不确定图中中心性度量的高效近似
Efficient Approximation of Centrality Measures in Uncertain Graphs
AI总结：
本文提出一种基于可能最短路径的启发式算法，用于在不确定图上高效近似计算调和接近中心性与介数中心性，并通过大规模实验验证其运行时间与准确性的可扩展性。
摘要：
本文提出了一种在不确定图（即边仅以一定概率存在的图）上启发式计算不同距离度量的算法，并将其应用于调和接近中心性的启发式计算。该方法主要基于 Potamias 等人关于距离度量计算的研究，以及 Chenxu Wang 和 Ziyuan Lin 提出的介数中心性启发式算法。我通过引入可能最短路径的概念并将其应用于上述距离，扩展了他们的研究。据我所知，这种算法方法此前从未被研究过。我将比较调和接近中心性的启发式结果与 Monte Carlo 方法在运行时间和准确性上的表现。同样，我将对 Chenxu Wang 和 Ziyuan Lin 提出的介数中心性启发式算法进行新实验，以测试其在更多样实例上的有效性。最后，我将在大规模图上测试这两种算法，以评估其运行时间的可扩展性。
英文摘要：
In this thesis I propose an algorithm to heuristically calculate different distance measures on uncertain graphs (i.e. graphs where edges only exist with a certain probability) and apply this to the heuristic calculation of harmonic closeness centrality. This approach is mainly based on previous work on the calculation of distance measures by Potamias et al. and on a heuristic algorithm for betweenness centrality by Chenxu Wang and Ziyuan Lin. I extend on their research by using the concept of possible shortest paths, applying them to the afformentioned distances. To the best of my knowledge, this algorithmic approach has never been studied before. I will compare my heuristic results for harmonic closeness against the Monte Carlo method both in runtime and accuracy. Similarly, I will conduct new experiments on the betweenness centrality heuristic proposed y Chenxu Wang and Ziyuan Lin to test its efficacy on a bigger variety of instances. Finally, I will test both of these algorithms on large scale graphs to evaluate the scalability of their runtime.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
