Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks
Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks
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DOI：
10.1145/3333028
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发表时间：
2017-09
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影响因子：
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通讯作者：
Sina Sajadmanesh;Jiawei Zhang;H. Rabiee
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文献类型：
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作者：
Sina Sajadmanesh;Jiawei Zhang;H. Rabiee
在线社交网络、万维网、媒体和技术网络以及其他类型的所谓信息网络如今无处不在。这些信息网络本质上是异构的和动态的。它们是异构的，因为它们由多种类型的对象和关系组成，并且它们是动态的，因为它们随着时间的推移不断发展。在这种异构和动态环境中，具有挑战性的问题之一是预测网络中未来将出现的那些关系。在本文中，我们尝试解决动态异构信息网络中的连续时间关系预测问题。这意味着考虑到通过考虑底层网络的异质性和时间动态而提取的特征，预测关系在未来出现所需的时间。为此，我们首先引入一种特征提取框架，该框架结合了基于元路径的建模和循环神经网络的强大功能，以有效地提取适合网络异质性和动态性关系预测的特征。接下来，我们提出了一种有监督的非参数方法，称为非参数广义线性模型（Np-Glm），它根据给定的特征推断关系建立时间的隐藏的潜在概率分布。然后，我们提出了一种训练 Np-Glm 的学习算法和一种回答与时间相关的查询的推理方法。对合成数据和三个真实数据集（即 Delicious、MovieLens 和 DBLP）进行的大量实验证明了 Np-Glm 在解决相对于竞争基线的连续时间关系预测问题方面的有效性。
Online social networks, World Wide Web, media, and technological networks, and other types of so-called information networks are ubiquitous nowadays. These information networks are inherently heterogeneous and dynamic. They are heterogeneous as they consist of multi-typed objects and relations, and they are dynamic as they are constantly evolving over time. One of the challenging issues in such heterogeneous and dynamic environments is to forecast those relationships in the network that will appear in the future. In this article, we try to solve the problem of continuous-time relationship prediction in dynamic and heterogeneous information networks. This implies predicting the time it takes for a relationship to appear in the future, given its features that have been extracted by considering both heterogeneity and temporal dynamics of the underlying network. To this end, we first introduce a feature extraction framework that combines the power of meta-path-based modeling and recurrent neural networks to effectively extract features suitable for relationship prediction regarding heterogeneity and dynamicity of the networks. Next, we propose a supervised non-parametric approach, called Non-Parametric Generalized Linear Model (Np-Glm), which infers the hidden underlying probability distribution of the relationship building time given its features. We then present a learning algorithm to train Np-Glm and an inference method to answer time-related queries. Extensive experiments conducted on synthetic data and three real-world datasets, namely Delicious, MovieLens, and DBLP, demonstrate the effectiveness of Np-Glm in solving continuous-time relationship prediction problem vis-à-vis competitive baselines.