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Volume 9 Issue 8
Aug.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Contents
J. H. Lü, G. H. Wen, R. Q. Lu, Y. Wang, and  S. M. Zhang,  “Networked knowledge and complex networks: An engineering view,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1366–1383, Aug. 2022. doi: 10.1109/JAS.2022.105737
Citation: J. H. Lü, G. H. Wen, R. Q. Lu, Y. Wang, and  S. M. Zhang,  “Networked knowledge and complex networks: An engineering view,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1366–1383, Aug. 2022. doi: 10.1109/JAS.2022.105737

Networked Knowledge and Complex Networks: An Engineering View

doi: 10.1109/JAS.2022.105737
Funds:  This work was supported in part by the National Natural Science Foundation of China (61621003, 62073079, 62088101, 12025107, 11871463, 11688101)
More Information
  • Along with the development of information technologies such as mobile Internet, information acquisition technology, cloud computing and big data technology, the traditional knowledge engineering and knowledge-based software engineering have undergone fundamental changes where the network plays an increasingly important role. Within this context, it is required to develop new methodologies as well as technical tools for network-based knowledge representation, knowledge services and knowledge engineering. Obviously, the term “network” has different meanings in different scenarios. Meanwhile, some breakthroughs in several bottleneck problems of complex networks promote the developments of the new methodologies and technical tools for network-based knowledge representation, knowledge services and knowledge engineering. This paper first reviews some recent advances on complex networks, and then, in conjunction with knowledge graph, proposes a framework of networked knowledge which models knowledge and its relationships with the perspective of complex networks. For the unique advantages of deep learning in acquiring and processing knowledge, this paper reviews its development and emphasizes the role that it played in the development of knowledge engineering. Finally, some challenges and further trends are discussed.


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  • [1]
    M. Saberi, H. Hamedmoghadam, M. Ashfaq, S. A. Hosseini, Z. Gu, S. Shafiei, D. J. Nair, V. Dixit, L. Gardner, S. T. Waller, and M. C. González, “A simple contagion process describes spreading of traffic jams in urban networks,” Nat. Commun., vol. 11, no. 1, p. 1616, Apr. 2020.
    D. J. Watts and S. H. Strogatz, “Collective dynamics of ‘small-world’ networks,” Nature, vol. 393, no. 6684, pp. 440–442, Jun. 1998. doi: 10.1038/30918
    A.-L. Barabási and R. Albert, “Emergence of scaling in random networks,” Science, vol. 286, no. 5439, pp. 509–512, Oct. 1999. doi: 10.1126/science.286.5439.509
    D. Brockmann and D. Helbing, “The hidden geometry of complex, network-driven contagion phenomena,” Science, vol. 342, no. 6164, pp. 1337–1342, Dec. 2013. doi: 10.1126/science.1245200
    H. Gu, J. H. Lü, and Z. Lin, “On PID control for synchronization of complex dynamical network with delayed nodes,” Science China: Technological Sciences, vol. 62, no. 8, pp. 1412–1422, Aug. 2019. doi: 10.1007/s11431-018-9379-8
    D. Guilbeault and D. Centola, “Topological measures for identifying and predicting the spread of complex contagions,” Nat. Commun., vol. 12, no. 1, p. 4430, Jul. 2021.
    Y.-Y. Liu, J.-J. Slotine, and A.-L. Barabási, “Controllability of complex networks,” Nature, vol. 473, no. 7346, pp. 167–173, May 2011. doi: 10.1038/nature10011
    A. Singhal, “Introducing the knowledge graph: Things, not strings,” May. 2012. [Online], Avaiable: https://www.blog.google/products/search/introducing-knowledge-graph-things-not/
    Y. Chi, Y. Qin, R. Song, and H. Xu, “Knowledge graph in smart education: A case study of entrepreneurship scientific publication management,” Sustainability, vol. 10, no. 4, p. 995, Mar. 2018.
    S. S. Hasan, D. Rivera, X.-C. Wu, E. B. Durbin, J. B. Christian, and G. Tourassi, “Knowledge graph-enabled cancer data analytics,” IEEE J. Biomed. Health Inform., vol. 24, no. 7, pp. 1952–1967, May 2020. doi: 10.1109/JBHI.2020.2990797
    D. Xu, C. Ruan, E. Korpeoglu, S. Kumar, and K. Achan, “Product knowledge graph embedding for e-commerce,” in Proc. 13th Int. Conf. Web Search and Data Mining, 2020, pp. 672–680.
