{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T20:11:13Z","timestamp":1742933473651,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":46,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819707973"},{"type":"electronic","value":"9789819707980"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-0798-0_11","type":"book-chapter","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T08:03:04Z","timestamp":1709193784000},"page":"174-190","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Graph Neural Network for Critical Class Identification in Software System"],"prefix":"10.1007","author":[{"given":"Meng-Yi","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Peng","family":"He","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"key":"11_CR1","unstructured":"Xiao, P.: Analysis and exploration of software testing technology. Comput. CD Softw. Appl. 18(02), 44\u201345 (2015)"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Wang, S., Liu, T., Nam, J., et al.: Deep semantic feature learning for software defect prediction. IEEE Trans. Softw. Eng. 46, 1267\u20131293 (2018)","DOI":"10.1109\/TSE.2018.2877612"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Li, J., He, P., Zhu, J., et al.: Software defect prediction via convolutional neural network. In: 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS), pp. 318\u2013328. IEEE (2017)","DOI":"10.1109\/QRS.2017.42"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Qu, Y., Liu, T., Chi, J., et al.: node2defect: using network embedding to improve software defect prediction. In: 2018 33rd IEEE\/ACM International Conference on Automated Software Engineering (ASE), pp. 844\u2013849. IEEE (2018)","DOI":"10.1145\/3238147.3240469"},{"key":"11_CR5","unstructured":"Huang, C., Liu, X., Deng, M., et al.: A survey on algorithms for epidemic source identification on complex networks. Chin. J. Comput. 41(06), 1156\u20131179 (2018)"},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.chaos.2018.12.017","volume":"119","author":"KA Kabir","year":"2019","unstructured":"Kabir, K.A., Kuga, K., Tanimoto, J.: Analysis of SIR epidemic model with information spreading of awareness. Chaos Solitons Fractals 119, 118\u2013125 (2019)","journal-title":"Chaos Solitons Fractals"},{"key":"11_CR7","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1038\/s41591-020-1036-8","volume":"26","author":"JA Firth","year":"2020","unstructured":"Firth, J.A., Hellewell, J., Klepac, P., et al.: Using a real-world network to model localized Covid-19 control strategies. Nat. Med. 26, 1616\u20131622 (2020)","journal-title":"Nat. Med."},{"issue":"1","key":"11_CR8","doi-asserted-by":"publisher","first-page":"183","DOI":"10.3233\/IDA-150800","volume":"20","author":"H Rahmani","year":"2016","unstructured":"Rahmani, H., Blockeel, H., Bender, A.: Using a human drug network for generating novel hypotheses about drugs. Intell. Data Anal. 20(1), 183\u2013197 (2016)","journal-title":"Intell. Data Anal."},{"key":"11_CR9","unstructured":"Gu, Q., Ju, C., Wu, G.: Knowledge communication model of social network with user cooperation and leadership encouragement. Telecommun. Sci. 36(10), 172\u2013182 (2020)"},{"issue":"01","key":"11_CR10","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1142\/S1793005706000294","volume":"2","author":"K Yada","year":"2006","unstructured":"Yada, K., Motoda, H., Washio, T., Miyawaki, A.: Consumer behavior analysis by graph mining technique. New Math. Natural Comput. 2(01), 59\u201368 (2006)","journal-title":"New Math. Natural Comput."},{"key":"11_CR11","unstructured":"Ma, S., Liu, J., Zuo, X.: Survey on graph neural network. J. Comput. Res. Develop. 59(01), 47\u201380 (2022)"},{"issue":"2","key":"11_CR12","first-page":"193","volume":"32","author":"W Zhan","year":"2011","unstructured":"Zhan, W., Guan, J., Zhang, Z.: Advance in the research of complex network: model and application. J. Chin. Comput. Syst. 32(2), 193\u2013202 (2011)","journal-title":"J. Chin. Comput. Syst."