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Information Cascading in Social Networks

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MDATA: A New Knowledge Representation Model

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12647))

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Abstract

This chapter introduces the application of information cascading analysis in social networks. We present a deep learning based framework of social network information cascade analysis, and we show the challenges of applying the MDATA model. The phenomenon of information dissemination in social networks is widespread, and Social Network Information Cascade Analysis (SNICA) aims to acquire valuable knowledge in the process of information dissemination in social networks. As the number, volume, and resolution of social network data increase rapidly, traditional social network data analysis methods, especially the analysis method of social network graph (SNG) data have become overwhelmed in SNICA. At the same time, the MDATA model fuses data from multiple sources in a graph, which can be applied to the SNICA problems. Recently, deep learning models have changed this situation, and it has achieved success in SNICA with its powerful implicit feature extraction capabilities. This chapter provides a comprehensive survey of recent progress in applying deep learning techniques for SNICA.

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References

  1. Osho, A., Goodman, C., Amariucai, G.: MIDMod-OSN: a microscopic-level information diffusion model for online social networks. In: Chellappan, S., Choo, K.-K.R., Phan, N.H. (eds.) CSoNet 2020. LNCS, vol. 12575, pp. 437–450. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66046-8_36

    Chapter  Google Scholar 

  2. Gruhl, D., Guha, R., Liben-Nowell, D., Tomkins, A.: Information diffusion through blogspace. In: Proceedings of the 13th international conference on World Wide Web, pp. 491–501 (2004)

    Google Scholar 

  3. Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N., Hurst, M.: Patterns of cascading behavior in large blog graphs. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 551–556. SIAM (2007)

    Google Scholar 

  4. Shen, H.-W., Wang, D., Song, C., Barabási, A.-L.: Modeling and predicting popularity dynamics via reinforced poisson processes. arXiv preprint arXiv:1401.0778 (2014)

  5. Liben-Nowell, D., Kleinberg, J.: Tracing information flow on a global scale using internet chain-letter data. Proc. Natl. Acad. Sci. 105(12), 4633–4638 (2008)

    Article  Google Scholar 

  6. Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66 (2001)

    Google Scholar 

  7. Leskovec, J., Singh, A., Kleinberg, J.: Patterns of influence in a recommendation network. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 380–389. Springer, Heidelberg (2006). https://doi.org/10.1007/11731139_44

    Chapter  Google Scholar 

  8. Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. ACM Trans. Web (TWEB) 1(1), 5-es (2007)

    Article  Google Scholar 

  9. Watts, D.J., Dodds, P.S.: Influentials, networks, and public opinion formation. J. Consum. Res. 34(4), 441–458 (2007)

    Article  Google Scholar 

  10. Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146 (2003)

    Google Scholar 

  11. Lappas, T., Terzi, E., Gunopulos, D., Mannila, H.: Finding effectors in social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1059–1068 (2010)

    Google Scholar 

  12. Dow, P.A., Adamic, L.A., Friggeri, A.: The anatomy of large Facebook cascades. In: Seventh International AAAI Conference on Weblogs and Social Media (2013)

    Google Scholar 

  13. Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the dynamics of the news cycle. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 497–506 (2009)

    Google Scholar 

  14. Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188 (2014)

  15. Mikolov, T., Kombrink, S., Burget, L., Černockỳ, J., Khudanpur, S.: Extensions of recurrent neural network language model. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5528–5531. IEEE (2011)

    Google Scholar 

  16. Zhou, J., et al.: Graph neural networks: a review of methods and applications. arXiv preprint arXiv:1812.08434 (2018)

  17. Cheng, J., Adamic, L., Dow, P.A., Kleinberg, J.M., Leskovec, J.: Can cascades be predicted? In: Proceedings of the 23rd International Conference on World Wide Web, pp. 925–936 (2014)

    Google Scholar 

  18. Guille, A., Hacid, H., Favre, C., Zighed, D.A.: Information diffusion in online social networks: a survey. ACM Sigmod Rec. 42(2), 17–28 (2013)

    Article  Google Scholar 

  19. Ibrahim, R.A., Hefny, H.A., Hassanien, A.E.: Controlling social information cascade: a survey. In: Big Data Analytics, pp. 196–212. CRC Press (2018)

    Google Scholar 

  20. Fang, B., Jia, Y., Han, Y., Li, S., Zhou, B.: A survey of social network and information dissemination analysis. Chin. Sci. Bull. 59(32), 4163–4172 (2014)

    Article  Google Scholar 

  21. Wani, M., Ahmad, M.: Information diffusion modelling and social network parameters (a survey). In: Proceedings of the International Conference on Advances in Computers, Communication and Electronic Engineering, Kashmir, India, pp. 16–18 (2015)