    S. Bhatt, A. Sheth, V. Shalin, and J. Zhao, “Knowledge graph semantic enhancement of input data for improving AI,” IEEE Internet Comput., vol. 24, no. 2, pp. 66–72, Mar. 2020. doi: 10.1109/MIC.2020.2979620
    P. Zheng, L. Xia, C. Li, X. Li, and B. Liu, “Towards Self-X cognitive manufacturing network: An industrial knowledge graph-based multi-agent reinforcement learning approach,” J. Manuf. Syst., vol. 61, pp. 16–26, Oct. 2021. doi: 10.1016/j.jmsy.2021.08.002
    Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015. doi: 10.1038/nature14539
    J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” Proceed. National Acad. Sci. USA, vol. 79, no. 8, pp. 2554–2558, Apr. 1982. doi: 10.1073/pnas.79.8.2554
    Y. LeCun, Learning Process in an Asymmetric Threshold Network, Berlin Heidelberg, Germany: Springer, 1986.
    Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, Nov. 1998. doi: 10.1109/5.726791
    A. Khan, A. Sohail, U. Zahoora, and A. Qureshi, “A survey of the recent architectures of deep convolutional neural networks,” Artif. Intell. Rev., vol. 53, no. 8, pp. 5455–5516, Dec. 2020. doi: 10.1007/s10462-020-09825-6
    M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,” IEEE Trans. Signal Proces., vol. 45, no. 11, pp. 2673–2681, Nov. 1997. doi: 10.1109/78.650093
    R. Socher, C. C.-Y. Lin, A. Y. Ng, and C. D. Manning, “Parsing natural scenes and natural language with recursive neural networks,” in Proc. 28th Int. Conf. Machine Learning, 2011.
    J. Zhou, G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, L. Wang, C. Li, and M. Sun, “Graph neural networks: A review of methods and applications,” AI Open, vol. 1, pp. 57–81, 2020. doi: 10.1016/j.aiopen.2021.01.001
    T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv: 1609.02907, 2016.
    V. Petar, C. Guillem, C. Arantxa, R. Adriana, L. Pietro, and B. Yoshua, “Graph attention networks,” arXiv preprint arXiv: 1710.10903, 2017.
    I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengjo,, “Generative adversarial nets,” in Proc. 27th Int. Conf. Neural Information Processing Systems, 2014, vol. 27, pp. 1630–1644.
    R. Lu, X. Jin, S. Zhang, M. Qiu, and X. Wu, “A study on big knowledge and its engineering issues,” IEEE Trans. Knowl. Data Eng., vol. 31, no. 9, pp. 1630–1644, Sep. 2016.
    G. Zhu and C. A. Iglesias, “Computing semantic similarity of concepts in knowledge graphs,” IEEE Trans. Knowl. Data Eng., vol. 29, no. 1, pp. 72–85, Jan. 2017. doi: 10.1109/TKDE.2016.2610428
    UniProt Consortium, “UniProt: The universal protein knowledgebase in 2021,” Nucleic Acids Res., vol. 49, no. D1, pp. D480–D489, Jan. 2021. doi: 10.1093/nar/gkaa1100
    The Gene Ontology Consortium, “The gene ontology resource: 20 years and still going strong,” Nucleic Acids Res., vol. 47, no. D1, pp. D330–D338, Jan. 2019. doi: 10.1093/nar/gky1055
    M. Zitnik, M. Agrawal, and J. Leskovec, “Modeling polypharmacy side effects with graph convolutional networks,” Bioinformatics, vol. 34, no. 13, pp. i457–i466, Jul. 2018. doi: 10.1093/bioinformatics/bty294
    B. Malone, A. García-Durán, and M. Niepert. “Knowledge graph completion to predict polypharmacy side effects,” in Data Integration in the Life Sciences, Hannover, Germany: Springer, 2018, vol. 11371, pp. 144–149.
    S. Mohamed, V. Nováček, and A. Nounu, “Discovering protein drug targets using knowledge graph embeddings,” Bioinformatics, vol. 36, no. 2, pp. 603–610, Jan. 2020.
    M. Alshahrani, M. Khan, O. Maddouri, A. Kinjo, N. Queralt-Rosinach, and R. Hoehndorf, “Neuro-symbolic representation learning on biological knowledge graphs,” Bioinformatics, vol. 33, no. 17, pp. 2723–2730, Sep. 2017. doi: 10.1093/bioinformatics/btx275
    Y. Wu, Z. Wang, S. Chen, G. Wang and C. Li, “Automatically semantic annotation of network document based on domain knowledge graph,” in Proc. IEEE Int. Symposium on Parallel and Distributed Processing with Applications and IEEE Int. Conf. Ubiquitous Computing and Communications, 2017, pp. 715–721.
    F. Al-Obeidat, O. Adedugbe, A. B. Hani, E. Benkhelifa and M. Majdalawieh, “Cone-KG: A semantic knowledge graph with news content and social context for studying Covid-19 news articles on social Media,” in Proc. Seventh Int. Conf. Social Networks Analysis, Management and Security, 2020, pp. 1–7.
    O. Elezaj, S. Y. Yayilgan, E. Kalemi, L. Wendelberg, M. Abomhara, and J. Ahmed, “Towards designing a knowledge graph-based framework for investigating and preventing crime on online social networks,” in Proc. Int. Conf. e-Democracy, 2019, pp. 181–195.
    J. Qian, X. Y. Li, C. Zhang, L. Chen, T. Jung, and J. Han, “Social network de-anonymization and privacy inference with knowledge graph model,” IEEE Trans. Dependable Secur. Comput., vol. 16, no. 4, pp. 679–692, Jul. 2006.
    X. Cheng and X. Li, “Trust evaluation in online social networks based on knowledge graph,” in Proc. Int. Conf. Algorithms, Computing and Artificial Intelligence, 2018, pp. 1–7.
