arXiv 2402.051542024-02-07cs.SIcs.AI

自适应超图网络用于信任预测

Adaptive Hypergraph Network for Trust Prediction

School of Computing, Macquarie University（麦考瑞大学计算学院）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）

Rongwei Xu, Guanfeng Liu, Yan Wang, Xuyun Zhang, Kai Zheng, Xiaofang Zhou

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AI总结：

本文提出自适应超图网络AHNTP，利用高阶相关性和自适应GCN及对比学习，提升信任预测准确性，优于现有方法。

摘要：

信任在个体决策中扮演着至关重要的角色。传统的信任预测模型依赖于成对相关性来推断用户之间的潜在关系。然而，在现实世界中，用户之间的交互通常复杂，不仅仅是成对关系。超图提供了一种灵活的方式来建模这些复杂的高阶相关性（不仅仅是成对连接），因为超图可以利用超边连接两个以上的节点。然而，大多数基于超图的方法是通用的，不能很好地应用于信任预测任务。在本文中，我们提出了一种用于信任预测的自适应超图网络（AHNTP），这是一种通过利用高阶相关性来提高信任预测准确性的新方法。AHNTP利用基于Motif的PageRank来捕获高阶社会影响力信息。此外，它从节点级和结构级属性构建超组，以整合复杂的相关性信息。此外，AHNTP利用自适应超图图卷积网络（GCN）层和多层感知机（MLPs）来生成全面的用户嵌入，促进信任关系预测。为了增强模型的泛化性和鲁棒性，我们引入了一种新颖的监督对比学习损失用于优化。大量实验证明了我们的模型在信任预测准确性方面优于最先进的方法。本工作的源代码可通过https://github.com/Sherry-XU1995/AHNTP访问。

英文摘要：

Trust plays an essential role in an individual's decision-making. Traditional trust prediction models rely on pairwise correlations to infer potential relationships between users. However, in the real world, interactions between users are usually complicated rather than pairwise only. Hypergraphs offer a flexible approach to modeling these complex high-order correlations (not just pairwise connections), since hypergraphs can leverage hyperedeges to link more than two nodes. However, most hypergraph-based methods are generic and cannot be well applied to the trust prediction task. In this paper, we propose an Adaptive Hypergraph Network for Trust Prediction (AHNTP), a novel approach that improves trust prediction accuracy by using higher-order correlations. AHNTP utilizes Motif-based PageRank to capture high-order social influence information. In addition, it constructs hypergroups from both node-level and structure-level attributes to incorporate complex correlation information. Furthermore, AHNTP leverages adaptive hypergraph Graph Convolutional Network (GCN) layers and multilayer perceptrons (MLPs) to generate comprehensive user embeddings, facilitating trust relationship prediction. To enhance model generalization and robustness, we introduce a novel supervised contrastive learning loss for optimization. Extensive experiments demonstrate the superiority of our model over the state-of-the-art approaches in terms of trust prediction accuracy. The source code of this work can be accessed via https://github.com/Sherry-XU1995/AHNTP.

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。