A New Link Prediction Method for Complex Networks Based onTopological Effectiveness of Resource Transmission Paths
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摘要:
链路预测旨在利用网络中已有的拓扑结构或其他信息，预测未连边节点间存在连接的可能性。资源分配指标具有较低复杂度的同时取得了较好的预测效果，但在资源传输过程的描述中缺少对路径有效性的刻画。资源传输过程是网络演化连边产生的重要内在动力，通过分析节点间资源传输路径周围拓扑的有效性，该文提出一种基于资源传输路径有效性的链路预测方法。该方法首先分析了节点间潜在的资源传输路径对资源传输量的影响，提出资源传输路径有效性的量化方法。然后，基于资源传输路径的有效性，通过对双向资源传输量进行刻画，提出了节点间传输路径的有效性指标。在12个实际网络数据集上的实验测试表明，相比其他基于相似性的链路预测方法，该方法在AUC和Precision衡量标准下能够取得更好的效果。
Abstract:Link prediction considers to discover the unknown or missing links of complex networks by using the existing topology or other information. Resource Allocation index can achieve a good performance with low complexity. However, it ignores the path effectiveness of resource transmission process. The resource transmission process is an important internal driving force for the evolution of the network. By analyzing the effectiveness of the topology around the resource transmission path between nodes, a link prediction method based on topological effectiveness of resource transmission paths is proposed. Firstly, the influence of potential resource transmission paths between nodes on resource transmission is analyzed, and a quantitative method for resource transmission path effectiveness is proposed. Then, based on the effectiveness of the resource transmission path, after studying the two-way resource transmission amount between two nodes, the transmission path effectiveness index is proposed. The experimental results of 12 real networks show that compared with other link prediction methods, the proposed method can achieve higher prediction accuracy under the AUC and Precision metrics.
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Key words:
- Complex network /
- Link prediction /
- Resource transmission path /
- Effectiveness
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表 1 网络数据特征参数
网络 AIDS FWEW HS Figeys UC Metbolic 节点数 146 69 1858 2239 1899 453 边数 180 880 12534 6432 13838 2025 集聚系数 2.47 25.51 13.49 5.76 14.57 8.94 平均度 3.42 1.64 3.39 3.98 3.06 2.66 平均路径 –0.725 –0.298 –0.085 –0.331 –0.188 –0.226 匹配系数 0.052 0.552 0.0904 0.04 0.109 0.647 
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表 2 AUC结果对比分析
方法 AIDS FWEW HS Figeys UC Metbolic CN 0.599 0.684 0.812 0.566 0.781 0.921 RA 0.609 0.702 0.816 0.570 0.787 0.959 AA 0.609 0.695 0.815 0.569 0.787 0.955 CAR 0.599 0.685 0.812 0.567 0.783 0.920 LP(a) 0.836 0.702 0.933 0.888 0.893 0.920 LP(b) 0.833 0.728 0.940 0.903 0.903 0.921 Katz(a) 0.854 0.704 0.933 0.887 0.893 0.920 Katz(b) 0.852 0.734 0.937 0.898 0.903 0.920 ACT 0.954 0.779 0.868 0.917 0.896 0.767 Cos+ 0.591 0.510 0.960 0.844 0.869 0.904 本文方法 0.961 0.827 0.971 0.952 0.929 0.964 (a)可调参数$\alpha {\rm{ = 0}}{\rm{.001}}$ (b)可调参数$\alpha {\rm{ = 0}}{\rm{.01}}$
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表 3 Pre结果对比
方法 AIDS FWEW HS Figeys UC Metbolic CN 0.019 0.143 0.017 0.011 0.034 0.202 RA 0.028 0.165 0.008 0.012 0.026 0.319 AA 0.028 0.152 0.012 0.012 0.033 0.252 CAR 0.019 0.137 0.033 0.025 0.064 0.193 LP(a) 0.055 0.153 0.021 0.011 0.034 0.202 LP(b) 0.055 0.180 0.055 0.012 0.053 0.200 Katz(a) 0.055 0.153 0.021 0.010 0.034 0.202 Katz(b) 0.055 0.183 0.071 0.011 0.054 0.198 ACT 0.000 0.128 0.000 0.000 0.000 0.000 Cos+ 0.000 0.000 0.015 0.005 0.010 0.097 本文方法 0.068 0.344 0.107 0.130 0.093 0.374 (a)可调参数$\alpha {\rm{ = 0}}{\rm{.001}}$ (b)可调参数$\alpha {\rm{ = 0}}{\rm{.01}}$
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