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In: NIPS, pp 41–50\ncitation_journal_title=J Algorithms; citation_title=Greedily finding a dense subgraph; citation_author=Y Asahiro, K Iwama, H Tamaki, T Tokuyama; citation_volume=34; citation_issue=2; citation_publication_date=2000; citation_pages=203-221; citation_doi=10.1006\u002Fjagm.1999.1062; citation_id=CR2\nBatagelj V, Zaversnik M (2003) An o(m) algorithm for cores decomposition of networks. arXiv preprint cs\u002F0310049\nBonchi F, Gullo F, Kaltenbrunner A, Volkovich Y (2014) Core decomposition of uncertain graphs. In: KDD, pp 1316–1325\nChang L, Yu JX, Qin L, Lin X, Liu C, Liang W (2013) Efficiently computing k-edge connected components via graph decomposition. In: SIGMOD, pp 205–216\nCharikar M (2000) Greedy approximation algorithms for finding dense components in a graph. In: APPROX, pp 84–95\nCheng J, Ke Y, Chu S, Tamer Özsu M (2011) Efficient core decomposition in massive networks. In: ICDE, pp 51–62\nCho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: KDD, pp 1082–1090\nConte A, Firmani D, Mordente C, Patrignani M, Torlone R (2017) Fast enumeration of large k-plexes. In: KDD, pp 115–124\ncitation_journal_title=Adv Phys; citation_title=Analyzing and modeling real-world phenomena with complex networks: a survey of applications; citation_author=L da Fontoura Costa, ON Oliveira, G Travieso, FA Rodrigues, PR Villas Boas, L Antiqueira, MP Viana, LE Correa Rocha; citation_volume=60; citation_issue=3; citation_publication_date=2011; citation_pages=329-412; citation_doi=10.1080\u002F00018732.2011.572452; citation_id=CR10\nCui W, Xiao Y, Wang H, Wang W (2014) Local search of communities in large graphs. In: SIGMOD, pp 991–1002\ncitation_journal_title=PVLDB; citation_title=Effective community search for large attributed graphs; citation_author=Y Fang, R Cheng, S Luo, J Hu; citation_volume=9; citation_issue=12; citation_publication_date=2016; citation_pages=1233-1244; citation_id=CR12\ncitation_journal_title=SIAM J Comput; citation_title=A fast parametric maximum flow algorithm and applications; citation_author=G Gallo, MD Grigoriadis, RE Tarjan; citation_volume=18; citation_issue=1; citation_publication_date=1989; citation_pages=30-55; citation_doi=10.1137\u002F0218003; citation_id=CR13\nGiatsidis C, Thilikos DM, Vazirgiannis M (2011) D-cores: measuring collaboration of directed graphs based on degeneracy. In: ICDM, pp 201–210\nGiatsidis C, Thilikos DM, Vazirgiannis M (2011) Evaluating cooperation in communities with the k-core structure. In: ASONAM, pp 87–93\ncitation_journal_title=Random Struct Algorithms; citation_title=A simple solution to the k-core problem; citation_author=S Janson, MJ Luczak; citation_volume=30; citation_issue=1–2; citation_publication_date=2007; citation_pages=50-62; citation_doi=10.1002\u002Frsa.20147; citation_id=CR16\ncitation_journal_title=PVLDB; citation_title=K-core decomposition of large networks on a single pc; citation_author=W Khaouid, M Barsky, V Srinivasan, A Thomo; citation_volume=9; citation_issue=1; citation_publication_date=2015; citation_pages=13-23; citation_id=CR17\ncitation_journal_title=TKDD; citation_title=Graph evolution: densification and shrinking diameters; citation_author=J Leskovec, J Kleinberg, C Faloutsos; citation_volume=1; citation_issue=1; citation_publication_date=2007; citation_pages=2; citation_doi=10.1145\u002F1217299.1217301; citation_id=CR18\ncitation_journal_title=PVLDB; citation_title=Influential community search in large networks; citation_author=R-H Li, L Qin, JX Yu, R Mao; citation_volume=8; citation_issue=5; citation_publication_date=2015; citation_pages=509-520; citation_id=CR19\ncitation_journal_title=TKDE; citation_title=Efficient core maintenance in large dynamic graphs; citation_author=R-H Li, JX Yu, R Mao; citation_volume=26; citation_issue=10; citation_publication_date=2014; citation_pages=2453-2465; citation_id=CR20\ncitation_journal_title=Discrete Math; citation_title=Size and connectivity of the k-core of a random graph; citation_author=T Łuczak; citation_volume=91; citation_issue=1; citation_publication_date=1991; citation_pages=61-68; citation_doi=10.1016\u002F0012-365X(91)90162-U; citation_id=CR21\nMa H, Zhou D, Liu C, Lyu MR, King I (2011) Recommender systems with social regularization. 