Self-Driven Graph Volterra Models for Higher-Order Link Prediction
Self-Driven Graph Volterra Models for Higher-Order Link Prediction
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DOI：
10.1109/icassp40776.2020.9053655
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
M. Coutiño;G. V. Karanikolas;G. Leus;G. Giannakis
中科院分区：
文献类型：
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作者：
M. Coutiño;G. V. Karanikolas;G. Leus;G. Giannakis
链路预测是网络与数据科学中的核心问题之一，有着广泛的应用。虽然预测网络数据中的成对节点相互作用（链接）已经得到了广泛的研究，但预测高阶相互作用（高阶链接）仍然没有完全理解。已经提出了几种方法来预测这种高阶相互作用，但到目前为止还没有提出任何原则性的方法来应对这一挑战。交叉借鉴沃尔泰拉级数模型和线性结构方程模型的思想，本文提出了自驱动图沃尔泰拉模型，该模型能够捕捉网络数据中节点观测量之间的高阶相互作用。使用来自社交网络的真实的交互数据对新模型进行了高阶链接预测任务的验证。
Link prediction is one of the core problems in network and data science with widespread applications. While predicting pairwise nodal interactions (links) in network data has been investigated extensively, predicting higher-order interactions (higher-order links) is still not fully understood. Several approaches have been advocated to predict such higher-order interactions, but no principled method has been put forth to tackle this challenge so far. Cross-fertilizing ideas from Volterra series and linear structural equation models, the present paper introduces self-driven graph Volterra models that can capture higher-order interactions among nodal observables available in networked data. The novel model is validated for the higher-order link prediction task using real interaction data from social networks.