Telecommunications Science ›› 2020, Vol. 36 ›› Issue (12): 20-32.doi: 10.11959/j.issn.1000-0801.2020309
• Research and Development • Previous Articles Next Articles
Shaoqing WU,Yihong DONG,Xiong WANG,Yan CAO,Yu XIN
Revised:
2020-11-27
Online:
2020-12-20
Published:
2020-12-23
Supported by:
CLC Number:
Shaoqing WU,Yihong DONG,Xiong WANG,Yan CAO,Yu XIN. Learning attribute network algorithm based on high-order similarity[J]. Telecommunications Science, 2020, 36(12): 20-32.
"
方法 | 30% | 40% | 50% | 60% | 70% | 80% | 90% |
Lap | 29.66% | 30.54% | 31.67% | 33.87% | 36.29% | 39.53% | 42.71% |
DeepWalk | 79.58% | 80.88% | 81.54% | 82.12% | 82.90% | 82.83% | 83.88% |
SDNE | 59.51% | 61.74% | 63.39% | 64.36% | 65.05% | 65.61% | 65.55% |
TADW | 53.49% | 56.17% | 57.92% | 59.47% | 60.29% | 60.86% | 61.72% |
CANE | 57.28% | 60.25% | 60.89% | 63.27% | 64.08% | 64.65% | 65.24% |
LANAHS_A | 81.02% | 81.27% | 82.26% | 83.02% | 83.71% | 84.12% | 84.31% |
LANAHS | 82.15% | 82.84% | 83.57% | 84.21% | 84.62% | 85.06% | 85.73% |
"
方法 | 30% | 40% | 50% | 60% | 70% | 80% | 90% |
Lap | 51.61% | 52.86% | 55.11% | 56.64% | 58.27% | 59.71% | 59.59% |
DeepWalk | 82.56% | 82.71% | 82.64% | 82.65% | 82.79% | 83.17% | 83.25% |
SDNE | 53.08% | 53.10% | 53.18% | 53.26% | 53.71% | 54.08% | 54.15% |
TADW | 82.50% | 82.71% | 82.91% | 83.29% | 83.29% | 84.04% | 84.04% |
CANE | 82.61% | 82.82% | 83.08% | 83.12% | 83.13% | 83.24% | 83.56% |
LANAHS_A | 83.01% | 83.24% | 83.59% | 83.62% | 83.73% | 84.25% | 84.25% |
LANAHS | 83.75% | 84.75% | 84.89% | 85.26% | 85.28% | 85.43% | 85.72% |
"
方法 | Lap | DeepWalk | SDNE | TADW | CANE | LANAHS_A | LANAHS |
16维 | 0.959 2 | 0.995 2 | 0.652 4 | 0.569 5 | 0.602 5 | 0.985 4 | 0.996 3 |
32维 | 0.973 1 | 0.997 3 | 0.662 0 | 0.599 6 | 0.637 1 | 0.994 1 | 0.998 5 |
48维 | 0.978 6 | 0.998 1 | 0.678 9 | 0.626 0 | 0.662 1 | 0.998 4 | 0.999 0 |
64维 | 0.981 6 | 0.998 4 | 0.679 4 | 0.630 6 | 0.673 6 | 0.999 0 | 0.999 0 |
80维 | 0.983 6 | 0.998 5 | 0.718 9 | 0.661 6 | 0.692 1 | 0.999 0 | 0.999 0 |
96维 | 0.985 8 | 0.998 5 | 0.738 1 | 0.661 8 | 0.708 2 | 0.998 9 | 0.999 0 |
112维 | 0.986 9 | 0.998 5 | 0.773 6 | 0.669 5 | 0.725 4 | 0.998 8 | 0.999 0 |
128维 | 0.988 8 | 0.998 5 | 0.887 7 | 0.679 4 | 0.736 5 | 0.998 8 | 0.999 0 |
"
方法 | Lap | DeepWalk | SDNE | TADW | CANE | LANAHS_A | LANAHS |
16维 | 0.910 9 | 0.994 8 | 0.519 6 | 0.918 7 | 0.905 4 | 0.982 5 | 0.996 9 |
32维 | 0.930 1 | 0.997 0 | 0.523 4 | 0.955 3 | 0.952 3 | 0.983 5 | 0.998 1 |
48维 | 0.952 1 | 0.997 8 | 0.524 2 | 0.966 6 | 0.957 4 | 0.983 7 | 0.999 2 |
64维 | 0.958 6 | 0.998 2 | 0.526 7 | 0.973 0 | 0.978 7 | 0.985 4 | 0.999 2 |
80维 | 0.965 9 | 0.998 5 | 0.531 6 | 0.976 4 | 0.979 2 | 0.993 9 | 0.999 3 |
96维 | 0.975 3 | 0.998 7 | 0.533 6 | 0.978 7 | 0.985 8 | 0.994 0 | 0.999 3 |
112维 | 0.976 8 | 0.998 8 | 0.534 1 | 0.980 5 | 0.985 9 | 0.996 5 | 0.999 3 |
128维 | 0.978 4 | 0.998 9 | 0.539 3 | 0.981 9 | 0.986 3 | 0.996 7 | 0.999 3 |
"
方法 | Lap | DeepWalk | SDNE | TADW | CANE | LANAHS_A | LANAHS |
16维 | 0.791 4 | 0.909 8 | 0.520 2 | 0.799 8 | 0.838 6 | 0.912 3 | 0.918 2 |
32维 | 0.811 6 | 0.927 0 | 0.570 6 | 0.828 1 | 0.857 9 | 0.917 5 | 0.940 1 |
48维 | 0.852 9 | 0.935 1 | 0.537 6 | 0.838 0 | 0.862 4 | 0.923 4 | 0.949 7 |
64维 | 0.866 3 | 0.939 8 | 0.571 5 | 0.848 2 | 0.871 2 | 0.934 9 | 0.953 5 |
80维 | 0.876 5 | 0.942 7 | 0.611 7 | 0.855 5 | 0.884 1 | 0.942 1 | 0.955 7 |
96维 | 0.883 8 | 0.944 7 | 0.567 3 | 0.862 4 | 0.889 7 | 0.945 6 | 0.955 4 |
112维 | 0.890 6 | 0.945 9 | 0.608 1 | 0.866 3 | 0.890 1 | 0.948 0 | 0.955 1 |
128维 | 0.895 8 | 0.946 9 | 0.561 1 | 0.871 7 | 0.894 2 | 0.947 3 | 0.9543 |
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