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In: ICDM, pp 460–469\ncitation_journal_title=J Combin Theory Ser B; citation_title=Sudden emergence of a giantk-core in a random graph; citation_author=B Pittel, J Spencer, N Wormald; citation_volume=67; citation_issue=1; citation_publication_date=1996; citation_pages=111-151; citation_doi=10.1006\u002Fjctb.1996.0036; citation_id=CR27\ncitation_journal_title=PVLDB; citation_title=Streaming algorithms for k-core decomposition; citation_author=AE Saríyüce, B Gedik, G Jacques-Silva, K-L Wu, ÜV Çatalyürek; citation_volume=6; citation_issue=6; citation_publication_date=2013; citation_pages=433-444; citation_id=CR28\ncitation_journal_title=Soc Netw; citation_title=Network structure and minimum degree; citation_author=SB Seidman; citation_volume=5; citation_issue=3; citation_publication_date=1983; citation_pages=269-287; citation_doi=10.1016\u002F0378-8733(83)90028-X; citation_id=CR29\nTang J, Zhang J, Yao L, Li J, Zhang L, Su Z (2008) Arnetminer: extraction and mining of academic social networks. In: KDD, pp 990–998\ncitation_journal_title=PVLDB; citation_title=Truss decomposition in massive networks; citation_author=J Wang, J Cheng; citation_volume=5; citation_issue=9; citation_publication_date=2012; citation_pages=812-823; citation_id=CR31\nWen D, Qin L, Zhang Y, Lin X, Yu JX (2016) I\u002Fo efficient core graph decomposition at web scale. In: ICDE, pp 133–144. IEEE\nWu Y, Jin R, Zhu X, Zhang X (2015) Finding dense and connected subgraphs in dual networks. In: ICDE, pp 915–926\ncitation_title=K-connected cores computation in large dual networks; citation_inbook_title=Database systems for advanced applications; citation_publication_date=2018; citation_pages=169-186; citation_id=CR34; citation_author=L Yue; citation_author=D Wen; citation_author=L Cui; citation_author=L Qin; citation_author=Y Zheng; citation_publisher=Springer",{"EN":157},"Computing $$k\\text {-}core$$ s is a fundamental and important graph problem, which can be applied in many areas, such as community detection, network visualization, and network topology analysis. Due to the complex relationship between different entities, dual graph widely exists in the applications. A dual graph contains a physical graph and a conceptual graph, both of which have the same vertex set. Given that there exist no previous studies on the $$k\\text {-}core$$ in dual graphs, we formulate a k-connected core ( $$k\\text {-}CCO$$ ) model in dual graphs. A $$k\\text {-}CCO$$ is a $$k\\text {-}core$$ in the conceptual graph, and also connected in the physical graph. Given a dual graph and an integer k, we propose a polynomial time algorithm for computing all $$k\\text {-}CCO$$ s. We also propose three algorithms for computing all maximum-connected cores ( $$MCCO$$ ), which are the existing $$k\\text {-}CCO$$ s such that a $$(k+1)$$ - $$CCO$$ does not exist. We further study a subgraph search problem, which is computing a $$k\\text {-}CCO$$ that contains a set of query vertices. We propose an index-based approach to efficiently answer the query for any given parameter k. We conduct extensive experiments on six real-world datasets and four synthetic datasets. 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In: 2019 IEEE 35th international conference on data engineering workshops (ICDEW). IEEE, pp 241–248\nJin X, Yang Z, Lin X, Yang S, Qin L, Peng Y (2021) Fast: Fpga-based subgraph matching on massive graphs. In: 2021 IEEE 37th international conference on data engineering (ICDE)",{"EN":274},"Knowledge graph has wide applications in the field of computer science. In the knowledge service environment, the information is large and explosive, and it is difficult to find knowledge of common phenomena. The urban traffic knowledge graph is a knowledge system that formally describes urban traffic concepts, entities and their interrelationships. It has great application potential in application scenarios such as