异质信息网络是一种信息网络,包含了节点和边,并且该节点和边具有一种或多种类型,异质信息网络包含了更更丰富的语义信息。

信息网络被定义为一个有向网络图G=(V,E),其中,V是所有实体结点的集合,E是所有关系边的集合。并且存在着一个结点类型的映射函数φ:V→A和一个边类型的映射函数Ψ:E→R,对于每个对象v∈V属于一种特殊的对象类型φ(v)∈A,每个链接e∈E属于一种特殊的关系类型Ψ(e)∈R,那么这种网络类型就是信息网络。当对象类型的种类|A|>1或者关系类型的种类|R|>1时,这种信息网络是异质信息网络,否则,它是一种同质信息网络

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异质信息网络的概念自 2009 年首次提出以来,迅速成为数据挖掘领域的研究热点,并在这类网络上开发了许多创新性的数据挖掘任务。此外,还开发了一些独特的分析技术来展示异质信息网络的好处。特别是,随着大数据时代的到来,异质信息网络为大数据中复杂对象及其关系的建模和分析提供了一种有效的途径。

本文将介绍两篇滴滴在KDD 2020上发表的利用异质信息网络解决实际问题的工作。 第一个工作是《HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival》,提出异质时空图卷积网络用于预估到达时间。

第二个工作是《Gemini: A Novel and Universal Heterogeneous Graph Information Fusing Framework for Online Recommendations》,提出一种通用的在线推荐异质信息融合框架。

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Recently, a special kind of graph, i.e., supernet, which allows two nodes connected by multi-choice edges, has exhibited its power in neural architecture search (NAS) by searching for better architectures for computer vision (CV) and natural language processing (NLP) tasks. In this paper, we discover that the design of such discrete architectures also appears in many other important learning tasks, e.g., logical chain inference in knowledge graphs (KGs) and meta-path discovery in heterogeneous information networks (HINs). Thus, we are motivated to generalize the supernet search problem on a broader horizon. However, none of the existing works are effective since the supernet topology is highly task-dependent and diverse. To address this issue, we propose to tensorize the supernet, i.e., unify the subgraph search problems by a tensor formulation and encode the topology inside the supernet by a tensor network. We further propose an efficient algorithm that admits both stochastic and deterministic objectives to solve the search problem. Finally, we perform extensive experiments on diverse learning tasks, i.e., architecture design for CV, logic inference for KG, and meta-path discovery for HIN. Empirical results demonstrate that our method leads to better performance and architectures.

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Recently, a special kind of graph, i.e., supernet, which allows two nodes connected by multi-choice edges, has exhibited its power in neural architecture search (NAS) by searching for better architectures for computer vision (CV) and natural language processing (NLP) tasks. In this paper, we discover that the design of such discrete architectures also appears in many other important learning tasks, e.g., logical chain inference in knowledge graphs (KGs) and meta-path discovery in heterogeneous information networks (HINs). Thus, we are motivated to generalize the supernet search problem on a broader horizon. However, none of the existing works are effective since the supernet topology is highly task-dependent and diverse. To address this issue, we propose to tensorize the supernet, i.e., unify the subgraph search problems by a tensor formulation and encode the topology inside the supernet by a tensor network. We further propose an efficient algorithm that admits both stochastic and deterministic objectives to solve the search problem. Finally, we perform extensive experiments on diverse learning tasks, i.e., architecture design for CV, logic inference for KG, and meta-path discovery for HIN. Empirical results demonstrate that our method leads to better performance and architectures.

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