Data Mining in Heterogeneous Information Networks with Attributes

具有属性的异构信息网络中的数据挖掘

• 批准号: RGPIN-2017-04072
• 负责人: Ester, Martin
• 金额: $ 3.06万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750555
• 关键词: Data; Mining; Heterogeneous; Information; Networks

Most research in the area of data mining has concentrated on attribute data, i.e. on data where entities are represented by a set or vector of attributes. In the last decade, more and more network data has become available, which has driven the development of data mining methods for network data. However, in many scenarios, data can be represented as networks with node and/or edge attributes, with complex interactions between the topology of the network and the attributes. Nevertheless, data mining methods for attributed networks have received relatively little attention in the literature. Another limitation of existing methods is their focus on homogeneous networks, i.e. networks with a single type of objects and links, while real-life network data is often heterogeneous, consisting of multiple object and link types. Many systems can be modeled as heterogeneous information networks, for instance social networks among authors, papers and conferences, or biological networks integrating protein-protein interactions and gene regulations. Heterogeneous information networks with object and/or link attributes are characterized by complex interplays between the topology and the attributes of the network. In a social network, for instance, actors tend to connect to actors with similar attributes, while friends tend to become more similar to each other in the course of time. The long-term objective of our research is to explore data mining methods that can model and analyze the interplay of the topology and the attributes of AHINs. The proposed research program will address the limitations of the state-of-the-art and investigate several fundamental issues of data mining methods that can model and analyze the interplay of the topology and the attributes in heterogeneous information networks. The methods to be developed will have significant applications in many domains, and we will consider the analysis of social networks and biological networks as driving applications. For social network analysis, we will apply the proposed methods to the task of recommendation in social networks. For biological network analysis, we will apply our methods to the problem of network-based patient stratification and to the problem of detecting genetic causes of adverse drug reactions in gene and disease networks associated with patient records.

数据挖掘领域的大多数研究都集中在属性数据上，即在实体由属性集或属性向量表示的数据上。在过去的十年中，越来越多的网络数据变得可用，这推动了网络数据的数据挖掘方法的发展。然而，在许多情况下，数据可以表示为具有节点和/或边属性的网络，在网络的拓扑和属性之间具有复杂的交互。然而，属性网络的数据挖掘方法在文献中得到的关注相对较少。现有方法的另一个局限性是它们关注同构网络，即具有单一类型的对象和链接的网络，而现实生活中的网络数据通常是异构的，由多个对象和链接类型组成。许多系统可以被建模为异构信息网络，例如作者、论文和会议之间的社交网络，或者整合蛋白质-蛋白质相互作用和基因调控的生物网络。具有对象和/或链路属性的异构信息网络的特征在于网络的拓扑和属性之间的复杂相互作用。例如，在社交网络中，参与者倾向于与具有相似属性的参与者建立联系，而朋友往往随着时间的推移变得更加相似。我们的研究的长期目标是探索数据挖掘方法，可以建模和分析的拓扑结构和属性的AHIN的相互作用。拟议的研究计划将解决国家的最先进的局限性，并调查数据挖掘方法，可以建模和分析的拓扑结构和异构信息网络中的属性的相互作用的几个基本问题。待开发的方法将在许多领域中具有重要的应用，并且我们将考虑将社交网络和生物网络的分析作为驱动应用。对于社会网络分析，我们将应用所提出的方法在社交网络的推荐任务。对于生物网络分析，我们将把我们的方法应用于基于网络的患者分层问题，以及在与患者记录相关的基因和疾病网络中检测药物不良反应的遗传原因的问题。