},{"key":"11_CR13","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1038\/30918","volume":"393","author":"DJ Watts","year":"1998","unstructured":"Watts, D.J., Strogatz, S.H.: Collective dynamics of \u2018small-world\u2019 networks. Nature 393, 440\u2013442 (1998)","journal-title":"Nature"},{"key":"11_CR14","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1126\/science.286.5439.509","volume":"286","author":"AL Barab\u00e1si","year":"1999","unstructured":"Barab\u00e1si, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509\u2013512 (1999)","journal-title":"Science"},{"key":"11_CR15","doi-asserted-by":"publisher","unstructured":"Li, X., Chen, G.R.: A local-world evolving network model. Physica A: Stat. Mech. Appl. 328(1\u20132), 274\u2013286 (2003). https:\/\/doi.org\/10.1016\/s0378-4371(03)00604-6]","DOI":"10.1016\/s0378-4371(03)00604-6"},{"key":"11_CR16","doi-asserted-by":"publisher","unstructured":"Yook, S.H., Jeong, H., Barabasi, A.L., et al.: Weighted evolving networks. Phys. Rev. Lett. 86(25), 5835\u20135838 (2001). https:\/\/doi.org\/10.1103\/PhysRevLett.86.5835]","DOI":"10.1103\/PhysRevLett.86.5835"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Wu, Z., Chen, Y.: Link prediction using matrix factorization with bagging. In: 2016 IEEE\/ACIS 15th International Conference on Computer and Information Science (ICIS). IEEE (2016)","DOI":"10.1109\/ICIS.2016.7550942"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Koene, J.: Applied network analysis : a methodological introduction. North-Holland 17(3), 422\u2013423 (1984)","DOI":"10.1016\/0377-2217(84)90146-2"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Chen, D.B, Lul, Y., Shang, M.S., et al.: Identifying influential nodes in complex networks. Physica A: Stat. Mech. Appl. 391(4), 1777\u20131787 (2012)","DOI":"10.1016\/j.physa.2011.09.017"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215\u2013239 (1978\u20131979)","DOI":"10.1016\/0378-8733(78)90021-7"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Wang, J., Ai, J., Yang. Y., et al.: Identifying key classes of object-oriented software based on software complex network. In: International Conference on System Reliability & Safety, pp. 444\u2013449. IEEE (2017)","DOI":"10.1109\/ICSRS.2017.8272862"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Newman Me, J.: A measure of betweenness centrality based on random walks. Soc. Netw. 27(1), 39\u201354 (2005)","DOI":"10.1016\/j.socnet.2004.11.009"},{"issue":"11","key":"11_CR23","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1038\/nphys1746","volume":"6","author":"M Kitsak","year":"2010","unstructured":"Kitsak, M., Gallos, L.K., Havlin, S., et al.: Identification of influential spreaders in complex networks. Nat. Phys. 6(11), 888\u2013893 (2010)","journal-title":"Nat. Phys."},{"issue":"5","key":"11_CR24","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1145\/324133.324140","volume":"46","author":"JM Kleinberg","year":"1999","unstructured":"Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604\u2013632 (1999)","journal-title":"J. ACM"},{"issue":"18","key":"11_CR25","doi-asserted-by":"publisher","first-page":"3825","DOI":"10.1016\/j.comnet.2012.10.007","volume":"56","author":"S Brin","year":"2012","unstructured":"Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. 56(18), 3825\u20133833 (2012)","journal-title":"Comput. Netw."},{"issue":"3","key":"11_CR26","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.socnet.2010.03.006","volume":"32","author":"T Opsahl","year":"2010","unstructured":"Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: generalizing degree and shortest paths. Soc. Netw. 32(3), 245\u2013251 (2010)","journal-title":"Soc. Netw."},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Osman, M.H., Chaudron, M.R.V., Putten, P.V.D.: An analysis of machine learning algorithms for condensing reverse engineered class diagrams. In: Proceedings of the 2013 IEEE International Conference on Software Maintenance (ICSM 2013), Eindhoven, The Netherlands, pp. 140\u2013149 (2013)","DOI":"10.1109\/ICSM.2013.25"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Li, Q., Zhou, T., L\u00fcL, C.D.: Identifying influential spreaders by weighted LeaderRank. Phys A 404, 47\u201355 (2014)","DOI":"10.1016\/j.physa.2014.02.041"},{"key":"11_CR29","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.physa.2018.05.067","volume":"508","author":"L Yin","year":"2018","unstructured":"Yin, L., Deng, Y.: Toward uncertainty of weighted networks: an entropy-based model. Physica A 508, 176\u2013186 (2018)","journal-title":"Physica A"},{"issue":"4","key":"11_CR30","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1209\/epl\/i2002-00248-2","volume":"60","author":"S Valverde","year":"2002","unstructured":"Valverde, S., Cancho, R.F., Sol\u00e9, R.V.: Scale free networks from optimal design. Europhys. Lett. 60(4), 512\u2013517 (2002)","journal-title":"Europhys. Lett."