Data Mining in Heterogeneous Information Networks with Attributes

数据挖掘领域的大多数研究都集中在属性数据上，即那些实体由属性集或属性向量表示的数据。近十年来，越来越多的网络数据可供使用，这推动了网络数据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/或边缘属性的网络，网络拓扑和属性之间存在复杂的交互。然而，属性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质网络，即具有单一类型对象和链路的网络，而实际网络数据通常是异构的，由多个对象和链路类型组成。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会网络，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点是拓扑结构和网络属性之间存在复杂的相互作用。例如，在社交网络中，演员倾向于与具有相似属性的演员建立联系，而朋友往往随着时间的推移变得越来越相似。

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.