    J. Yang, J. Yang, Y. Wang, and Y. Mao, “Social network-based news recommendation with knowledge graph,” in Proc. IEEE Int. Conf. Information Technology, Big Data and Artificial Intelligence, 2020, vol. 1, pp. 1255–1260.
    Y. W. Teng, Y. Shi, J. Y. Tsai, H. H. Shuai, C. H. Tai, and D. N. Yang, “Optimizing social-topic engagement on social network and knowledge graph,” in Proc. IEEE Global Communications Conf., 2019, pp. 1–6.
    C. Wang, J. An, and G. Mu, “Power system network topology identification based on knowledge graph and graph neural network,” Front. Energy Res., vol. 8, p. 613331, Feb. 2021.
    Y. Tang, T. Liu, G. Liu, J. Li, R. Dai and C. Yuan, “Enhancement of power equipment management using knowledge graph,” in Proc. IEEE Innovative Smart Grid Technologies-Asia, 2019, pp. 905–910.
    F. Peng, T. An, D. Li, H. Wang, C. Tian and Z. Chen, “Knowledge graph for power grid dispatching of digital homes based on graph convolutional network,” in Proc. 8th Int. Conf. Digital Home, 2020, pp. 203–208.
    Y. Ma, D. Hong, F. Dan, X. Yang, and X. Li, “Research on the construction method of knowledge graph for power grid education resources,” in Proc. IEEE 3rd Int. Conf. Computer Science and Educational Informatization, 2021, pp. 99–103.
    S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D.-U. Hwang, “Complex networks: Structure and dynamics,” Phys. Rep.-Rev. Sec. Phys. Lett., vol. 424, no. 4–5, pp. 175–308, Feb. 2006.
    L. Wu, P. Wang, and J. H. Lü, “Substrate concentration effect on gene expression in genetic circuits with additional positive feedback,” Science China: Technological Sciences, vol. 61, no. 8, pp. 1175–1183, Aug. 2018. doi: 10.1007/s11431-018-9301-0
    P. Erdös and A. Rényi, “On random graphs,” Publ. Math.-Debr., vol. 6, pp. 290–297, 1959.
    E. N. Gilbert, “Random graphs,” Ann. Math. Statist., vol. 30, pp. 1141–1144, 1959. doi: 10.1214/aoms/1177706098
    J. Travers and S. Milgram, “An experimental study of the small world problem,” Sociometry, vol. 32, no. 4, p. 425, Dec. 1969.
    L. Backstrom, P. Boldi, M. Rosa, J. Ugander, and S. Vigna, “Four degrees of separation,” in Proc. 3rd Annual ACM Web Science Conf.-WebSci, ACM Press, 2012.
    L. A. N. Amaral, A. Scala, M. Barthelemy, and H. E. Stanley, “Classes of small-world networks,” Proc. Natl. Acad. Sci. USA, vol. 97, no. 21, pp. 11149–11152, Sep. 2000. doi: 10.1073/pnas.200327197
    S. Abe and N. Suzuki, “Scale-free network of earthquakes,” Europhys. Lett., vol. 65, no. 4, pp. 581–586, Feb. 2004. doi: 10.1209/epl/i2003-10108-1
    A. Clauset, M. Young, and K. S. Gleditsch, “On the frequency of severe terrorist events,” J. Confl. Resolut., vol. 51, no. 1, pp. 58–87, Feb. 2007. doi: 10.1177/0022002706296157
    A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan, R. Stata, A. Tomkins, and J. Wiener, “Graph structure in the web,” Computer Networks, vol. 33, no. 1–6, pp. 309–320, Jun. 2000. doi: 10.1016/S1389-1286(00)00083-9
    A. Clauset, C. R. Shalizi, and M. E. J. Newman, “Power-law distributions in empirical data,” SIAM Rev., vol. 51, no. 4, pp. 661–703, Nov. 2009. doi: 10.1137/070710111
    A. D. Broido and A. Clauset, “Scale-free networks are rare,” Nat. Commun., vol. 10, no. 1, p. 1017, Mar. 2019.
    I. Voitalov, P. van der Hoorn, R. van der Hofstad, and D. Krioukov, “Scale-free networks well done,” Phys. Rev. Res., vol. 1, no. 3, p. 033034, Oct. 2019.
    M. D. Domenico, A. Solé-Ribalta, E. Cozzo, M. Kivelä, Y. Moreno, M. A. Porter, S. Gómez, and A. Arenas, “Mathematical formulation of multilayer networks,” Phys. Rev. X, vol. 3, no. 4, p. 041022, Dec. 2013.
    S. Gómez, A. Díaz-Guilera, J. Gómez-Gardeñes, C. J. Pérez-Vicente, Y. Moreno, and A. Arenas, “Diffusion dynamics on multiplex networks,” Phys. Rev. Lett., vol. 110, no. 2, p. 028701, Jan. 2013.
    A. Solé-Ribalta, M. D. Domenico, N. E. Kouvaris, A. Díaz-Guilera, S. Gómez, and A. Arenas, “Spectral properties of the laplacian of multiplex networks,” Phys. Rev. E, vol. 88, no. 3, p. 032807, Sep. 2013.
