加权多层网络（即w-MLN）的度中心性算法
Degree Centrality Algorithms for Weighted Multilayer Networks (or w-MLNs)
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中文总结 AI 辅助
针对现有多层网络分析会丢失结构与语义信息的问题，该研究提出基于解耦框架的加权同构多层网络度中心性启发式算法，在保证准确性的同时大幅提升计算效率，验证了算法的可扩展性与有效性。
中文摘要 AI 辅助
中心性度量是为简单图定义的，包括有向、无向、加权或无权图。属性图必须简化为简单图才能计算中心性度量。然而，当使用多层网络（MLN）对具有多种关系类型的应用进行建模时，简单图算法无法直接使用。现有方法通常通过将MLN的层聚合为单个图来分析MLN，这会导致结构和语义信息的丢失，在加权网络中，语义信息的丢失可能更为明显。本研究聚焦于使用基于解耦的框架计算加权同构多层网络（HoMLN）中的度中心性，该框架对MLN执行独立的分层分析，无需将其简化为简单图。这种解耦方法允许在每一层使用现有算法，并利用各层的最少信息来计算HoMLN的度中心性。我们提出了基于启发式的算法，在准确性和效率之间取得平衡。将所提方法与使用布尔或聚合得到的真值（GT）结果及朴素基线进行评估。在合成和真实HoMLN数据集上的实验结果表明，这些启发式算法达到了与真值相当的准确性，同时显著提高了计算效率，从而验证了使用解耦方法开发的HoMLN算法的可扩展性和有效性。
英文摘要
Centrality measures are defined for simple graphs -- directed, undirected, weighted or unweighted. Attributed graphs have to be reduced to simple graphs for computing centrality measures. However, when applications with multiple types of relationships are modeled using multilayer networks (MLNs), simple graph algorithms cannot be directly used. Existing approaches typically analyze MLNs by aggregating layers of an MLN into a single graph, which results in the loss of structural and semantic information. The semantic information loss can be more pronounced particularly, in weighted networks. This work focuses on computing degree centrality in weighted homogeneous multilayer networks (HoMLNs) using a decoupling-based framework. The framework performs independent layer-wise analysis on MLNs without reducing them to simple graphs. The decoupling approach allows use of exiting algorithms for each layer and uses minimal information from individual layers for computing degree centrality of HoMLNs. We propose heuristic-based algorithms that strike a balance between accuracy and efficiency. The proposed methods are evaluated against ground truth (GT) results obtained using Boolean OR aggregation and naive baselines. Experimental results on both synthetic and real-world HoMLN datasets demonstrate that the heuristics achieve accuracy comparable to the ground truth while significantly improving computational efficiency, thereby establishing the scalability and effectiveness of the HoMLN algorithms developed using the decoupling approach.