    Google Scholar 

  22. Gomez-Rodriguez, M., Leskovec, J., Schölkopf, B.: Modeling information propagation with survival theory. In: International Conference on Machine Learning, pp. 666–674 (2013)

    Google Scholar 

  23. Wang, Y., Shen, H.-W., Liu, S., Cheng, X.-Q.: Learning user-specific latent influence and susceptibility from information cascades. arXiv preprint arXiv:1310.3911 (2013)

  24. Ohsaka, N., Sonobe, T., Fujita, S., Kawarabayashi, K.-I.: Coarsening massive influence networks for scalable diffusion analysis. In: Proceedings of the 2017 ACM International Conference on Management of Data, pp. 635–650 (2017)

    Google Scholar 

  25. Saito, K., Nakano, R., Kimura, M.: Prediction of information diffusion probabilities for independent cascade model. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008. LNCS (LNAI), vol. 5179, pp. 67–75. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85567-5_9

    Chapter  Google Scholar 

  26. Gao, S., Ma, J., Chen, Z.: Modeling and predicting retweeting dynamics on microblogging platforms. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 107–116 (2015)

    Google Scholar 

  27. Cao, Q., Shen, H., Cen, K., Ouyang, W., Cheng, X.: DeepHawkes: bridging the gap between prediction and understanding of information cascades. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1149–1158 (2017)

    Google Scholar 

  28. Hong, L., Dan, O., Davison, B.D.: Predicting popular messages in Twitter. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 57–58 (2011)

    Google Scholar 

  29. Tsur, O., Rappoport, A.: What’s in a hashtag? Content based prediction of the spread of ideas in microblogging communities. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 643–652 (2012)

    Google Scholar 

  30. Petrovic, S., Osborne, M., Lavrenko, V.: RT to win! Predicting message propagation in Twitter. Icwsm 11, 586–589 (2011)

    Google Scholar 

  31. Berger, J., Milkman, K.L.: What makes online content viral? J. Mark. Res. 49(2), 192–205 (2012)

    Article  Google Scholar 

  32. Bhagat, S., Cormode, G., Muthukrishnan, S.: Node classification in social networks. In: Aggarwal, C. (ed.) Social Network Data Analytics, pp. 115–148. Springer, Boston (2011). https://doi.org/10.1007/978-1-4419-8462-3_5

    Chapter  Google Scholar 

  33. Vishwanathan, S.V.N., Schraudolph, N.N., Kondor, R., Borgwardt, K.M.: Graph kernels. J. Mach. Learn. Res. 11, 1201–1242 (2010)

    MathSciNet  MATH  Google Scholar 

  34. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)

    Article  Google Scholar 

  35. Rong, X.: Word2vec parameter learning explained. arXiv preprint arXiv:1411.2738 (2014)

  36. Guthrie, D., Allison, B., Liu, W., Guthrie, L., Wilks, Y.: A closer look at skip-gram modelling. In: LREC, vol. 6, pp. 1222–1225 (2006)

    Google Scholar 

  37. Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)

    Google Scholar 

  38. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077 (2015)

    Google Scholar 

  39. Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)

    Google Scholar 

  40. Hamilton, W.L., Ying, R., Leskovec, J.: Representation learning on graphs: methods and applications. arXiv preprint arXiv:1709.05584 (2017)

  41. Bengio, Y.: Learning Deep Architectures for AI. Now Publishers Inc. (2009)

    Google Scholar 

  42. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. (2020)

    Google Scholar 

  43. Liu, Y., Safavi, T., Dighe, A., Koutra, D.: Graph summarization methods and applications: a survey. ACM Comput. Surv. (CSUR) 51(3), 1–34 (2018)

    Article  Google Scholar 

  44. Gardner, M.W., Dorling, S.: Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos. Environ. 32(14–15), 2627–2636 (1998)

    Article  Google Scholar 

  45. Yang, C., Sun, M., Liu, H., Han, S., Liu, Z., Luan, H.: Neural diffusion model for microscopic cascade prediction. arXiv preprint arXiv:1812.08933 (2018)

  46. Kefato, Z.T., Sheikh, N., Bahri, L., Soliman, A., Montresor, A., Girdzijauskas, S.: Cas2vec: network-agnostic cascade prediction in online social networks. In: 2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 72–79. IEEE (2018)

    Google Scholar 

  47. Zhang, W., Wang, W., Wang, J., Zha, H.: User-guided hierarchical attention network for multi-modal social image popularity prediction. In: Proceedings of the 2018 World Wide Web Conference, pp. 1277–1286 (2018)

    Google Scholar 

  48. Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM (1999)

    Google Scholar 

  49. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)

  50. Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)

  51. Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2008)

    Article  Google Scholar 

  52. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  53. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)