    L. Tang, J. Lu, and J. H. Lü, “A threshold effect of coupling delays on intra-layer synchronization in duplex networks,” Science China: Technological Sciences, vol. 61, no. 12, pp. 1907–1914, Dec. 2018. doi: 10.1007/s11431-017-9285-7
    F. D. Rossa, L. Pecora, K. Blaha, A. Shirin, I. Klickstein, and F. Sorrentino, “Symmetries and cluster synchronization in multilayer networks,” Nat. Commun., vol. 11, no. 1, p. 3179, Jun. 2020.
    S. Osat, A. Faqeeh, and F. Radicchi, “Optimal percolation on multiplex networks,” Nat. Commun., vol. 8, no. 1, p. 1540, Nov. 2017.
    X. Liu, E. Maiorino, A. Halu, K. Glass, R. B. Prasad, J. Loscalzo, J. Gao, and A. Sharma, “Robustness and lethality in multilayer biological molecular networks,” Nat. Commun., vol. 11, no. 1, p. 6043, Nov. 2020.
    L. Tang, X. Wu, J. Lü, J. A. Lu, and R. M. D’Souza, “Master stability functions for complete, intralayer, and interlayer synchronization in multiplex networks of coupled róssler oscillators,” Phys. Rev. E, vol. 99, no. 1, p. 012304, Jan. 2019.
    J. Grilli, G. Barabás, M. J. Michalska-Smith, and S. Allesina, “Higher-order interactions stabilize dynamics in competitive network models,” Nature, vol. 548, no. 7666, pp. 210–213, Jul. 2017. doi: 10.1038/nature23273
    I. Iacopini, G. Petri, A. Barrat, and V. Latora, “Simplicial models of social contagion,” Nat. Commun., vol. 10, no. 1, p. 2485, Jun. 2019.
    T. Tanaka and T. Aoyagi, “Multistable attractors in a network of phase oscillators with three-body interactions,” Phys. Rev. Lett., vol. 106, no. 22, p. 224101, May. 2011.
    P. S. Skardal and A. Arenas, “Abrupt desynchronization and extensive multistability in globally coupled oscillator simplexes,” Phys. Rev. Lett., vol. 122, no. 24, p. 248301, Jun. 2019.
    L. V. Gambuzza, F. D. Patti, L. Gallo, S. Lepri, M. Romance, R. Criado, M. Frasca, V. Latora, and S. Boccaletti, “Stability of synchronization in simplicial complexes,” Nat. Commun., vol. 12, no. 1, p. 1255, Feb. 2021.
    J. Hoffart, F. M. Suchanek, K. Berberich, and G. Weikum, “Yago2: A spatially and temporally enhanced knowledge base from Wikipedia,” Artif. Intell., vol. 194, pp. 28–61, Jan. 2013. doi: 10.1016/j.artint.2012.06.001
    W. Wu, H. Li, H. Wang, and K. Q. Zhu, “Probase: A probabilistic taxonomy for text understanding,” in Proc. ACM SIGMOD Int. Conf. Management of Data, 2012, pp. 481–492.
    X. Niu, X. Sun, H. Wang, S. Rong, G. Qi, and Y. Yu, “Zhishi.me-weaving Chinese linking open data,” in Proc. 10th Int. Semantic Web Conf., 2011, pp. 205–220.
    B. Xu, Y. Xu, J. Liang, C. Xie, B. Liang, W. Cui, and Y. Xiao, “CN-DBpedia: A never-ending chinese knowledge extraction system,” in Proc. 30th Int. Conf. Industrial, Engineering and Other Applications of Applied Intelligent Systems, 2017, pp. 428–438.
    F. Zhang, N. J. Yuan, D. Lian, X. Xie, and W.-Y. Ma, “Collaborative knowledge base embedding for recommender systems,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016, pp. 353–362.
    Q. Wang, Z. Mao, B. Wang, and L. Guo, “Knowledge graph embedding: A survey of approaches and applications,” IEEE Trans. Know. Data Eng., vol. 29, no. 12, pp. 2724–2743, Sep. 2017. doi: 10.1109/TKDE.2017.2754499
    X. Chen, S. Jia, and Y. Xiang, “A review: Knowledge reasoning over knowledge graph,” Expert Syst. Appl., vol. 141, p. 112948, Mar. 2020.
    A. P. Quimbaya, A. S. Múnera, R. A. G. Rivera, J. C. D. Rodríguez, O. M. M. Velandia, A. A. G. Peña, and C. Labbé, “Named entity recognition over electronic health records through a combined dictionary-based approach,” Procedia Comput. Sci., vol. 100, pp. 55–61, Oct. 2016. doi: 10.1016/j.procs.2016.09.123
    X. Liu, S. Zhang, F. Wei, and M. Zhou, “Recognizing named entities in tweets,” in Proc. 49th Annual Meeting Association for Computational Linguistics: Human Language Technologies, 2011, pp. 359–367.
    A. Jain and M. Pennacchiotti, “Open entity extraction from web search query logs,” in Proc. of Int. Conf. Computational Linguistics, 2010, pp. 510–518.
    S. Singh, S. Riedel, B. Martin, J. Zheng, and A. McCallum, “Joint inference of entities, relations, and coreference,” in Proc. Workshop on Automated Knowledge Base Construction, 2013, pp. 1–6.
