Multidimensional Heterogeneous Information Network Analysis and Mining

今天的真实系统大多由不同的实体组成，这些实体通过多个连接渠道相互交互。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上下文中互动。这些相互作用不断变化，可以是唯一的，也可以是多个的，相似的，也可以不同的。为了更好地表示这样的复杂系统，一类新的多维异构信息网络应运而生。这些网络在不同类型的节点之间以及相同类型的节点之间包含多条边。因此，多维异质网络提供了对大多数互连系统和许多实际场景的通用和灵活的建模。 多维非均质网络是理解其结构特性以及预测其多尺度和多组分动力学过程的重大科学挑战的来源。我提出的研究计划将解决挖掘这种复杂网络的算法方法的稀缺性，并将处理导致新的信息网络分析范式的原创性和挑战性问题。我的长期目标是设计理论和算法来挖掘海量的、随时间演变的多维异质信息网络。我的目标是创建高效的算法，考虑多类型实体之间的多维交互、生成的内容、网络的动态性质以及数据源的异构性。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，我们将分析我们大学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，跟踪学生随着时间的推移的轨迹，预测可操作的见解，然后利用这些信息来制定有效的学生留住和成功计划。 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质信息网络。因此，这一研究计划的结果可能很重要，因为它们将为数据分析师配备创新工具，以深入研究包括多个不同连接层的深度复杂系统。此外，这项研究计划为HQP的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应用，这将使他们在出版工作中获得显着的优势，并为他们提供有助于满足学术界和行业需求的专业知识。

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.