Please note: This seminar has been cancelled
Carl Yang, Department of Computer Science
University of Illinois at Urbana–Champaign
Graph data are ubiquitous and indispensable in a variety of high-impact data mining problems and applications, due to its natural and unique modeling of interconnected objects. However, real-world graph data are often massive, complex, and noisy, challenging the design of both effective and efficient knowledge discovery frameworks.
In this talk, I will present our recent progress on multi-facet contextualized graph mining, centered around the objective of multi-modal data integration across different domains. In particular, I will focus on (1) a new data model of cube networks, which organizes massive complex networks into controllable small subnetworks with clear structures and semantics under multi-facet contexts; (2) a few algorithmic examples on what can be done on top of cube networks. Beyond that, I will also briefly give examples on how to construct cube networks from existing data models like attributed heterogeneous networks, and what real-world impact cube networks can make on industry-level applications. Finally, I will conclude with some visions and future plans regarding learning with cube networks.
Bio: Carl Yang is a final-year Ph.D. student with Jiawei Han in Computer Science at University of Illinois, Urbana Champaign. Before that, he received his B.Eng. in Computer Science at Zhejiang University under Xiaofei He in 2014. In his research, he develops data-driven, weakly supervised, and scalable techniques for knowledge discovery from massive, complex and noisy network (graph) data.
His interests span graph data mining, network data science, and applied machine learning, with a focus on designing novel graph analysis and deep learning frameworks for the construction, modeling, and application of real-world network data, towards tasks like conditional structure generation, contextualized network embedding, graph-aided recommendations, and so on. Carl’s leading-author research results have been published and well-cited in top conferences like KDD, WWW, NeurIPS, ICDE, WSDM, ICDM, CIKM, ECML-PKDD, SDM and ICML.
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