  54. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

    Google Scholar 

  55. Molaei, S., Zare, H., Veisi, H.: Deep learning approach on information diffusion in heterogeneous networks. Knowl.-Based Syst. 189, 105153 (2020)

    Article  Google Scholar 

  56. Li, C., Guo, X., Mei, Q.: Joint modeling of text and networks for cascade prediction. In: Twelfth International AAAI Conference on Web and Social Media (2018)

    Google Scholar 

  57. Qiu, J., Tang, J., Ma, H., Dong, Y., Wang, K., Tang, J.: DeepInf: social influence prediction with deep learning. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2110–2119 (2018)

    Google Scholar 

  58. Cao, Q. Shen, H., Gao, J., Wei, B., Cheng, X.: Coupled graph neural networks for predicting the popularity of online content. arXiv preprint arXiv:1906.09032 (2019)

  59. Chen, X., Zhang, K., Zhou, F., Trajcevski, G., Zhong, T., Zhang, F.: Information cascades modeling via deep multi-task learning. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 885–888 (2019)

    Google Scholar 

  60. Wang, Z., Chen, C., Li, W.: A sequential neural information diffusion model with structure attention. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1795–1798 (2018)

    Google Scholar 

  61. Su, Y., Zhang, X., Wang, S., Fang, B., Zhang, T., Yu, P.S.: Understanding information diffusion via heterogeneous information network embeddings. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds.) DASFAA 2019. LNCS, vol. 11446, pp. 501–516. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-18576-3_30

    Chapter  Google Scholar 

  62. Liao, D., Xu, J., Li, G., Huang, W., Liu, W., Li, J.: Popularity prediction on online articles with deep fusion of temporal process and content features. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 200–207 (2019)

    Google Scholar 

  63. Nguyen, D.T., Al-Mannai, K., Joty, S.R., Sajjad, H., Imran, M., Mitra, P.: Robust classification of crisis-related data on social networks using convolutional neural networks. ICWSM 31(3), 632–635 (2017)

    Google Scholar 

  64. Guo, H., Cao, J., Zhang, Y., Guo, J., Li, J.: Rumor detection with hierarchical social attention network. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 943–951 (2018)

    Google Scholar 

  65. Liu, Y., Wu, Y.-F.B.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  66. Wang, J. Zheng, V.W., Liu, Z., Chang, K.C.-C.: Topological recurrent neural network for diffusion prediction. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 475–484. IEEE (2017)

    Google Scholar 

  67. Li, C., Ma, J., Guo, X., Mei, Q.: DeepCas: an end-to-end predictor of information cascades. In: Proceedings of the 26th International Conference on World Wide Web, pp. 577–586 (2017)

    Google Scholar 

  68. Chen, G., Kong, Q., Xu, N., Mao, W.: NPP: a neural popularity prediction model for social media content. Neurocomputing 333, 221–230 (2019)

    Article  Google Scholar 

  69. Wang, W., Zhang, W., Wang, J., Yan, J., Zha, H.: Learning sequential correlation for user generated textual content popularity prediction. In: IJCAI, pp. 1625–1631 (2018)

    Google Scholar 

  70. Mishra, S., Rizoiu, M.-A., Xie, L.: Modeling popularity in asynchronous social media streams with recurrent neural networks. arXiv preprint arXiv:1804.02101 (2018)

  71. Dou, H., Zhao, W.X., Zhao, Y., Dong, D., Wen, J.-R., Chang, E.Y.: Predicting the popularity of online content with knowledge-enhanced neural networks. In: ACM KDD (2018)

    Google Scholar 

  72. Islam, M.R., Muthiah, S., Adhikari, B., Prakash, B.A., Ramakrishnan, N.: DeepDiffuse: predicting the ‘who’ and ‘when’ in cascades. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 1055–1060. IEEE (2018)

    Google Scholar 

  73. Chen, X., Zhou, F., Zhang, K., Trajcevski, G., Zhong, T., Zhang, F.: Information diffusion prediction via recurrent cascades convolution. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 770–781. IEEE (2019)

    Google Scholar 

  74. Qiu, X., Huang, X.: Convolutional neural tensor network architecture for community-based question answering. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)

    Google Scholar 

  75. Feng, X., Zhao, Q., Liu, Z.: Prediction of information cascades via content and structure integrated whole graph embedding. In: IJCAI (2019)

    Google Scholar 

  76. Yang, C., Tang, J., Sun, M., Cui, G., Liu, Z.: Multi-scale information diffusion prediction with reinforced recurrent networks. In: IJCAI, pp. 4033–4039 (2019)

    Google Scholar 

  77. Wang, Y., Shen, H., Liu, S., Gao, J., Cheng, X.: Cascade dynamics modeling with attention-based recurrent neural network. In: IJCAI, pp. 2985–2991 (2017)