    M. Miwa and M. Bansal, “End-to-end relation extraction using LSTMs on sequences and tree structures,” in Proc. 54th Annual Meeting Association for Computational Linguistics, 2016, pp. 1105–1116.
    K.-W. Chang, W. T. Yih, B. Yang, and C. Meek, “Typed tensor decomposition of knowledge bases for relation extraction,” in Proc. Conf. Empirical Methods in Natural Language Processing, 2014, pp. 1568–1579.
    X. Zhao, Y. Jia, A. Li, R. Jiang, and Y. Song, “Multi-source knowledge fusion: A survey,” World Wide Web, vol. 23, no. 4, pp. 2567–2592, Apr. 2020. doi: 10.1007/s11280-020-00811-0
    P. Sen, “Collective context-aware topic models for entity disambiguation,” in Proc. 21st Int. Conf. World Wide Web, 2012, pp. 729–738.
    G. Zhu and C. A. Iglesias, “Exploiting semantic similarity for named entity disambiguation in knowledge graphs,” Expert Syst. Appl., vol. 101, pp. 8–24, Jul. 2018. doi: 10.1016/j.eswa.2018.02.011
    A. Alokaili and M. E. B. Menai, “SVM ensembles for named entity disambiguation,” Computing, vol. 102, no. 4, pp. 1051–1076, Apr. 2020. doi: 10.1007/s00607-019-00748-x
    X. L. Dong, “Challenges and innovations in building a product knowledge graph,” in Proc. 24th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, 2018, pp. 2869–2869.
    O. Deshpande, D. S. Lamba, M. Tourn, S. Das, S. Subramaniam, A. Rajaraman, V. Harinarayan, and A. Doan, “Building, maintaining, and using knowledge bases: A report from the trenches,” in Proc. ACM SIGMOD Int. Conf. Management of Data, 2013, pp. 1209–1220.
    B. D. Trisedya, J. Qi, and R. Zhang, “Entity alignment between knowledge graphs using attribute embeddings,” in Proc. AAAI Conf. Artificial Intelligence, 2019, pp. 297–304.
    A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” in Proc. 26th Int. Conf. Neural Information Processing Systems, 2013, pp. 2787–2795.
    S. M. Kazemi and D. Poole, “Simple embedding for link prediction in knowledge graphs,” in Proc. 32nd Int. Conf. Neural Information Processing Systems, 2018, pp. 4289–4300.
    Z. Sun, Z.-H. Deng, J.-Y. Nie, and J. Tang, “Rotate: Knowledge graph embedding by relational rotation in complex space,” arXiv preprint arXiv: 1902.10197, 2019.
    S. Zhang, Y. Tay, L. Yao, and Q. Liu, “Quaternion knowledge graph embeddings,” in Proc. 33rd Int. Conf. Neural Information Processing Systems, 2019, pp. 2735–2748.
    M. Nickel, V. Tresp, and H.-P. Kriegel, “A three-way model for collective learning on multi-relational data,” in Proc. 28th Int. Conf. Machine Learning, 2011, pp. 809–816.
    T. Trouillon, J. Welbl, and S. Riedel, É. Gaussier, and G. Bouchard, “Complex embeddings for simple link prediction,” in Proc. 33rd Int. Conf. Machine Learning, 2016, pp. 2071–2080.
    W. Zhang, B. Paudel, W. Zhang, A. Bernstein, and H. Chen, “Interaction embeddings for prediction and explanation in knowledge graphs,” in Proc. 12th ACM Int. Conf. Web Search and Data Mining, 2019, pp. 96–104.
    N. Lao and W. W. Cohen, “Relational retrieval using a combination of path-constrained random walks,” Mach. Learn., vol. 81, no. 1, pp. 53–67, Jul. 2010. doi: 10.1007/s10994-010-5205-8
    W. Xiong, T. Hoang, and W. Y. Wang, “Deeppath: A reinforcement learning method for knowledge graph reasoning,” in Proc. Conf. Empirical Methods in Nature Language Processing, 2017, pp. 575–584.
    M. Kampffmeyer, Y. Chen, X. Liang, H. Wang, Y. Zhang, and E. P. Xing, “Rethinking knowledge graph propagation for zero-shot learning,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, 2019, pp. 11479–11488.
    A. Neelakantan, B. Roth, and A. McCallum, “Compositional vector space models for knowledge base inference,” in Proc. AAAI Spring Symposium Series, 2015, pp. 31–34.
    A. Graves, G. Wayne, M. Reynolds, T. Harley, I. Danihelka, A. Grabska-Barwińska, S. G. Colmenarejo, E. Grefenstette, T. Ramalho, J. Agapiou, Z. Jin, X.-D. Li, G. Huang, H. A. Muller, J. Pang, and L.-J. Zhang , “Hybrid computing using a neural network with dynamic external memory,” Nature, vol. 538, no. 7626, pp. 471–476, Oct. 2016. doi: 10.1038/nature20101
    S. Guo, B. Ding, Q. Wang, L. Wang, and B. Wang, “Knowledge base completion via rule-enhanced relational learning,” in Proc. China Conf. Knowledge Graph and Semantic Computing, 2016, pp. 219–227.