user travel, route planning, and urban planning. This paper first defines the urban traffic knowledge graph and the star subgraph query of the urban traffic knowledge graph. Then, the road network data and trajectory data are collected to extract the urban traffic knowledge, and the urban traffic knowledge graph is constructed with this knowledge. Finally, a star subgraph query algorithm on the urban traffic knowledge graph is proposed. The discussion of the star subgraph query mode gives the corresponding application scenarios of our method in the urban traffic knowledge graph. Experimental results verify the performance advantages of this method.",{"EN":276},"An Efficient Algorithm of Star Subgraph Queries on Urban Traffic Knowledge Graph",{"VOID":278},"10.1007\u002Fs41019-022-00198-0","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs41019-022-00198-0",[281,298,310],{"id":282,"sortIndex":130,"researcher":20,"roles":283,"affiliations":284,"properties":295},"0c01bbe0-2f7c-4b56-b1c6-bd55c397419e",[171],[285],{"id":20,"sortIndex":21,"affiliation":286,"properties":20},{"id":287,"createTime":288,"updateTime":289,"relativeEntities":290,"slug":291,"properties":292,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"9ee1d42b-0a60-4c9e-8200-5a45b66b4f4d","2023-12-13T22:04:40.937+00:00","2024-12-19T23:03:00.274+00:00",[],"Nanjing-University-of-Aeronautics-and-Astronautics-Nanjing-China",{"title":293},{"VI":294},"Nanjing University of Aeronautics and Astronautics, Nanjing, China",{"title":296},{"VI":297},"Jianqiu Xu",{"id":299,"sortIndex":21,"researcher":20,"roles":300,"affiliations":301,"properties":307},"5e9749d2-7629-417b-9bcf-929f22eab8af",[171],[302],{"id":20,"sortIndex":21,"affiliation":303,"properties":20},{"id":287,"createTime":288,"updateTime":289,"relativeEntities":304,"slug":291,"properties":305,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":306},{"VI":294},{"title":308},{"VI":309},"Tao Sun",{"id":311,"sortIndex":114,"researcher":20,"roles":312,"affiliations":313,"properties":322},"a0c2740b-1cb2-42d9-bf2d-7974452f8d2b",[171],[314],{"id":20,"sortIndex":21,"affiliation":315,"properties":20},{"id":316,"createTime":317,"updateTime":317,"relativeEntities":318,"slug":20,"properties":319,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"a92a368e-db45-4eb0-888e-515755795f67","2024-02-15T23:11:52.224+00:00",[],{"title":320},{"VI":321},"Department of Computer Engineering, Jinling Institute of Technology, Nanjing, China",{"title":323},{"VI":324},"Caiping Hu",{"url":279,"publisher":326,"properties":354},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":327,"slug":10,"properties":328,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":332,"manageAffiliations":333,"indexDatabases":334,"url":101,"thumbnailPath":20,"statistic":349,"gsStatistic":20,"type":142,"analyzePriority":20},[],{"issn":329,"eissn":330,"title":331},{"VOID":13},{"VOID":15},{"EN":17},[],[],[335,342],{"id":82,"indexDatabase":336,"url":95,"indexYears":96,"academicFieldIds":341,"indexDatabaseRanking":100},{"id":84,"createTime":85,"updateTime":86,"relativeEntities":337,"label":338,"description":339,"key":92,"publicationTags":340,"standard":20},[],{"EN":89,"VI":89},{"EN":89,"VI":91},[94],[98,99],{"id":63,"indexDatabase":343,"url":78,"indexYears":20,"academicFieldIds":348,"indexDatabaseRanking":20},{"id":65,"createTime":66,"updateTime":67,"relativeEntities":344,"label":345,"description":346,"key":74,"publicationTags":347,"standard":20},[],{"EN":70,"VI":70},{"VI":72,"EN":73},[76,77],[80],{"impactFactor":21,"impactFactorByYear":350,"i10Index":110,"i10IndexLast5Year":111,"totalPublication":112,"totalPublicationByYear":351,"totalCitation":122,"totalCitationByYear":352,"totalCitationPerPublication":131,"totalCitationPerPublicationByYear":353,"hindexLast5Year":141,"hindex":141},{"2017":104,"2018":105,"2019":106,"2020":107,"2021":106,"2022":108,"2023":109},{"2015":114,"2016":115,"2017":116,"2018":117,"2019":118,"2020":117,"2021":119,"2022":120,"2023":119,"2024":121},{"2016":124,"2017":125,"2018":126,"2019":127,"2020":128,"2021":129,"2022":116,"2024":130},{"2016":133,"2017":134,"2018":135,"2019":