Among diverse topics in complex network analysis, the idea of extracting a small set of nodes which can maximally influence other nodes in the network has a variety of applications, especially for e-marketing and social networking. While there is an abundance of heuristics to identify such influential nodes, the method of quantifying the influence itself, has not been investigated in the research community. Most of the classical and state-of-the-art works use Diffusion tests for influence benchmark of a particular set of nodes in the network. The underlying study challenges this method and conducts thorough experiments to show that for real-world applications, the diffusion test alone is not only insufficient, but in some cases is also an inaccurate method of benchmarking. Using eight widely adopted heuristics, 25 networks were tested using Diffusion tests and compared with resilience test, we found out that no single algorithm performs consistently on both types of tests. Thus, we conclude that a more accurate way of benchmarking a set of influential nodes is to run diffusion tests alongside resilience test, in order to label a certain technique as best performer.

在复杂网络分析的各种主题中，提取可以最大程度地影响网络中其他节点的一小组节点的想法具有多种应用，特别是对于电子营销和社交网络。虽然有大量的启发式方法可以识别此类有影响力的节点，但研究界尚未研究量化影响力本身的方法。大多数经典和最先进的作品都使用扩散测试作为网络中特定节点集的影响基准。基础研究挑战了这种方法并进行了彻底的实验，以表明对于现实世界的应用，仅靠扩散测试不仅不够，而且在某些情况下也是一种不准确的基准测试方法。使用八种广泛采用的启发式方法，25 个网络使用扩散测试进行了测试，并与弹性测试进行了比较，我们发现没有一种算法在两种类型的测试中表现一致。因此，我们得出结论，对一组有影响力的节点进行基准测试的更准确方法是在弹性测试的同时运行扩散测试，以便将某种技术标记为最佳执行者。