    W. Zhang, B. Paudel, L. Wang, J. Chen, H. Zhu, W. Zhang, A. Bernstein, and H. Chen, “Iteratively learning embeddings and rules for knowledge graph reasoning,” in Proc. World Wide Web Conf., 2019, pp. 2366–2377.
    P. N. Mendes, H. Mühleisen, and C. Bizer, “Sieve: Linked data quality assessment and fusion,” in Proc. Joint EDBT/ICDT Workshops, 2012, pp. 116–123.
    X. Dong, E. Gabrilovich, G. Heitz, W. Horn, N. Lao, K. Murphy, T. Strohmann, S. Sun, and W. Zhang, “Knowledge vault: A web-scale approach to probabilistic knowledge fusion,” in Proc. 20th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2014, pp. 601–610.
    H. Paulheim, “Knowledge graph refinement: A survey of approaches and evaluation methods,” Semant. Web, vol. 8, no. 3, pp. 489–508, Jan. 2017.
    B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in Proc. 20th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2014, pp. 701–710.
    Y. S. Abumostafa, M. Magdonismail, and H. T. Lin, Learning from data: A short course. Chicago: Amlbook, 2012.
    S. Sun, Z. Cao, H. Zhu, and J. Zhao, “A survey of optimization methods from a machine learning perspective,” IEEE Trans. Cybernetics, vol. 50, no. 8, pp. 3668–3681, Aug. 2020. doi: 10.1109/TCYB.2019.2950779
    W. S. Mcculloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bulletin Math. Biol., vol. 52, no. 1−2, pp. 99–115, Dec. 1943.
    D. O. Hebb, The Organization of Behavior: A Neuropsychological Theory. New York, USA: John Wiley and Sons, 1949.
    F. Rosenblatt, “The perceptron: A probabilistic model for information storage and organization in the brain,” Psychol. Rev., vol. 65, no. 6, pp. 386–408, 1958. doi: 10.1037/h0042519
    M. L. Minsky and S. A. Papert, Perceptrons: An Introduction to Computational Geometry. Cambridge, USA: MIT Press, 1969.
    T. Kohonen, Self-Organization and Associative Memory. Berlin Heidelberg, Germany: Springer-Verlag, 1984.
    J. Mcclelland, Information Processing in Dynamical Systems: Foundations of Harmony Theory. Cambridge, USA: MIT Press, 1986.
    G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, Jul. 2006. doi: 10.1126/science.1127647
    N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res., vol. 15, pp. 1929–1958, Jun. 2014.
    D. Yarotsky, “Error bounds for approximations with deep relu networks,” Neural Netw., vol. 94, pp. 103–114, Oct. 2017. doi: 10.1016/j.neunet.2017.07.002
    P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,” J. Mach. Learn. Res., vol. 11, pp. 3371–3408, Dec. 2010.
    A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, Jun. 2017. doi: 10.1145/3065386
    D. Silver, A. Huang, C. Maddison, A. Guez, L. Sifre, et al., “Mastering the game of go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484–489, Jan. 2016. doi: 10.1038/nature16961
    J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Netw., vol. 61, pp. 85–117, Jan. 2015. doi: 10.1016/j.neunet.2014.09.003
    D. H. Hubel and T. Wiesel, “Shape and arrangement of columns in cat’s striate cortex,” J. Physiol., vol. 165, no. 3, pp. 559–568, Mar. 1963. doi: 10.1113/jphysiol.1963.sp007079
    K. Fukushima, S. Miyake, and T. Ito, “Neocognitron: A neural network model for a mechanism of visual pattern recognition,” IEEE Trans. Syst. Man Cybern., vol. 5, pp. 826–834, Sept. 1983.
    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2016, pp. 770–778.
    A. Sengupta, Y. Ye, R. Wang, C. Liu, and K. Roy, “Going deeper in spiking neural networks: VGG and residual architectures,” Front. Neurosci., vol. 13, p. 95, Mar. 2019.
    S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997. doi: 10.1162/neco.1997.9.8.1735
    G. Wen, J. Qin, X. Fu, and W. Yu, “DLSTM: Distributed long short-term memory neural networks for the Internet of Things,” IEEE Trans. Netw. Sci. Eng., vol. 9, no. 1, pp. 111–120, Jan.–Feb. 2022. doi: 10.1109/TNSE.2021.3054244
    S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” in Proc. Advances Neural Infor. Processing Syst., 2015.
    C. Farabet, C. Couprie, L. Najman, and Y. LeCun, “Learning hierarchical features for scene labeling,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1915–1929, Aug. 2013. doi: 10.1109/TPAMI.2012.231
    A. Graves, et al., “A novel connectionist system for unconstrained handwriting recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 5, pp. 855–868, May 2009. doi: 10.1109/TPAMI.2008.137
    E. Mansimov, E. Parisotto, J. L. Ba, and R. Salakhutdinov, “Generating images from captions with attention,” in Proc. Int. Conf. Learning Representations, 2016.