},{"key":"11_CR31","unstructured":"Ding, Y.: Research on measurement method in open software ecosystem based on complex network. Wuhan University (2017)"},{"key":"11_CR32","unstructured":"Pan, W., Li, B., Ma, Y., et al.: Identifying the key packages using weighted PageRank algorithem. Acta Electronica Sinica 42(11), 2174\u20132183 (2014)"},{"key":"11_CR33","doi-asserted-by":"crossref","unstructured":"Singh, S., Jha, R.K.: A survey on software defined networking: architecture for next generation network. J. Netw. Syst. Manage. 25(2), 321\u2013374 (2017)","DOI":"10.1007\/s10922-016-9393-9"},{"key":"11_CR34","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s11334-017-0302-5","volume":"13","author":"SM Srinivasan","year":"2017","unstructured":"Srinivasan, S.M., Sangwan, R.S., Neill, C.J.: On the measures for ranking software components. Innovations Syst. Softw. Eng. 13, 161\u2013175 (2017)","journal-title":"Innovations Syst. Softw. Eng."},{"key":"11_CR35","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2019.2946357","author":"W Pan","year":"2019","unstructured":"Pan, W., Ming, H., Chang, C.K., Yang, Z., Kim, D.-K.: ElementRank: ranking Java Software classes and packages using multilayer complex network-based approach. IEEE Trans. Software Eng. (2019). https:\/\/doi.org\/10.1109\/TSE.2019.2946357","journal-title":"IEEE Trans. Software Eng."},{"key":"11_CR36","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.future.2017.10.006","volume":"81","author":"W Pan","year":"2018","unstructured":"Pan, W., Song, B., Li, K., Zhang, K.: Identifying key classes in object-oriented software using generalized k-core decomposition. Futur. Gener. Comput. Syst. 81, 188\u2013202 (2018)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"11_CR37","unstructured":"Zhang, J., Song, K., He, P., Li, B.: Identification of key classes in software systems based on graph neural networks. Comput. Sci. 48(12), 149\u2013158 (2021)"},{"key":"11_CR38","unstructured":"Ma, Y., Cheng, G., Liang, X., Li, Y., Yang, Y., Liu, Z.: Improved SDNE in weighted directed network. Comput. Sci. 47(04), 233\u2013237 (2020)"},{"key":"11_CR39","doi-asserted-by":"crossref","unstructured":"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\u2013710 (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"11_CR40","doi-asserted-by":"crossref","unstructured":"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\u2013864 (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"11_CR41","unstructured":"Figueiredo, D.R., Ribeiro, L.F.R., Saverese, P.H.P.: struc2vec: learning node representations from structural identity. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, pp. 13\u201317 (2017)"},{"key":"11_CR42","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of ICLR (2017)"},{"key":"11_CR43","unstructured":"Velikovi, P., Cucurull, G., Casanovam A., et al.: Graph Attention Networks (2017)"},{"key":"11_CR44","unstructured":"Kipf, T.N., Welling, M.: Variational graph auto-encoders. In: NIPS Workshop on Bayesian Deep Learning (2016)"},{"key":"11_CR45","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of NIPS, pp. 1024\u20131034 (2017)"},{"key":"11_CR46","doi-asserted-by":"crossref","unstructured":"Fan, C., Zeng, L., Ding, Y., et al.: Learning to identify high betweenness centrality nodes from scratch: a novel graph neural network approach. arXiv:1905.10418v1 (2019)","DOI":"10.1145\/3357384.3357979"}],"container-title":["Lecture Notes in Computer Science","Algorithms and Architectures for Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-0798-0_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T08:05:21Z","timestamp":1709193921000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-0798-0_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819707973","9789819707980"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-0798-0_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICA3PP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Algorithms and Architectures for Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ica3pp2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tjutanklab.com\/ica3pp2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Online submission system","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"439","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"145","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}