A New Link Prediction Method for Complex Networks Based on Resources Carrying Capacity Between Nodes
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摘要: 链路预测旨在发现网络的未知、缺失连接，具有重要的实际应用价值。基于网络结构相似性的链路预测方法具有简单且有效的特点，受到各领域学者的普遍关注。然而，许多现有方法在计算节点间存在连接可能性时，忽视了节点间资源承载能力的影响。鉴于此，该文提出一种基于节点间资源承载度的链路预测方法。该方法首先通过分析节点间资源传输过程，进而对节点间资源承载能力进行量化，提出资源承载度。然后，基于资源承载度对节点间连接可能性的影响进行分析，并提出相应的链路预测方法。9个真实网络的实验结果表明，相比其他链路预测方法，该方法在3个衡量标准下均具有较高的预测精度。Abstract: Link prediction aims to discover the unknown or missing links of complex networks, which plays an important role in practical application. The similarity-based link prediction methods attract a lot of attention due to their briefness and effectiveness. However, most of similarity indices ignore the influence of resource carrying capacity between nodes when calculating the likelihood that a link exists between two endpoints. Because of the problem, a new link prediction method based on resources carrying capacity between nodes is proposed. Firstly, the resource carrying capacity is proposed to quantify the capability of resource carrying between nodes. Then, based on the resource carrying capacity, a new link prediction method is proposed by analyzing the impact of node connectivity. The experimental results of nine real networks show that compared with other link prediction methods, the proposed method can achieve higher prediction accuracy under three standard metrics.
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Key words:
- Complex network /
- Link prediction /
- Resources carrying capacity /
- Similarity
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表 1 网络数据特征参数
Network AIDS FWFB FWFW CE Email PB Hamster Figeys UC |V| 146 128 69 297 167 1222 1858 2239 1899 |E| 180 2075 880 2148 5784 16717 12534 6432 13838 C 0.052 0.335 0.552 0.308 0.541 0.361 0.0904 0.04 0.109 <k> 2.47 32.42 25.51 14.46 69.26 27.36 13.49 5.76 14.57 <d> 3.42 1.78 1.64 2.46 1.87 2.74 3.39 3.98 3.06 r –0.725 –0.112 –0.298 –0.225 –0.295 –0.221 –0.085 –0.331 –0.188 
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表 2 AUC结果对比
Network AIDS FWFB FWEW CE Email PB Hamster Figeys UC CN 0.588 0.605 0.686 0.853 0.920 0.923 0.817 0.563 0.782 RA 0.601 0.609 0.701 0.873 0.928 0.927 0.822 0.568 0.786 AA 0.602 0.608 0.695 0.870 0.922 0.926 0.821 0.567 0.786 CAR 0.589 0.620 0.689 0.853 0.919 0.921 0.817 0.564 0.780 LP-0.001 0.831 0.622 0.707 0.871 0.922 0.935 0.936 0.889 0.891 LP-0.01 0.831 0.670 0.730 0.871 0.921 0.937 0.942 0.903 0.902 Katz-0.001 0.847 0.620 0.706 0.870 0.921 0.934 0.935 0.886 0.891 Katz-0.01 0.848 0.675 0.737 0.869 0.919 0.932 0.939 0.900 0.901 ACT 0.951 0.722 0.784 0.755 0.900 0.891 0.871 0.918 0.895 Cos+ 0.584 0.650 0.514 0.862 0.906 0.925 0.961 0.843 0.871 QN-18.5 0.936 0.918 0.927 0.896 0.935 0.946 0.977 0.945 0.928 QN-max 0.962 0.919 0.928 0.896 0.936 0.947 0.977 0.952 0.928
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表 3 Pre结果对比
Network AIDS FWFB FWEW CE Email PB Hamster Figeys UC CN 0.014 0.086 0.161 0.131 0.708 0.417 0.015 0.011 0.022 RA 0.026 0.088 0.170 0.129 0.727 0.247 0.007 0.014 0.020 AA 0.026 0.090 0.164 0.138 0.720 0.380 0.010 0.012 0.022 CAR 0.014 0.088 0.150 0.131 0.703 0.478 0.030 0.026 0.052 LP-0.001 0.051 0.094 0.171 0.137 0.709 0.421 0.017 0.011 0.025 LP-0.01 0.051 0.123 0.198 0.136 0.701 0.455 0.052 0.012 0.034 Katz-0.001 0.053 0.093 0.171 0.137 0.709 0.422 0.017 0.011 0.025 Katz-0.01 0.053 0.134 0.202 0.136 0.696 0.454 0.071 0.012 0.037 ACT 0.000 0.000 0.126 0.000 0.000 0.000 0.000 0.000 0.000 Cos+ 0.000 0.039 0.000 0.081 0.620 0.333 0.017 0.008 0.011 QN-2.5 0.075 0.397 0.415 0.156 0.734 0.460 0.251 0.192 0.114 QN-max 0.078 0.651 0.547 0.251 0.927 0.580 0.906 0.210 0.165
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