136,"2020":137,"2021":138,"2022":139,"2024":140},{"volume":355,"pages":357},{"VOID":356},"7",{"VOID":358},"383-401","2022-10-21",2022,{"id":362,"createTime":363,"updateTime":363,"relativeEntities":364,"slug":365,"properties":366,"entityType":162,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":377,"fullTextUrl":20,"authors":378,"publicationType":226,"publisherRelationship":435,"citationCount":20,"citationInfo":20,"publishDate":464,"publishYear":360,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":265},"b675d59b-8128-45e5-bf1e-cd84de6e2c5b","2024-04-08T23:08:04.529+00:00",[],"Link-Prediction-on-Complex-Networks-An-Experimental-Survey",{"references":367,"keywords":369,"abstract":371,"title":373,"doi":375},{"VOID":368},"Amaral LA, Ottino JM (2004) Complex networks. 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In: Proceedings of the international AAAI conference on web and social media, vol 3, pp 361–362\nDe Domenico M, Porter MA, Arenas A (2015) Muxviz: a tool for multilayer analysis and visualization of networks. J Complex Netw 3(2):159–176\nHanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology 143(1):29–36\nChakrabarti S, Khanna R, Sawant U, Bhattacharyya C (2008) Structured learning for non-smooth ranking losses. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’08, Association for Computing Machinery, New York, NY, USA, pp 88–96. https:\u002F\u002Fdoi.org\u002F10.1145\u002F1401890.1401906\nBordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. Adv Neural Inf Process Syst 26:1\nhttps:\u002F\u002Fgithub.com\u002Fwhxhx\u002FLink-Prediction-Methods (2021)",{"EN":370},"",{"EN":372},"Complex networks have been used widely to model a large number of relationships. The outbreak of COVID-19 has had a huge impact on various complex networks in the real world, for example global trade networks, air transport networks, and even social networks, known as racial equality issues caused by the spread of the epidemic. Link prediction plays an important role in complex network analysis in that it can find missing links or predict the links which will arise in the future in the network by analyzing the existing network structures. Therefore, it is extremely important to study the link prediction problem on complex networks. There are a variety of techniques for link prediction based on the topology of the network and the properties of entities. In this work, a new taxonomy is proposed to divide the link prediction methods into five categories and a comprehensive overview of these methods is provided. The network embedding-based methods, especially graph neural network-based methods, which have attracted increasing attention in recent years, have been creatively investigated as well. Moreover, we analyze thirty-six datasets and divide them into seven types of networks according to their topological features shown in real networks and perform comprehensive experiments on these networks. We further analyze the results of experiments in detail, aiming to discover the most suitable approach for each kind of network.",{"EN":374},"Link Prediction on Complex Networks: An Experimental Survey",{"VOID":376},"10.1007\u002Fs41019-022-00188-2","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs41019-022-00188-2",[379,395,407,423],{"id":380,"sortIndex":21,"researcher":20,"roles":381,"affiliations":382,"properties":392},"157ed632-41e8-481d-b0ac-4effe06996de",[171],[383],{"id":20,"sortIndex":21,"affiliation":384,"properties":20},{"id":385,"createTime":386,"updateTime":386,"relativeEntities":387,"slug":388,"properties":389,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"c57c1e7e-945e-4cab-af8c-3e22d6b4b154","2024-04-08T23:08:04.647+00:00",[],"College-of-Computer-Science-Tianjin-Key-Laboratory-of-Network-and-Data-Security-Technology-Nankai-University-Tianjin-China",{"title":390},{"VI":391},"College of Computer Science, Tianjin Key Laboratory of Network and Data Security Technology, Nankai University, Tianjin, China",{"title":393},{"VI":394},"Haixia