Modelling and Mining Complex Networks

在大数据时代，数据被认为是新的化石燃料。每一次人与技术的交互，或传感器网络，都会产生新的数据点，这些数据点可以根据交互的类型被视为一个自组织网络。在这些网络（例如Facebook）中，节点不仅包含一些有用的信息（例如用户的个人资料，照片，标签），而且还内部连接到其他节点（基于友谊的关系，类似的用户行为，年龄，地理位置）。这种网络是大规模的，自组织的，分散的，并随着时间的推移动态演变。因此，随机几何图形是自然的，非常适合建模。理解驱动复杂网络的组织和行为的原理，以及基于这些网络的算法，对于信息和社会科学、经济学、生物学和神经科学等广泛领域至关重要。 PI的研究计划通过专注于复杂网络的建模和挖掘（纯研究以及与行业合作伙伴的应用研究），有助于我们的理解。该计划是多学科的性质，因此需要从至少三个领域的知识，技能和工具的相当独特的混合：1）数学和理论计算机科学，2）社会科学，3）应用计算机科学。所有这三个领域日益相互关联，要在实地产生重要影响，就需要在所有这些领域取得经验。

In the big data era, data is considered as the new fossil fuel. Every human-technology interaction, or sensor network, generates new data points that can be viewed, based on the type of interaction, as a self-organizing network. In these networks (for example, Facebook) nodes not only contain some useful information (such as user's profile, photos, tags) but are also internally connected to other nodes (relations based on friendship, similar users behaviour, age, geographic location). Such networks are large-scale, self-organizing, decentralized, and evolve dynamically over time. As a result, random geometric graphs turn out to be natural and well suited in modelling them. Understanding the principles driving the organization and behaviour of complex networks, as well as algorithms based on these networks, is crucial for a broad range of fields, including information and social sciences, economics, biology, and neuroscience. The research program of the PI contributes to our understanding by focusing on modelling and mining of complex networks (pure research as well as applied one with industry partners). The program is multidisciplinary in nature and as such requires a rather unique blend of knowledge, skills, and tools from at least three areas: 1) mathematics and theoretical computer science, 2) social science, and 3) applied computer science. Increasingly, all three areas are interconnected, and experience in all of them is required in order to make an important impact in the field.