Efficient and scalable techniques for pagerank-based graph analytics - Yang, Renchi (Nanyang Technological University, 2020)
Graphs are ubiquitous today and are a fundamental data structure to represent objects and their relations in various domains, e.g., social science, citation analysis, weblink analysis, and biological informatics. PageRank-based techniques such as personalized PageRank, heat kernel PageRank, TrustRank have been well studied and shown great eﬃcacy in graph processing tasks including web ranking, recommender system, graph clustering and combating web spam. In this thesis, we investigate three important problems in graph processing by exploiting PageRank-based techniques, namely, local graph clustering, homogeneous network embedding and attributed network embedding. These three problems are not only interesting in themselves when the graph size becomes large, but also ﬁnd numerous applications in both academia and industry.