6 | 1.8 | 1.5 | 1.1 | 1.3 | 1.3 |
random-10000-nodes.graphml | 3,555,915 | 10,000 | 49,826 | 0.67 | 0.69 | 0.70 | 0.69 | 7.3 | 6.9 | 6.8 | 7.0 | 7.0 | 7.1 | 6.8 | 7.0 |
random-100000-nodes.graphml | 37,271,224 | 100,000 | 500,061 | 10.01 | 11.74 | 6.55 | 9.43 | 139.4 | 120.1 | 118.5 | 126.0 | 129.0 | 119.7 | 119.1 | 122.6 |
random-250000-nodes.graphml | 95,452,841 | 250,000 | 1,250,487 | 16.84 | 15.36 | 15.24 | 15.81 | 349.3 | 357.3 | 361.3 | 356.0 | 356.8 | 352.7 | 334.5 | 348.0 |
random-500000-nodes.graphml | 193,263,339 | 500,000 | 2,501,346 | 26.21 | 25.71 | 24.47 | 25.46 | >1,200 | |||||||
random-1000000-nodes.graphml | 388,461,043 | 1,000,000 | 4,997,089 | 44.25 | 43.75 | 45.19 | 44.40 | Visone could not load graphml file. Insufficient memory | |||||||
code-dna.graphml | 155,222 | 28 | 292 | <1 sec | <1 sec | <1 sec | <1 sec | <1 sec | <1 sec | <1 sec | <1 sec | <1 sec | |||
jazz-directed.graphml | 361,796 | 198 | 4,113 | <1 sec | <1 sec | <1 sec | <1 sec | <1 sec | <1 sec | <1 sec | <1 sec | <1 sec | |||
toster_CA_Edge.graphml | 5,349,861 | 23,916 | 75,050 | 1.02 | 0.96 | 0.96 | 0.98 | 20.6 | 19.8 | 20.1 | 20.2 | 17.1 | 18.9 | 17.8 | 17.9 |
iran-tweet-replies.no-retweet.by-userid.graphml | 294,153,484 | 228,626 | 440,244 | 1.26 | 1.12 | 1.13 | 1.17 | >1,200 |
Feature | Hairball buster | Histogram/node-degree display | Force-directed | Visone backbone | Adjacency matrix | Block modeling |
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1. Distribution of nodes by degree | Yes | Yes | No | No | Nof | Nof |
2. Quickly determine the number of high-degree nodes | Yes | Yes | No | No | Yes | Nof |
3. Quickly identify which are the highest degree nodes | Yes | Yesa | Nob | No | Yes | Yes |
4. Determine if the highest degree nodes are directly connected to other high-degree nodes | Yes | No | Yesc | Nob | Yes | Yes |
5. Determine whether the highest degree nodes are connected to each other indirectly via two hops | Yes | No | Yes | Yesc | Yes | Yes |
6. Determine which lower-degree nodes are directly connected to the high-degree nodes | Yes | No | Yes | Yes | Yes | Yes |
7. Provide visual cue of how much difference exists between the degree of the nodes, especially high-degree nodes | Yes | Yes | No | No | No | Yes |
8. Determine if there is one central cluster or many clusters that contain the highest degree nodes | Yes | No | Yes | Yes | No | Yes |
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9. Provide log–log or semi–log representation for very large data sets | Yes | Yes | No | No | No | No |
10. Can visualize both directed and undirected graphs | Yes | No | Yese | Yese | Yes | Yes |
11. Determine which nodes connect to the highest weighted links | Yes | No | Yesd | Yes | Yesg | Yesg |
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12. Distribution of nodes by other centrality measures | Yes | Yes | No | No | No | No |
13. Provide a canonical representation of the graph | Yes | Yes | No | No | Yes | No |
14. Low calculation cost | Yes | Yes | No | No | Yes | Noh |