Multidimensional Heterogeneous Information Network Analysis and Mining

今天的实际系统大多是由异质实体组成的，这些实体通过多种连接渠道相互作用。例如，在社交网络中，不同类型的参与者通过不同的在线平台在不同的语境中互动。这些相互作用不断变化，可能是唯一的，也可能是多种的，相似的或不同的。为了更好地表示这种复杂的系统，出现了一类新的多维异构信息网络。这些网络既包含不同类型节点之间的多条边，也包含相同类型节点之间的多条边。因此，多维异构网络为大多数互连系统和许多实际场景提供了通用和灵活的建模。多维异构网络是理解其结构特性以及预测其多尺度和多组分动态过程的重大科学挑战的来源。我提出的研究计划将处理挖掘这种复杂网络的算法方法的稀缺性，并将处理产生新的信息网络分析范式的原始和具有挑战性的问题。我的长期目标是设计理论和算法来挖掘大量随时间变化的多维异构信息网络。我的目标是创建有效的算法，考虑到多类型实体之间的多维交互、生成的内容、网络的动态性质和数据源的异构性。作为驱动型应用，我和我的研究团队将考虑对大规模动态社交网络的分析，以便更好地理解人与网络的交互，并对在线社交网络中的各种角色和分组进行语义解释和洞察。此外，我们将分析学校提供的大量匿名学术数据，以提取有意义的模式，发现各种分组，跟踪学生的轨迹，预测可操作的见解，然后利用这些信息制定有效的学生保留和成功计划。这个程序处理缺乏有效的方法来挖掘复杂的语义丰富的多维异构信息网络。因此，这项研究计划的成果可能是重要的，因为它们将为数据分析师提供创新工具，以深入研究包括多个和异构连接层的复杂系统。此外，该研究项目为HQP培训提供了一个独特的机会。参与这项研究的学生将致力于具有挑战性的问题和具体的应用，这将使他们在出版工作中具有显着的优势，并为他们提供专业知识，这将有助于满足学术界和工业界的需求。

Today's real systems are mostly made of heterogeneous entities that interact with each other through multiple channels of connectivity. For instance, in social networks, different types of actors interact on different contexts via different online platforms. These interactions continually change and could be unique or multiple, similar or different. To better represent such complex systems, a new class of multidimensional heterogeneous information networks has emerged. These networks contain multiple edges between nodes of different types as well as nodes of the same type. Multidimensional heterogeneous networks provide thus general and flexible modeling of most interconnected systems and many practical scenarios. Multidimensional heterogeneous networks are the sources of big scientific challenges for understanding their structural properties as well as their dynamic processes for predicting their multiscale and multicomponent dynamics. The research program that I propose will deal with the scarcity of algorithmic methods for mining such complex networks and will handle original and challenging problems that give rise to novel information network analysis paradigms. My long-term goal is to devise theories and algorithms for mining massive time-evolving multidimensional heterogeneous information networks. My aim is the creation of efficient algorithms that take into account multidimensional interactions between multi-typed entities, the generated content, the dynamic nature of the network, and the heterogeneity of the data sources. As driven applications, my research team and I will consider the analysis of large-scale dynamic social networks in order to provide better understanding of human-Web interactions and give semantic interpretation and insight into the various roles and groupings in online social networks. Furthermore, we will analyze massive anonymized academic data provided by our university to extract meaningful patterns, discover various groupings, track students' trajectories over time, predict actionable insights, and then utilize this information to elaborate an effective students' retention and success plan. This program deals with the lack of effective approaches for mining complex semantically-rich multidimensional heterogeneous information networks. The outcomes of this research program can thus be important as they will equip data analysts with innovative tools to delve deeply complex systems that include multiple and heterogeneous layers of connectivity. Furthermore, this research program represents a unique opportunity for the training of HQP. The students involved in this research will work on challenging problems and concrete applications which will give them a notable advantage in their publishing efforts and also give them expertise that will be useful to meet the needs of academia and the industry as well.