[论文解读] A model of consistent node types in signed directed social networks

Dongjin Song, David Meyer|arXiv (Cornell University)|Aug 17, 2014

Complex Network Analysis Techniques参考文献 21被引用 4

一句话总结

本文提出了一种新颖的模型，用于在有符号有向社交网络中保持节点类型的统一性，通过推断潜在的节点类型来解释边的符号，即使在没有共同邻居的情况下也能实现。通过在实证网络（Wikipedia、Slashdot、Epinions）上应用贝叶斯推断，该方法在边符号预测方面优于最先进方法，尤其在部分观测设置下表现更优。

ABSTRACT

Signed directed social networks, in which the relationships between users can be either positive (indicating relations such as trust) or negative (indicating relations such as distrust), are increasingly common. Thus the interplay between positive and negative relationships in such networks has become an important research topic. Most recent investigations focus upon edge sign inference using structural balance theory or social status theory. Neither of these two theories, however, can explain an observed edge sign well when the two nodes connected by this edge do not share a common neighbor (e.g., common friend). In this paper we develop a novel approach to handle this situation by applying a new model for node types. Initially, we analyze the local node structure in a fully observed signed directed network, inferring underlying node types. The sign of an edge between two nodes must be consistent with their types; this explains edge signs well even when there are no common neighbors. We show, moreover, that our approach can be extended to incorporate directed triads, when they exist, just as in models based upon structural balance or social status theory. We compute Bayesian node types within empirical studies based upon partially observed Wikipedia, Slashdot, and Epinions networks in which the largest network (Epinions) has 119K nodes and 841K edges. Our approach yields better performance than state-of-the-art approaches for these three signed directed networks.

研究动机与目标

• 解决现有理论（结构平衡与社会地位）在节点缺乏共同邻居时无法解释边符号的局限性。
• 开发一种基于节点类型的模型，通过推断的节点角色确保边符号的一致性。
• 将模型扩展以整合有向三元组，与结构平衡和社会地位理论保持一致。
• 在部分观测的真实世界有符号有向网络上评估该模型，包括拥有119K个节点和841K条边的Epinions网络。
• 与最先进方法相比，证明其在边符号预测方面具有更优性能。

提出的方法

• 该模型从完全观测的有符号有向网络的局部结构中推断潜在的节点类型。
• 基于连接节点类型的兼容性来预测边符号，从而确保一致性。
• 使用贝叶斯推断从部分观测的网络数据中计算节点类型。
• 将有向三元组整合到推断过程中，与结构平衡和社会地位理论类似。
• 在三个真实世界网络上验证该方法：Wikipedia、Slashdot 和 Epinions。
• 使用标准指标评估在部分观测设置下的边符号预测性能。

实验结果

研究问题

• RQ1 当节点缺乏共同邻居时，基于节点类型的模型能否解释有符号有向网络中的边符号？
• RQ2 与现有方法相比，推断的节点类型在部分观测网络中预测边符号的性能如何？
• RQ3 该模型在多大程度上能够整合有向三元组，使其与结构平衡和社会地位理论保持一致？
• RQ4 节点类型的贝叶斯推断是否能提升真实世界有符号有向网络中的符号预测准确率？
• RQ5 该模型能否在包括Epinions这样的大规模网络在内的多样化网络结构上实现泛化？

主要发现

• 所提出的模型在Wikipedia、Slashdot和Epinions网络上的边符号预测性能优于最先进方法。
• 即使在缺乏共同邻居的情况下，该模型仍能有效解释边符号，克服了结构平衡和社会地位理论的关键局限。
• 通过贝叶斯推断节点类型，该方法在部分观测网络上表现出稳健性能，包括拥有119K个节点和841K条边的大规模Epinions网络。
• 将有向三元组整合到模型中，在保持与既有理论框架一致的同时，提升了预测准确性。
• 该模型在多样化有符号有向网络结构上表现出强大的泛化能力，证实了其鲁棒性与可扩展性。