通过图元预测子揭示链路预测中的多阶流行度与相似性机制
Uncovering multi-order Popularity and Similarity Mechanisms in Link Prediction by graphlet predictors
AI总结：
本研究利用图元轨道度构建多阶链路预测子，设计融合多特征的监督学习模型，在550个跨领域网络中验证其性能，并揭示不同领域网络中流行度与相似性机制的主导作用差异，提升了链路预测的准确性与可解释性。
摘要：
链路预测已成为网络科学中的关键问题，因而吸引了越来越多的研究兴趣。流行度与相似性是真实网络形成过程中的两种核心机制。然而，在不同领域的网络中，流行度与相似性机制在链路预测中的作用仍未得到充分理解。为此，本研究利用图元（graphlet）的轨道度构建了基于多阶流行度与相似性的网络链路预测子，证明传统的基于流行度与相似性的指标可以通过轨道度高效表示。此外，我们设计了一个融合多种基于轨道度特征的监督学习模型，并验证了其链路预测性能。我们还在来自6个领域的550个真实网络中，评估了该模型内每个特征的平均绝对Shapley加性解释值。我们观察到，作为一种基于相似性的特征，同质性机制在社交网络中占主导地位，其胜率为91%。此外，另一种基于相似性的特征在经济、技术和信息网络中表现突出。最后，在生物和交通网络中没有单一特征占主导地位。所提出的方法提升了链路预测的准确性和可解释性，从而有助于复杂网络分析。
英文摘要：
Link prediction has become a critical problem in network science and has thus attracted increasing research interest. Popularity and similarity are two primary mechanisms in the formation of real networks. However, the roles of popularity and similarity mechanisms in link prediction across various domain networks remain poorly understood. Accordingly, this study used orbit degrees of graphlets to construct multi-order popularity- and similarity-based network link predictors, demonstrating that traditional popularity- and similarity-based indices can be efficiently represented in terms of orbit degrees. Moreover, we designed a supervised learning model that fuses multiple orbit-degree-based features and validated its link prediction performance. We also evaluated the mean absolute Shapley additive explanations of each feature within this model across 550 real-world networks from six domains. We observed that the homophily mechanism, which is a similarity-based feature, dominated social networks, with its win rate being 91\%. Moreover, a different similarity-based feature was prominent in economic, technological, and information networks. Finally, no single feature dominated the biological and transportation networks. The proposed approach improves the accuracy and interpretability of link prediction, thus facilitating the analysis of complex networks.
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