{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T03:05:01Z","timestamp":1743131101030,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031306747"},{"type":"electronic","value":"9783031306754"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-30675-4_6","type":"book-chapter","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T10:02:24Z","timestamp":1681466544000},"page":"75-92","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Subgraph Reconstruction via\u00a0Reversible Subgraph Embedding"],"prefix":"10.1007","author":[{"given":"Boyu","family":"Yang","sequence":"first","affiliation":[]},{"given":"Weiguo","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,15]]},"reference":[{"issue":"4","key":"6_CR1","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1016\/j.sigpro.2011.10.012","volume":"92","author":"V Abolghasemi","year":"2012","unstructured":"Abolghasemi, V., Ferdowsi, S., Sanei, S.: A gradient-based alternating minimization approach for optimization of the measurement matrix in compressive sensing. Signal Process. 92(4), 999\u20131009 (2012)","journal-title":"Signal Process."},{"key":"6_CR2","unstructured":"Abu-El-Haija, S., Perozzi, B., Al-Rfou, R., Alemi, A.A.: Watch your step: learning node embeddings via graph attention. In: NeurIPS, pp. 9198\u20139208 (2018)"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Adhikari, B., Zhang, Y., Ramakrishnan, N., Prakash, B.A.: Sub2vec: feature learning for subgraphs. In: PAKDD, pp. 170\u2013182 (2018)","DOI":"10.1007\/978-3-319-93037-4_14"},{"issue":"1","key":"6_CR4","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/T-C.1974.223784","volume":"100","author":"N Ahmed","year":"1974","unstructured":"Ahmed, N., Natarajan, T., Rao, K.R.: Discrete cosine transform. IEEE Trans. Comput. 100(1), 90\u201393 (1974)","journal-title":"IEEE Trans. Comput."},{"key":"6_CR5","unstructured":"Bai, Y., Xu, D., Sun, Y., Wang, W.: GLSearch: maximum common subgraph detection via learning to search. In: ICML, vol. 139, pp. 588\u2013598 (2021)"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Balalau, O., Goyal, S.: SubRank: subgraph embeddings via a subgraph proximity measure. In: PAKDD, pp. 487\u2013498 (2020)","DOI":"10.1007\/978-3-030-47426-3_38"},{"issue":"12","key":"6_CR7","doi-asserted-by":"publisher","first-page":"4203","DOI":"10.1109\/TIT.2005.858979","volume":"51","author":"EJ Candes","year":"2005","unstructured":"Candes, E.J., Tao, T.: Decoding by linear programming. IEEE Trans. Inf. Theory 51(12), 4203\u20134215 (2005)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Fey, M., Lenssen, J.E., Weichert, F., M\u00fcller, H.: SplineCNN: fast geometric deep learning with continuous b-spline kernels. In: CVPR, pp. 869\u2013877 (2018)","DOI":"10.1109\/CVPR.2018.00097"},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Ge, Y., Bertozzi, A.L.: Active learning for the subgraph matching problem. In: Big Data, pp. 2641\u20132649 (2021)","DOI":"10.1109\/BigData52589.2021.9671760"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: SIGKDD, pp. 855\u2013864 (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Hao, Z., et al.: ASGN: an active semi-supervised graph neural network for molecular property prediction. In: SIGKDD, pp. 731\u2013752. ACM (2020)","DOI":"10.1145\/3394486.3403117"},{"key":"6_CR12","unstructured":"Huang, K., Zitnik, M.: Graph meta learning via local subgraphs. In: NeurIPS (2020)"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Iwata, Y., Shigemura, T.: Separator-based pruned dynamic programming for steiner tree. In: AAAI, pp. 1520\u20131527 (2019)","DOI":"10.1609\/aaai.v33i01.33011520"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Izadi, M.R., Fang, Y., Stevenson, R., Lin, L.: Optimization of graph neural networks with natural gradient descent. arXiv preprint arXiv:2008.09624 (2020)","DOI":"10.1109\/BigData50022.2020.9378063"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Izadi, M.R., Fang, Y., Stevenson, R., Lin, L.: Optimization of graph neural networks with natural gradient descent. CoRR abs\/2008.09624 (2020)","DOI":"10.1109\/BigData50022.2020.9378063"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Jin, W., Ma, Y., Liu, X., Tang, X., Wang, S., Tang, J.: Graph structure learning for robust graph neural networks. In: SIGKDD, pp. 66\u201374. ACM (2020)","DOI":"10.1145\/3394486.3403049"},{"key":"6_CR17","unstructured":"Kim, D., Oh, A.: Efficient representation learning of subgraphs by subgraph-to-node translation. CoRR abs\/2204.04510 (2022)"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Leskovec, J., Kleinberg, J., Faloutsos, C.: Graph evolution: densification and shrinking diameters. TKDD 1(1), 2-es (2007)","DOI":"10.1145\/1217299.1217301"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Ou, M., Cui, P., Pei, J., Zhang, Z., Zhu, W.: Asymmetric transitivity preserving graph embedding. In: SIGKDD, pp. 1105\u20131114 (2016)","DOI":"10.1145\/2939672.2939751"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: SIGKDD, pp. 701\u2013710 (2014)","DOI":"10.1145\/2623330.2623732"},{"issue":"3","key":"6_CR21","first-page":"93","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93\u201393 (2008)","journal-title":"AI Mag."},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Sun, S., Luo, Q.: In-memory subgraph matching: an in-depth study. In: SIGMOD, pp. 1083\u20131098. ACM (2020)","DOI":"10.1145\/3318464.3380581"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: WWW, pp. 1067\u20131077 (2015)","DOI":"10.1145\/2736277.2741093"},{"key":"6_CR24","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: ICLR, OpenReview.net (2018)"},{"key":"6_CR25","unstructured":"Wang, C., Liu, Z.: Graph representation learning by ensemble aggregating subgraphs via mutual information maximization. CoRR abs\/2103.13125 (2021)"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhang, Y., Qin, L., Wang, W., Zhang, W., Lin, X.: Reinforcement learning based query vertex ordering model for subgraph matching, pp. 245\u2013258 (2022)","DOI":"10.1109\/ICDE53745.2022.00023"},{"key":"6_CR27","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: ICLR (2018)"},{"key":"6_CR28","doi-asserted-by":"crossref","unstructured":"Yin, Y., Wei, Z.: Scalable graph embeddings via sparse transpose proximities. In: SIGKDD, pp. 1429\u20131437 (2019)","DOI":"10.1145\/3292500.3330860"},{"key":"6_CR29","unstructured":"Zhang, J.: Segmented graph-BERT for graph instance modeling. arXiv preprint arXiv:2002.03283 (2020)"},{"key":"6_CR30","unstructured":"Zhang, J., Meng, L.: Gresnet: graph residual network for reviving deep gnns from suspended animation. CoRR abs\/1909.05729 (2019)"},{"key":"6_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Cui, P., Wang, X., Pei, J., Yao, X., Zhu, W.: Arbitrary-order proximity preserved network embedding. In: SIGKDD, pp. 2778\u20132786 (2018)","DOI":"10.1145\/3219819.3219969"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30675-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T12:07:32Z","timestamp":1710245252000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30675-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031306747","9783031306754"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30675-4_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","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":"17 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.tjudb.cn\/dasfaa2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"652","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":"125","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":"66","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":"19% - 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":"7.3","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)"}}]}}