20221015Finding key players in complex networks through deep reinforcement learning

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已于 2022-10-21 08:44:15 修改 · 929 阅读

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于 2022-10-15 10:55:45 首次发布

提出一种结合图神经网络(GNN)与强化学习(RL)的方法FINDER，用于高效识别复杂网络中的关键节点。该方法通过在小型合成图上训练，即可有效推广至更大规模的真实网络，展现出优异的性能和成本效益。

小部分节点key players在复杂系统/网络中发挥重要作用
Finding an optimal set of key players in complex networks has been a long-standing problem in network science, with many real-world applications
Finding an optimal set of key players in general graphs that optimizes nontrivial and hereditary connectivity measures is typically NP-hard.NP-hard带来的解决方案不通用、无法扩展的问题。

训练图与测试图的生成：Three classic network models, the Erdős–Rényi (ER) model34 , the Watts–Strogatz (WS) 35 model and the Barabási–Albert (BA) model36 , were used to generate both training and test graphs.
训练图与测试图特征：In all cases, FINDER is trained on BA graphs of 30–50 nodes. We then evaluate the well-trained FINDER on synthetic BA graphs of different scales: 30–50, 50–100, 100–200, 200–300, 300–400 and 400–500 nodes. For each scale, we randomly generated 100 instances, and reported the average results over them. To obtain node-weighted graphs, we assign each node a normalized weight, which is proportional to its degree (degree-weighted) or a random non-negative number (random-weighted).
关于度量图的指标：pairwise connectivity，the size of the GCC（GCC-giant connected component 极大连通分量）
最终target与指标：minimizes the following accumulated normalized connectivity (ANC)