网络拓扑变化对信息源定位的影响
Impact of network topology changes on information source localization
摘要：
在复杂网络中定位信息源的成熟方法通常是在假设网络拓扑结构完全准确的情况下推导出来的。我们研究了三个这样的算法（LPTVA，GMLA和皮尔逊相关算法）的性能在不满足这一假设的情况下，通过修改网络之前本地化。这是通过添加多余的新链接，隐藏现有的链接，或根据网络的结构哈密尔顿重新连接链接来完成的。我们发现，GMLA是高度弹性的多余的边缘，因为它的精度下降超过统计不确定性，只有当链接的数量大约是一倍。另一方面，如果边缘集被低估或重新附着已经发生，GMLA的性能显着下降。在这种情况下，皮尔逊算法是优选的，当其他模拟参数有利于定位（高密度的观察者，高度确定性的传播）时，保留其大部分性能。它通常也比LPTVA更准确，并且快几个数量级。定位算法之间的上述差异可以直观地解释，但需要进一步的理论研究。
英文摘要：
Well-established methods of locating the source of information in a complex network are usually derived with the assumption of complete and exact knowledge of network topology. We study the performance of three such algorithms (LPTVA, GMLA and Pearson correlation algorithm) in scenarios that do not fulfill this assumption by modifying the network prior to localization. This is done by adding superfluous new links, hiding existing ones, or reattaching links in accordance with the network's structural Hamiltonian. We find that GMLA is highly resilient to the addition of superfluous edges, as its precision falls by more than statistical uncertainty only when the number of links is approximately doubled. On the other hand, if the edge set is underestimated or reattachment has taken place, the performance of GMLA drops significantly. In such a scenario the Pearson algorithm is preferable, retaining most of its performance when other simulation parameters favor localization (high density of observers, highly deterministic propagation). It is also generally more accurate than LPTVA, as well as orders of magnitude faster. The aforementioned differences between localization algorithms can be intuitively explained, although a need for further theoretical research is noted.
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