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Han, Jiawei Chan, Po-Wei 2017-08-10T19:15:36Z 2017-08-10T19:15:36Z 2017-04-24 2017-05 As a powerful representation paradigm for networked and multi-typed data, the heterogeneous information network (HIN) is ubiquitous. Meanwhile, defining proper relevance measures has always been a fundamental problem and of great pragmatic importance for network mining tasks. Inspired by the probabilistic interpretation of existing path-based relevance measures, we propose to study HIN relevance from a probabilistic perspective. We also identify, from real-world data, and propose to model cross-meta-path synergy, which is a characteristic important for defining path-based HIN relevance and has not been modeled by existing methods. A generative model is established to derive a novel path-based relevance measure, which is data-driven and tailored for each HIN. We develop an inference algorithm to find the maximum a posteriori (MAP) estimate of the model parameters, which entails non-trivial tricks. Experiments on two real-world datasets demonstrate the effectiveness of the proposed model and relevance measure. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms The student, Po-Wei Chan, accepted the attached license on 2017-04-19 at 20:50. The student, Po-Wei Chan, submitted this Thesis for approval on 2017-04-19 at 21:03. This Thesis was approved for publication on 2017-04-24 at 09:56. DSpace SAF Submission Ingestion Package generated from Vireo submission #10918 on 2017-08-10 at 13:43:24 Made available in DSpace on 2017-08-10T19:15:36Z (GMT). No. of bitstreams: 2 CHAN-THESIS-2017.pdf: 833122 bytes, checksum: cd8c658d3904291bd82a55c09719de05 (MD5) LICENSE.txt: 4208 bytes, checksum: 40bb5d8629ef196fbc1e326b21d78476 (MD5) Previous issue date: 2017-04-24 application/pdf http://hdl.handle.net/2142/97416 en Copyright 2017 Po-Wei Chan Relevance measure Heterogeneous information network Generative model Probabilistic interpretation of path-based relevance in heterogeneous information networks text text Computer Science Computer Science University of Illinois at Urbana-Champaign Thesis M.S.