Learning with attributed networksalgorithms and applications
My dissertation research aims to develop a suite of novel learning algorithms to understand, characterize, and gain actionable insights from attributed networks, to benefit high-impact real-world applications. In the first part of this dissertation, I mainly focus on developing learning algorithms for attributed networks in a static environment at two different levels: (i) attribute level - by designing feature selection algorithms to find high-quality features that are tightly correlated with the network topology; and (ii) node level - by presenting network embedding algorithms to learn discriminative node embeddings by preserving node proximity w.r.t. network topology structure and node attribute similarity. As changes are essential components of attributed networks and the results of learning algorithms will become stale over time, in the second part of this dissertation, I propose a family of online algorithms for attributed networks in a dynamic environment to continuously update the learning results on the fly. In fact, developing application-aware learning algorithms is more desired with a clear understanding of the application domains and their unique intents. As such, in the third part of this dissertation, I am also committed to advancing real-world applications on attributed networks by incorporating the objectives of external tasks into the learning process.]]>autLi, JundongthsLiu, HuandgcFaloutsos, ChristosdgcHe, JingruidgcXue, GuoliangpblArizona State UniversityengPartial requirement for: Ph.D., Arizona State University, 2019Includes bibliographical referencesField of study: Computer scienceby Jundong Lihttps://hdl.handle.net/2286/R.I.5483700Doctoral DissertationAcademic thesesxi, 150 pages : color illustrations115730762631630032421157589adminIn Copyright2019TextComputer ScienceApplicationsAttributed NetworksFeature SelectionNetwork EmbeddingOnline AlgorithmsComputer networksComputer algorithms