    S. Valverde, et al., “Scale free networks from optimal design,” Europhysics Letters, vol. 60, pp. 512–517, 2002. doi: 10.1209/epl/i2002-00248-2
    C. R. Myers, “Software systems as complex networks: Structure, function, and evolvability of software collaboration graphs,” Phys. Rev. E, vol. 68, no. 4, p. 046116, 2003.
    K. He, Y. Ma, B. Li, J. Liu, and R. Peng, Software Network (in Chinese). Beijing, China: Science Press, 2008.
    G. Concas, M. Marchesi, S. Pinna, and N. Serra, “Power-laws in a large object-oriented software system,” IEEE Trans. Softw. Eng., vol. 33, no. 10, pp. 687–708, Oct. 2007. doi: 10.1109/TSE.2007.1019
    S. Valverde and R. Sole, “Universal properties of bipartite software graphs,” in Proc. 9th IEEE Int. Conf. Engineering of Complex Computer Systems, 2004.
    A. Begel, J. Bosch, and M.-A. Storey, “Social networking meets software development: Perspectives from github, msdn, stack exchange, and topcoder,” IEEE Softw., vol. 30, no. 1, pp. 52–66, 2013. doi: 10.1109/MS.2013.13
    F. Thung, T. F. Bissyandé, D. Lo, and L. Jiang, “Network structure of social coding in GitHub,” in Proc. 17th European Conf. Software Maintenance and Reengineering, 2013, pp. 323–326.
    H. Mei, “Internetware: Challenges and future direction of software paradigm for Internet as a computer,” in Proc. IEEE 34th Annual Computer Software and Applications Conf., 2010, pp. 14–16.
    T. Xie, et al., “Preface (Special section on software systems 2020),” J. Comput. Sci. Technol., vol. 35, no. 6, pp. 1231–1233, 2020. doi: 10.1007/s11390-020-0006-4
    H. Mei and X. Z. Liu, “Internetware: An emerging software paradigm for Internet computing,” J. Comput. Sci. Technol., vol. 26, no. 4, pp. 588–599, 2011. doi: 10.1007/s11390-011-1159-y
    X. Zhang and X. Liu, “Research on massiveness characteristics weights of big knowledge based on the big data,” in Proc. 7th Int. Conf. Information Science and Control Engineering, 2020, pp. 1178–1183.
    I.T. Koponen, “Modelling students’ thematically associated knowledge: Networked knowledge from affinity statistics,” in Complex Networks X., Springer International Publishing, Cham, Switzerland, 2019, pp. 123–134.
    S. Decker, S. Handschuh, and M. Hauswirth, “Towards networked knowledge,” in Foundations for the Web of Information and Services, Berlin Heidelberg, Germany: Springer, 2011, pp. 155–174.
    X. R. Lopez and M. Larsgaard, “Towards a California geospatial digital library: A strategy for networked knowledge,” Cartogr. Geogr. Inf. Sci., vol. 25, no. 3, pp. 133–141, Mar. 2013.
    Y. Lu, Z. Feng, S. Zhang, and Y. Wang, “Annotating regulatory elements by heterogeneous network embedding,” Bioinformatics, vol. 38, no. 10, pp. 2899–2911, May 2022. doi: 10.1093/bioinformatics/btac185
    E. Muñoz, V. Novácek, and P.-Y. Vandenbussche, “Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models,” Brief. Bioinform., vol. 20, no. 1, pp. 190–202, Jan. 2019. doi: 10.1093/bib/bbx099
    J. Borge-Holthoefer and A. Arenas, “Semantic networks: Structure and dynamics,” Entropy, vol. 12, no. 5, pp. 1264–1302, 2010. doi: 10.3390/e12051264
    H. Liu, “Statistical properties of Chinese semantic networks,” Chin. Sci. Bull., vol. 54, no. 16, pp. 2781–2785, 2009.
    M. Galkin, S. Auer, H. Kim and S. Scerri, “Integration strategies for enterprise knowledge graphs,” in Proc. IEEE 10th Int. Conf. Semantic Computing, 2016, pp. 242–245.
    A. Nesen and B. Bhargava, “Knowledge graphs for semantic-aware anomaly detection in video,” in Proc. IEEE 3rd Int. Conf. Artificial Intelligence and Knowledge Engineering, 2020, pp. 65–70.
    K. Rajaraman and A.-H. Tan, “Mining semantic networks for knowledge discovery,” in Proc. 3rd IEEE Int. Conf. Data Mining, 2003, pp. 633–636.
    W.-W. Luo and X.-Y. Chen, “A research on flexible business process management system based on knowledge base and semantic web services,” in Proc. Int. Conf. Electronic & Mechanical Engineering and Information Technology, 2011, pp. 4289–4292.
    J. Zhang, L. Tan, X. Tao, D. Wang, J. J. C. Ying, and X. Wang, “Learning relational fractals for deep knowledge graph embedding in online social networks,” in Proc. Int. Conf. Web Information Systems Engineering, 2020, pp. 660–674.
    Q. He, J. Yang, and B. Shi, “Constructing knowledge graph for social networks in a deep and holistic way,” in Proc. Companion Web Conf., 2020, pp. 307–308.
    H. V. Pham and D. N. Tien, “Hybrid louvain-clustering model using knowledge graph for improvement of clustering user’ behavior on social networks,” in Proc. Int. Conf. Intelligent Systems & Networks, 2021, pp. 126–133.
