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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 10/2020

20.03.2019 | Original Article

SRTM: a supervised relation topic model for multi-classification on large-scale document network

verfasst von: Chunshan Li, Hua Zhang, Dianhui Chu, Xiaofei Xu

Erschienen in: Neural Computing and Applications | Ausgabe 10/2020

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Abstract

The increasing use of social networking platforms has raised the need to develop automated multi-classifications on document network. In this paper, we propose a supervised relation topic model that leverages the links between documents to learn the latent content of documents and enhance performance of prediction. Our model takes advantage of Bayesian generative model to exploit the relation between word feature and link feature in a document network. We evaluate our model on large-scale data collections that include scientific citation community and medical article network. We demonstrate its effectiveness and efficiency on document classification with SLDA model and collective classification approaches.
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Literatur
1.
Neville J, Jensen D (2000) Iterative classification in relational data. In: Proceedings of AAAI-2000 workshop on learning statistical models from relational data, pp 13–20
2.
Jensen D, Neville J, Gallagher B (2004) Why collective inference improves relational classification. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 593–598
3.
Macskassy SA, Provost F (2007) Classification in networked data: a toolkit and a univariate case study. J Mach Learn Res 8(May):935–983
4.
Quan X, Liu G, Lu Z, Ni X, Wenyin L (2010) Short text similarity based on probabilistic topics. Knowl Inf Syst 25(3):473–491
5.
Rodrigues F, Lourenco M, Ribeiro B, Pereira FC (2017) Learning supervised topic models for classification and regression from crowds. IEEE Trans Pattern Anal Mach Intell 39(12):2409–2422
6.
Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(Jan):993–1022MATH
7.
Cambria E, Rajagopal D, Olsher D, Das D (2013) Big social data analysis. Big Data Comput 2013:401–414
8.
Rajagopal D, Olsher D, Cambria E, Kwok K (2013) Commonsense-based topic modeling. In: Proceedings of the 2nd international workshop on issues of sentiment discovery and opinion mining. ACM, p 6
9.
Lau RY, Xia Y, Ye Y (2014) A probabilistic generative model for mining cybercriminal networks from online social media. IEEE Comput Intell Mag 9(1):31–43
10.
Nallapati RM, Ahmed A, Xing EP, Cohen WW (2008) Joint latent topic models for text and citations. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 542–550
11.
Mei Q, Cai D, Zhang D, Zhai C (2008) Topic modeling with network regularization. In: Proceedings of the 17th international conference on World Wide Web. ACM, pp 101–110
12.
Gehler PV, Holub AD, Welling M (2006) The rate adapting poisson model for information retrieval and object recognition. In: Proceedings of the 23rd international conference on Machine learning. ACM, pp 337–344
13.
Zhang H, Chow TW, Rahman M (2009) A new dual wing harmonium model for document retrieval. Pattern Recognit 42(11):2950–2960MATH
14.
Pan Z, Liu Y, Liu G, Guo M, Li Y (2015) Topic network: topic model with deep learning for image classification. In: International conference on knowledge science, engineering and management. Springer, pp 525–534
15.
Wu H, Lerman K (2017) Deep context: a neural language model for large-scale networked documents. In: Proceedings of the 26th international joint conference on artificial intelligence. AAAI Press, pp 3091–3097
16.
He T, Yin H, Chen Z, Zhou X, Sadiq S, Luo B (2016) A spatial-temporal topic model for the semantic annotation of pois in lbsns. ACM Trans Intell Syst Technol (TIST) 8(1):12
17.
Rudolph M, Ruiz F, Athey S, Blei D (2017) Structured embedding models for grouped data. In: Advances in neural information processing systems, pp 251–261
18.
Wang C, Blei DM et al (2018) A general method for robust bayesian modeling. Bayesian Anal 13(4):1159–1187MathSciNetMATH