    Google Scholar 

  78. Wang, Z., Chen, C., Li, W.: Attention network for information diffusion prediction. In: Companion Proceedings of the The Web Conference 2018, pp. 65–66 (2018)

    Google Scholar 

  79. Zhao, Y., Yang, N., Lin, T., Philip, S.Y.: Deep collaborative embedding for information cascade prediction. Knowl.-Based Syst. 193, 105502 (2020)

    Article  Google Scholar 

  80. Ma, J., et al.: Detecting rumors from microblogs with recurrent neural networks (2016)

    Google Scholar 

  81. Ruchansky, N., Seo, S., Liu, Y.: CSI: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 797–806 (2017)

    Google Scholar 

  82. Nguyen, T.N., Li, C., Niederée, C.: On early-stage debunking rumors on Twitter: leveraging the wisdom of weak learners. In: Ciampaglia, G.L., Mashhadi, A., Yasseri, T. (eds.) SocInfo 2017. LNCS, vol. 10540, pp. 141–158. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67256-4_13

    Chapter  Google Scholar 

  83. Deng, S., Rangwala, H., Ning, Y.: Learning dynamic context graphs for predicting social events. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1007–1016 (2019)

    Google Scholar 

  84. Wu, Q., Zhang, Z., Gao, X., Yan, J., Chen, G.: Learning latent process from high-dimensional event sequences via efficient sampling. In: Advances in Neural Information Processing Systems, pp. 3847–3856 (2019)

    Google Scholar 

  85. Wu, W., Liu, H., Zhang, X., Liu, Y., Zha, H.: Modeling event propagation via graph biased temporal point process. IEEE Trans. Neural Netw. Learn. Syst. (2020)

    Google Scholar 

  86. Figueiredo, F., Benevenuto, F., Almeida, J.M.: The tube over time: characterizing popularity growth of YouTube videos. In: Proceedings of the fourth ACM International Conference on Web Search and Data Mining, pp. 745–754 (2011)

    Google Scholar 

  87. Chen, T., Li, X., Yin, H., Zhang, J.: Call attention to rumors: deep attention based recurrent neural networks for early rumor detection. In: Ganji, M., Rashidi, L., Fung, B.C.M., Wang, C. (eds.) PAKDD 2018. LNCS (LNAI), vol. 11154, pp. 40–52. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04503-6_4

    Chapter  Google Scholar 

  88. Wang, Z., Guo, Y.: Rumor events detection enhanced by encoding sentimental information into time series division and word representations. Neurocomputing 397, 224–243 (2020)

    Article  Google Scholar 

  89. Kleinberg, J.: Bursty and hierarchical structure in streams. Data Min. Knowl. Disc. 7(4), 373–397 (2003)

    Article  MathSciNet  Google Scholar 

  90. Weng, J., Lee, B.-S.: Event detection in Twitter. Icwsm 11(2011), 401–408 (2011)

    Google Scholar 

  91. Hussain, A., Keshavamurthy, B.N., Wazarkar, S.: An efficient approach for classifying social network events using convolution neural networks. In: Kolhe, M.L., Trivedi, M.C., Tiwari, S., Singh, V.K. (eds.) Advances in Data and Information Sciences. LNNS, vol. 39, pp. 177–184. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-0277-0_15

    Chapter  Google Scholar 

  92. Karahalios, K.G., Viégas, F.B.: Social visualization: exploring text, audio, and video interaction. In: CHI 2006 Extended Abstracts on Human Factors in Computing Systems, pp. 1667–1670 (2006)

    Google Scholar 

  93. Du, M., Liu, N., Hu, X.: Techniques for interpretable machine learning. Commun. ACM 63(1), 68–77 (2019)

    Article  Google Scholar 

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Authors and Affiliations

  1. National University of Defense Technology, Changsha, 410073, China

    Liqun Gao, Bin Zhou, Hongkui Tu & Ye Wang

  2. Harbin Institute of Technology, Shenzhen, 518055, China

    Yan Jia

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  1. Liqun Gao
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  2. Bin Zhou
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  4. Hongkui Tu
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Correspondence to Bin Zhou .

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Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, Shenzhen, China

    Yan Jia

  2. Guangzhou University, Guangzhou, China

    Zhaoquan Gu

  3. National University of Defense Technology, Changsha, China

    Aiping Li

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Gao, L., Zhou, B., Jia, Y., Tu, H., Wang, Y. (2021). Information Cascading in Social Networks. In: Jia, Y., Gu, Z., Li, A. (eds) MDATA: A New Knowledge Representation Model. Lecture Notes in Computer Science(), vol 12647. Springer, Cham. https://doi.org/10.1007/978-3-030-71590-8_14

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