    H. Patel, P. Paraskevopoulos, and M. Renz, “GeoTeGra: A system for the creation of knowledge graph based on social network data with geographical and temporal information,” in Proc. IEEE/ACM Int. Conf. Advances in Social Networks Analysis and Mining, 2018, pp. 617–620.
    B. C. Molokwu and Z. Kobti, “Social network analysis using RLVECN: Representation learning via knowledge-graph embeddings and convolutional neural-network,” in Proc. 29th Int. Conf. International Joint Conf. Artificial Intelligence, 2021, pp. 5198–5199.
    B. Koloski, T. S. Perdih, M. Robnik-Šikonja, S. Pollak, B. Škrlj, “Knowledge graph informed fake news classification via heterogeneous representation ensembles,” Neurocomputing, 2022. DOI: 10.1016/j.neucom.2022.01.096
    M. Mayank, S. Sharma, R. Sharma, “DEAP-FAKED: Knowledge graph based approach for fake news detection,” arXiv preprint arXiv: 2107.10648, 2022.
    H. Huang, Z. Hong, H. Zhou, J. Wu, and N. Jin, “Knowledge graph construction and application of power grid equipment,” Math. Probl. Eng., vol. 2020, p. 8269082, Oct. 2000.
    G. Xiao, R. Meng, H. Xu, Z. Hong, W. Ping, R. Ru, and G. Feng, “Construction technology of knowledge graph and its application in power grid,” in Proc. E3S Web of Conf., 2021, vol. 256, p. 01039.
    W. Yuan, K. Zhang, Q. Dai, C. Peng, and K. Zhao, “Construction and application of knowledge graph in full-service unified data center of electric power system,” in Proc. IOP Conf. Series: Materials Science and Engineering, 2018, vol. 452, no. 3, p. 032065.
    W. Miao, H. Wu, P. Chen, and J. Jing, “Intelligent auxiliary operation and maintenance system of power communication network based on knowledge graph,” in Proc. J. Physics: Conf. Series, 2020, vol. 1684, no. 1, p. 012105.
    K. Huang, “Self-organized network of knowledge,” Ph.D. dissertation, Institute of Mathematics and System Science, Chinese Academy of Sciences, 2022 (in Chinese).
    A. Capocci, V. D. P. Servedio, F. Colaiori, L. S. Buriol, D. Donato, S. Leonardi, and G. Caldarelli, “Preferential attachment in the growth of social networks: The internet encyclopedia Wikipedia,” Phys. Rev. E, vol. 74, p. 036116, 2006.
    F. N. Silva, M. P. Viana, B. A. N. Travençolo, and L. da F. Costa, “Investigating relationships within and between category networks in Wikipedia,” J. Informetr., vol. 5, no. 3, pp. 431–438, 2011. doi: 10.1016/j.joi.2011.03.003
    L. da F. Costa, “Learning about knowledge: A complex network approach,” Phys. Rev. E, vol. 74, p. 026103, 2006.
    H. F. de Arruda, F. N. Silva, L. da F. Costa, and D. R. Amancio, “Knowledge acquisition: A complex networks approach,” Inf. Sci., vol. 421, pp. 154–166, 2017. doi: 10.1016/j.ins.2017.08.091
    T. S. Lima, H. F. de Arruda, F. N. Silva, C. H. Comin, D. R. Amancio, and L. da F. Costa, “The dynamics of knowledge acquisition via self-learning in complex networks,” Chaos, vol. 28, p. 083106, 2018.
    L. Guerreiro, F. N. Silva, and D. R. Amancio, “A comparative analysis of knowledge acquisition performance in complex networks,” Inf. Sci., vol. 555, pp. 46–57, 2021. doi: 10.1016/j.ins.2020.12.060
    D. R. Amancio, E. G Altmann, O. N. Oliveira Jr., and L. da F. Costa, “Comparing intermittency and network measurements of words and their dependence on authorship,” New J. Phys., vol. 13, p. 123024, 2011.
    A. Mehri, A. H. Darooneh, and A. Shariati, “The complex networks approach for authorship attribution of books,” Physica A, vol. 391, pp. 2429–2437, 2012. doi: 10.1016/j.physa.2011.12.011
    J. Cong and H. Liu, “Approaching human language with complex networks,” Phys. Life Rev., vol. 11, pp. 598–618, 2014. doi: 10.1016/j.plrev.2014.04.004
    C. Akimushkin, D. R. Amancio, and O. N. Oliveira Jr., “Text authorship identified using the dynamics of word co-occurrence networks,” PloS One, vol. 12, no. 1, p. e0170527, 2017.
    C. Akimushkin, D. R. Amancio, and O. N. Oliveira Jr., “On the role of words in the network structure of texts: Application to authorship attribution,” Physica A, vol. 495, pp. 49–58, 2018. doi: 10.1016/j.physa.2017.12.054


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    • The state-of-the-art advances of complex networks and deep learning were briefly reviewed
    • A new framework of networked knowledge was suggested from the perspective of complex networks
    • Deep learning technologies for networked knowledge were reviewed and analyzed


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