19.
Liu Y, Niculescu-Mizil A, Gryc W (2009) Topic-link lda: joint models of topic and author community. In: proceedings of the 26th annual international conference on machine learning. ACM, pp 665–672
20.
Getoor L (2007) Link-based classification. In: Advanced methods for knowledge discovery from complex data. Advanced information and knowledge processing. Springer, London, pp 189–207
21.
Mcdowell LK, Gupta KM, Aha DW (2009) Cautious collective classification. J Mach Learn Res 10(18):2777–2836MathSciNetMATH
22.
Jiang M, Liang Y, Feng X, Fan X, Pei Z, Xue Y, Guan R (2016) Text classification based on deep belief network and softmax regression. Neural Comput Appl 7:1–10
23.
Mertiya M, Singh A (2017) Combining naive bayes and adjective analysis for sentiment detection on twitter. In: International conference on inventive computation technologies, pp 1–6
24.
Taskar B, Abbeel P, Koller D (2004) Discriminative probabilistic models for relational data. Algorithmic Bioproceses LNCS 7(7):485–492
25.
Tatikonda SC, Jordan MI (2002) Loopy belief propagation and Gibbs measures. In: 18th conference on uncertainty in artificial intelligence, pp 493–500
26.
Ueda N, Saito K (2002) Parametric mixture models for multi-labeled text. In: International conference on neural information processing systems, pp 737–744
27.
Macskassy SA, Provost Foster (2007) Classification in networked data-a toolkit and a univariate case study. J Mach Learn Res 8(3):1–41MathSciNet
28.
Wang S, Ye Y, Li X, Huang X, Lau RYK (2016) Semi-supervised collective classification in multi-attribute network data. Neural Process Lett 45(1):1–20
29.
Kajdanowicz T, Kazienko P (2017) Collective classification. In: Encyclopedia of social network analysis and mining. Springer
30.
McDowell L, Aha D (2012) Semi-supervised collective classification via hybrid label regularization. arXiv preprint arXiv:​1206.​6467
31.
Wu Q, Tan M, Li X, Min H, Sun N (2015) NMFE-SSCC: Non-negative matrix factorization ensemble for semi-supervised collective classification. Knowl Based Syst 89:160–172
32.
Pham T, Tran T, Phung D, Venkatesh S (2017) Column networks for collective classification. Proceedings of the thirty-first AAAI conference on artificial intelligence (AAAI-17), pp 2485–2491
33.
Gupta S, Khattar A, Gogia A, Kumaraguru P, Chakraborty T (2018) Collective classification of spam campaigners on twitter: a hierarchical meta-path based approach
34.
Mcauliffe JD, Blei DM (2008) Supervised topic models. In: Advances in neural information processing systems, pp 121–128
35.
Li C, Cheung WK, Ye Y, Zhang X, Chu D, Li X (2015) The author-topic-community model for author interest profiling and community discovery. Knowl Inf Syst 44(2):359–383
36.
Bui T, Hernández-Lobato D, Hernandez-Lobato J, Li Y, Turner R (2016) Deep gaussian processes for regression using approximate expectation propagation. In: International conference on machine learning, pp 1472–1481
37.
Billio M, Casarin R, Osuntuyi A (2016) Efficient Gibbs sampling for markov switching garch models. Comput Stat Data Anal 100:37–57MathSciNetMATH
38.
Porteous I, Newman D, Ihler A, Asuncion A, Smyth P, Welling M (2008) Fast collapsed Gibbs sampling for latent dirichlet allocation. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 569–577
39.
Kong X, Shi X, Yu PS (2011) Multi-label collective classification. In: Proceedings of the 2011 SIAM international conference on data mining. SIAM, pp 618–629
40.
Wu Q, Ye Y, Ng MK, Ho SS, Shi R (2014) Collective prediction of protein functions from protein-protein interaction networks. BMC Bioinf 15(S2):1–10
41.
Zhou ZH, Zhang ML, Huang SJ, Li YF (2008) Multi-instance multi-label learning. Artif Intell 176(1):2291–2320MathSciNetMATH
Metadaten
Titel
SRTM: a supervised relation topic model for multi-classification on large-scale document network
verfasst von
Chunshan Li
Hua Zhang
Dianhui Chu
Xiaofei Xu
Publikationsdatum
20.03.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 10/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04145-5

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