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28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KKD)

作者： Zhang, Delvin Ce Lauw, Hady W. Singapore Management Univ Singapore Singapore

ISBN: (纸本)9781450393850

While Variational GraphAuto-Encoder (VGAE) has presented promising ability to learn representations for documents, most existing VGAE methods do not model a latent topic structure and therefore lack semantic interpretability. Exploring hidden topics within documents and discovering key words associated with each topic allow us to develop a semantic interpretation of the corpus. Moreover, documents are usually associated with authors. For example, news reports have journalists specializing in writing certain type of events, academic papers have authors with expertise in certain research topics, etc. modeling authorship information could benefit topic modeling, since documents by the same authors tend to reveal similar semantics. This observation also holds for documents published on the same venues. However, most topic models ignore the auxiliary authorship and publication venues. Given above two challenges, we propose a Variational Graph author topic Model for documents to integrate both semantic interpretability and authorship and venue modeling into a unified VGAE framework. For authorship and venue modeling, we construct a hierarchical multilayered document graph with both intra- and cross-layer topic propagation. For semantic interpretability, three word relations (contextual, syntactic, semantic) are modeled and constitute three word sub-layers in the document graph. We further propose three alternatives for variational divergence. Experiments verify the effectiveness of our model on supervised and unsupervised tasks.

关键词： Graph Neural Networks Variational Graph Auto-Encoder author topic modeling Text Mining

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A bibliometric analysis on tobacco regulation investigators

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BIODATA MINING 2015年第1期8卷 11-11页

作者： Li, Dingcheng Okamoto, Janet Liu, Hongfang Leischow, Scott Mayo Clin Dept Biomed Stat & Informat Rochester MN 55905 USA Mayo Clin Dept Hematol Oncol Scottsdale AZ USA

Background: To facilitate the implementation of the Family Smoking Prevention and Tobacco Control Act of 2009, the Federal Drug Agency (FDA) Center for Tobacco Products (CTP) has identified research priorities under the umbrella of tobacco regulatory science (TRS). As a newly integrated field, the current boundaries and landscape of TRS research are in need of definition. In this work, we conducted a bibliometric study of TRS research by applying author topic modeling (ATM) on MEDLINE citations published by currently-funded TRS principle investigators (PIs). Results: We compared topics generated with ATM on dataset collected with TRS PIs and topics generated with ATM on dataset collected with a TRS keyword list. It is found that all those topics show a good alignment with FDA's funding protocols. More interestingly, we can see clear interactive relationships among PIs and between PIs and topics. Based on those interactions, we can discover how diverse each PI is, how productive they are, which topics are more popular and what main components each topic involves. Temporal trend analysis of key words shows the significant evaluation in four prime TRS areas. Conclusions: The results show that ATM can efficiently group articles into discriminative categories without any supervision. This indicates that we may incorporate ATM into author identification systems to infer the identity of an author of articles using topics generated by the model. It can also be useful to grantees and funding administrators in suggesting potential collaborators or identifying those that share common research interests for data harmonization or other purposes. The incorporation of temporal analysis can be employed to assess the change over time in TRS as new projects are funded and the extent to which new research reflects the funding priorities of the FDA.

关键词： author topic modeling Bibliometric analysis Tobacco regulation science FDA Principle investigators

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