{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:28:17Z","timestamp":1743100097652,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031264184"},{"type":"electronic","value":"9783031264191"}],"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\/978-3-031-26419-1_33","type":"book-chapter","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T00:24:57Z","timestamp":1679876697000},"page":"549-564","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CGPM: Poverty Mapping Framework Based on\u00a0Multi-Modal Geographic Knowledge Integration and\u00a0Macroscopic Social Network Mining"],"prefix":"10.1007","author":[{"given":"Zhao","family":"Geng","sequence":"first","affiliation":[]},{"given":"Gao","family":"Ziqing","sequence":"additional","affiliation":[]},{"given":"Tsai","family":"Chihsu","sequence":"additional","affiliation":[]},{"given":"Lu","family":"Jiamin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"33_CR1","doi-asserted-by":"crossref","unstructured":"Ayush, K., Uzkent, B., Tanmay, K., Burke, M., Lobell, D., Ermon, S.: Efficient poverty mapping from high resolution remote sensing images. In: Proceedings of AAAI Conference on Artificial Intelligence, vol. 35, pp. 12\u201320 (2021)","DOI":"10.1609\/aaai.v35i1.16072"},{"issue":"4","key":"33_CR2","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1111\/meca.12297","volume":"71","author":"B Belhadj","year":"2020","unstructured":"Belhadj, B., Kaabi, F.: New membership function for poverty measure. Metroeconomica 71(4), 676\u2013688 (2020)","journal-title":"Metroeconomica"},{"issue":"1","key":"33_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1140\/epjds\/s13688-020-00235-w","volume":"9","author":"M Fatehkia","year":"2020","unstructured":"Fatehkia, M., et al.: Mapping socioeconomic indicators using social media advertising data. EPJ Data Sci. 9(1), 1\u201315 (2020). https:\/\/doi.org\/10.1140\/epjds\/s13688-020-00235-w","journal-title":"EPJ Data Sci."},{"issue":"2","key":"33_CR4","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1111\/roiw.12498","volume":"67","author":"S Flechtner","year":"2021","unstructured":"Flechtner, S.: Poverty research and its discontents: review and discussion of issues raised in dimensions of poverty. measurement, epistemic injustices and social activism. Rev. Income Wealth 67(2), 530\u2013544 (2021). (beck, v., h. hahn, and r. lepenies eds., springer, cham, 2020)","journal-title":"Rev. Income Wealth"},{"key":"33_CR5","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"33_CR6","doi-asserted-by":"crossref","unstructured":"Htet, N.L., Kongprawechnon, W., Thajchayapong, S., Isshiki, T.: Machine learning approach with multiple open-source data for mapping and prediction of poverty in myanmar. In: 2021 18th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 1041\u20131045. IEEE (2021)","DOI":"10.1109\/ECTI-CON51831.2021.9454768"},{"key":"33_CR7","first-page":"102694","volume":"107","author":"S Hu","year":"2022","unstructured":"Hu, S., Ge, Y., Liu, M., Ren, Z., Zhang, X.: Village-level poverty identification using machine learning, high-resolution images, and geospatial data. Int. J. Appl. Earth Obs. Geoinf. 107, 102694 (2022)","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"33_CR8","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: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 66\u201374 (2020)","DOI":"10.1145\/3394486.3403049"},{"key":"33_CR9","unstructured":"Ledesma, C., Garonita, O.L., Flores, L.J., Tingzon, I., Dalisay, D.: Interpretable poverty mapping using social media data, satellite images, and geospatial information. arXiv preprint arXiv:2011.13563 (2020)"},{"key":"33_CR10","unstructured":"Lee, K., Braithwaite, J.: High-resolution poverty maps in sub-saharan africa. arXiv preprint arXiv:2009.00544 (2020)"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Liu, F., Wu, X., Ge, S., Fan, W., Zou, Y.: Federated learning for vision-and-language grounding problems. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 11572\u201311579 (2020)","DOI":"10.1609\/aaai.v34i07.6824"},{"key":"33_CR12","doi-asserted-by":"publisher","first-page":"113054","DOI":"10.1016\/j.cam.2020.113054","volume":"405","author":"S Mart\u00ednez","year":"2020","unstructured":"Mart\u00ednez, S., Rueda, M., Illescas, M.: The optimization problem of quantile and poverty measures estimation based on calibration. J. Comput. Appl. Math. 405, 113054 (2020)","journal-title":"J. Comput. Appl. Math."},{"key":"33_CR13","unstructured":"Pilco, D.S., Rivera, A.R.: Graph learning network: a structure learning algorithm. arXiv preprint arXiv:1905.12665 (2019)"},{"key":"33_CR14","doi-asserted-by":"publisher","first-page":"115377","DOI":"10.1016\/j.eswa.2021.115377","volume":"183","author":"H Roghani","year":"2021","unstructured":"Roghani, H., Bouyer, A., Nourani, E.: PLDLS: a novel parallel label diffusion and label selection-based community detection algorithm based on spark in social networks. Expert Syst. Appl. 183, 115377 (2021)","journal-title":"Expert Syst. Appl."