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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634788
• 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.

数据挖掘领域的大多数研究都集中在属性数据上，即实体由属性集或向量表示的数据。近十年来，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，在许多场景中，数据可以表示为具有节点和/或边缘属性的网络，网络拓扑和属性之间存在复杂的交互。然而，归因网络的数据挖掘方法在文献中受到的关注相对较少。现有方法的另一个限制是它们关注同构网络，即具有单一类型对象和链接的网络，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复杂的相互作用。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，而随着时间的推移，朋友往往会变得更加相似。