研究组合数据的拓扑与连接特性，提升学习效率与算法性能。
Computing and Learning on Combinatorial Data
- 通过分析数据间的拓扑与连接结构，挖掘组合数据特征。
- 在社交网络、分子等组合数据上实现更高效的学习与计算。
- 适合对图神经网络、数据结构优化感兴趣的读者。
二十一世纪是数据驱动的时代，人类活动、物理现象、科技进展等产生海量数据，其连接性是关键属性。例如万维网中网页通过超链接形成有向连接。组合数据指基于特定连接规则组合的数据项，包括社交网络、网格、社区簇、集合系统及分子等。本博士论文聚焦于组合数据的学习与计算，研究数据内部及跨数据的拓扑与连接特性，旨在提升学习性能并实现高算法效率。
原文摘要 · Abstract (English)
The twenty-first century is a data-driven era where human activities and behavior, physical phenomena, scientific discoveries, technology advancements, and almost everything that happens in the world resulting in massive generation, collection, and utilization of data. Connectivity in data is a crucial property. A straightforward example is the World Wide Web, where every webpage is connected to other web pages through hyperlinks, providing a form of directed connectivity. Combinatorial data refers to combinations of data items based on certain connectivity rules. Other forms of combinatorial data include social networks, meshes, community clusters, set systems, and molecules. This Ph.D. dissertation focuses on learning and computing with combinatorial data. We study and examine topological and connectivity features within and across connected data to improve the performance of learning and achieve high algorithmic efficiency.
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