},{"key":"33_CR15","doi-asserted-by":"crossref","unstructured":"Sheehan, E., et al.: Predicting economic development using geolocated Wikipedia articles. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2698\u20132706 (2019)","DOI":"10.1145\/3292500.3330784"},{"issue":"127","key":"33_CR16","doi-asserted-by":"publisher","first-page":"20160690","DOI":"10.1098\/rsif.2016.0690","volume":"14","author":"JE Steele","year":"2017","unstructured":"Steele, J.E., et al.: Mapping poverty using mobile phone and satellite data. J. Roy. Soc. Interface 14(127), 20160690 (2017)","journal-title":"J. Roy. Soc. Interface"},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"Tang, J., Qian, T., Liu, S., Du, S., Hu, J., Li, T.: Spatio-temporal latent graph structure learning for traffic forecasting. arXiv preprint arXiv:2202.12586 (2022)","DOI":"10.1109\/IJCNN55064.2022.9892191"},{"key":"33_CR18","unstructured":"Thornton, P., et al.: Mapping poverty and livestock in the developing world, vol. 1. ILRI (aka ILCA and ILRAD) (2002)"},{"key":"33_CR19","doi-asserted-by":"crossref","unstructured":"Tingzon, I., et al.: Mapping poverty in the Philippines using machine learning, satellite imagery, and crowd-sourced geospatial information. In: AI for Social Good ICML 2019 Workshop (2019)","DOI":"10.5194\/isprs-archives-XLII-4-W19-425-2019"},{"key":"33_CR20","unstructured":"Vashishth, S., Sanyal, S., Nitin, V., Talukdar, P.: Composition-based multi-relational graph convolutional networks. arXiv preprint arXiv:1911.03082 (2019)"},{"issue":"2","key":"33_CR21","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1109\/TNNLS.2020.2979607","volume":"32","author":"L Wang","year":"2020","unstructured":"Wang, L., Chan, R., Zeng, T.: Probabilistic semi-supervised learning via sparse graph structure learning. IEEE Transa. Neural Netw. Learn. Syst. 32(2), 853\u2013867 (2020)","journal-title":"IEEE Transa. Neural Netw. Learn. Syst."},{"issue":"4","key":"33_CR22","first-page":"369","volume":"48","author":"D Watson","year":"2017","unstructured":"Watson, D., Whelan, C.T., Ma\u00eetre, B., Williams, J.: Non-monetary indicators and multiple dimensions: the ESRI approach to poverty measurement. Econ. Soc. Rev. 48(4), 369\u2013392 (2017)","journal-title":"Econ. Soc. Rev."},{"key":"33_CR23","doi-asserted-by":"crossref","unstructured":"Xie, M., Jean, N., Burke, M., Lobell, D., Ermon, S.: Transfer learning from deep features for remote sensing and poverty mapping. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)","DOI":"10.1609\/aaai.v30i1.9906"},{"key":"33_CR24","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)"},{"key":"33_CR25","unstructured":"Ying, C., et al.: Do transformers really perform badly for graph representation? In: Advances in Neural Information Processing Systems, vol. 34 (2021)"},{"key":"33_CR26","doi-asserted-by":"publisher","first-page":"118382","DOI":"10.1016\/j.jclepro.2019.118382","volume":"241","author":"H Zhang","year":"2019","unstructured":"Zhang, H., Xu, Z., Wu, K., Zhou, D., Wei, G.: Multi-dimensional poverty measurement for photovoltaic poverty alleviation areas: evidence from pilot counties in china. J. Cleaner Prod. 241, 118382 (2019)","journal-title":"J. Cleaner Prod."},{"key":"33_CR27","unstructured":"Zhu, Y., et al.: A survey on graph structure learning: progress and opportunities (2021)"},{"key":"33_CR28","unstructured":"Zhu, Y., Xu, W., Zhang, J., Liu, Q., Wu, S., Wang, L.: Deep graph structure learning for robust representations: A survey. arXiv preprint arXiv:2103.03036 (2021)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26419-1_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T01:53:08Z","timestamp":1729129988000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26419-1_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031264184","9783031264191"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26419-1_33","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":"17 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.ecmlpkdd.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1060","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":"236","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":"22% - 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":"3-4","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":"3-4","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)"}},{"value":"17 demo track papers have been accepted from 28 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}