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Collective topic modeling for heterogeneous networks

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SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Pages 1109 - 1110

https://doi.org/10.1145/2009916.2010073

Published: 24 July 2011 Publication History

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Abstract

In this paper, we propose a joint probabilistic topic model for simultaneously modeling the contents of multi-typed objects of a heterogeneous information network. The intuition behind our model is that different objects of the heterogeneous network share a common set of latent topics so as to adjust the multinomial distributions over topics for different objects collectively. Experimental results demonstrate the effectiveness of our approach for the tasks of topic modeling and object clustering.

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Cited By

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Ding J(2022)New Computational Methods for Literature-Based Discoveryundefined10.12794/metadc1944357Online publication date: May-2022
https://doi.org/10.12794/metadc1944357
Aggarwal CAggarwal C(2022)Joint Text Mining with Heterogeneous DataMachine Learning for Text10.1007/978-3-030-96623-2_8(233-256)Online publication date: 10-Feb-2022
https://doi.org/10.1007/978-3-030-96623-2_8
Zhang YYang XWang LLi K(2020)WMPEClus: Clustering via Weighted Meta-Path Embedding for Heterogeneous Information Networks2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI50040.2020.00127(799-806)Online publication date: Nov-2020
https://doi.org/10.1109/ICTAI50040.2020.00127
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Index Terms

Collective topic modeling for heterogeneous networks
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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Published In

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

July 2011

1374 pages

ISBN:9781450307574

DOI:10.1145/2009916

General Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Jian-Yun Nie
University of Montreal, Canada
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Research, Spain
,
Tat-Seng Chua
National University of Singapore
,
W. Bruce Croft
University of Massachusetts, Amherst, USA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 July 2011

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SIGIR '11

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SIGIR

SIGIR '11: The 34th International ACM SIGIR conference on research and development in Information Retrieval

July 24 - 28, 2011

Beijing, China

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Ding J(2022)New Computational Methods for Literature-Based Discoveryundefined10.12794/metadc1944357Online publication date: May-2022
https://doi.org/10.12794/metadc1944357
Aggarwal CAggarwal C(2022)Joint Text Mining with Heterogeneous DataMachine Learning for Text10.1007/978-3-030-96623-2_8(233-256)Online publication date: 10-Feb-2022
https://doi.org/10.1007/978-3-030-96623-2_8
Zhang YYang XWang LLi K(2020)WMPEClus: Clustering via Weighted Meta-Path Embedding for Heterogeneous Information Networks2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI50040.2020.00127(799-806)Online publication date: Nov-2020
https://doi.org/10.1109/ICTAI50040.2020.00127
Zhang YYang XWang L(2020)Clustering via Meta-path Embedding for Heterogeneous Information Networks2020 IEEE International Conference on Knowledge Graph (ICKG)10.1109/ICBK50248.2020.00036(188-194)Online publication date: Aug-2020
https://doi.org/10.1109/ICBK50248.2020.00036
Ding JJin W(2019)A Prior Setting that Improves LDA in both Document Representation and Topic Extraction2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852000(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8852000
Ding JJin W(2019)MDLDA: A New Multi-Dimension Topic Approach2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8851812(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8851812
Ding JJin W(2019)OverlapLDA: A Generative Approach for Literature-Based Discovery2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM47256.2019.8983400(362-369)Online publication date: Nov-2019
https://doi.org/10.1109/BIBM47256.2019.8983400
Aggarwal CAggarwal C(2018)Joint Text Mining with Heterogeneous DataMachine Learning for Text10.1007/978-3-319-73531-3_8(235-258)Online publication date: 20-Mar-2018
https://doi.org/10.1007/978-3-319-73531-3_8
Shi CLi YZhang JSun YYu P(2017)A Survey of Heterogeneous Information Network AnalysisIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.259856129:1(17-37)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/TKDE.2016.2598561
Shi CYu PShi CYu P(2017)Survey of Current DevelopmentsHeterogeneous Information Network Analysis and Applications10.1007/978-3-319-56212-4_2(13-30)Online publication date: 26-May-2017
https://doi.org/10.1007/978-3-319-56212-4_2
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Probabilistic topic models with biased propagation on heterogeneous information networks

Heterogeneous-Length Text Topic Modeling for Reader-Aware Multi-Document Summarization

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