Wu",{"id":396,"sortIndex":114,"researcher":20,"roles":397,"affiliations":398,"properties":404},"fecdd0ca-79c6-4b2c-98d7-0b48ce20df80",[171],[399],{"id":20,"sortIndex":21,"affiliation":400,"properties":20},{"id":385,"createTime":386,"updateTime":386,"relativeEntities":401,"slug":388,"properties":402,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":403},{"VI":391},{"title":405},{"VI":406},"Yao Ge",{"id":408,"sortIndex":203,"researcher":20,"roles":409,"affiliations":410,"properties":420},"28fd5e1b-8d68-4c21-b9e8-e31c9380926c",[171],[411],{"id":20,"sortIndex":21,"affiliation":412,"properties":20},{"id":413,"createTime":414,"updateTime":414,"relativeEntities":415,"slug":416,"properties":417,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"f8ea3e43-80cd-4dd5-ba6e-83cadbe1ca7c","2024-04-08T23:08:04.658+00:00",[],"University-of-Massachusetts-Lowell-Massachusetts-United-States",{"title":418},{"VI":419},"University of Massachusetts Lowell, Massachusetts, United States",{"title":421},{"VI":422},"Tingjian Ge",{"id":424,"sortIndex":130,"researcher":20,"roles":425,"affiliations":426,"properties":432},"39dcb9aa-2a69-447a-9b98-61573cf32746",[171],[427],{"id":20,"sortIndex":21,"affiliation":428,"properties":20},{"id":385,"createTime":386,"updateTime":386,"relativeEntities":429,"slug":388,"properties":430,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":431},{"VI":391},{"title":433},{"VI":434},"Chunyao Song",{"url":20,"publisher":436,"properties":20},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":437,"slug":10,"properties":438,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":442,"manageAffiliations":443,"indexDatabases":444,"url":101,"thumbnailPath":20,"statistic":459,"gsStatistic":20,"type":142,"analyzePriority":20},[],{"issn":439,"eissn":440,"title":441},{"VOID":13},{"VOID":15},{"EN":17},[],[],[445,452],{"id":82,"indexDatabase":446,"url":95,"indexYears":96,"academicFieldIds":451,"indexDatabaseRanking":100},{"id":84,"createTime":85,"updateTime":86,"relativeEntities":447,"label":448,"description":449,"key":92,"publicationTags":450,"standard":20},[],{"EN":89,"VI":89},{"EN":89,"VI":91},[94],[98,99],{"id":63,"indexDatabase":453,"url":78,"indexYears":20,"academicFieldIds":458,"indexDatabaseRanking":20},{"id":65,"createTime":66,"updateTime":67,"relativeEntities":454,"label":455,"description":456,"key":74,"publicationTags":457,"standard":20},[],{"EN":70,"VI":70},{"VI":72,"EN":73},[76,77],[80],{"impactFactor":21,"impactFactorByYear":460,"i10Index":110,"i10IndexLast5Year":111,"totalPublication":112,"totalPublicationByYear":461,"totalCitation":122,"totalCitationByYear":462,"totalCitationPerPublication":131,"totalCitationPerPublicationByYear":463,"hindexLast5Year":141,"hindex":141},{"2017":104,"2018":105,"2019":106,"2020":107,"2021":106,"2022":108,"2023":109},{"2015":114,"2016":115,"2017":116,"2018":117,"2019":118,"2020":117,"2021":119,"2022":120,"2023":119,"2024":121},{"2016":124,"2017":125,"2018":126,"2019":127,"2020":128,"2021":129,"2022":116,"2024":130},{"2016":133,"2017":134,"2018":135,"2019":136,"2020":137,"2021":138,"2022":139,"2024":140},"2022-06-21",{"id":466,"createTime":467,"updateTime":467,"relativeEntities":468,"slug":20,"properties":469,"entityType":162,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":478,"fullTextUrl":20,"authors":479,"publicationType":226,"publisherRelationship":543,"citationCount":20,"citationInfo":20,"publishDate":577,"publishYear":578,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":265},"8b1ddb62-f0d1-4b4c-bc41-756576afa932","2024-02-09T23:03:17.688+00:00",[],{"references":470,"abstract":472,"title":474,"doi":476},{"VOID":471},"Antoniou 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In: Proceedings of the AISTATS, vol 51, pp 370–378\nWilson AG, Izmailov P (2020) Bayesian deep learning and a probabilistic perspective of generalization. In: Proceedings of the NeurIPS\nXu K, Hu W, Leskovec J, Jegelka S (2019) How powerful are graph neural networks? In: Proceedings of the ICLR’19\nYang Q, Zhang Y, Dai W, Pan SJ (2020) Transfer learning. Cambridge University Press, Cambridge\nYang R, Shi J, Xiao X, Yang Y, Bhowmick SS (2020) Homogeneous network embedding for massive graphs via reweighted personalized pagerank. Proc VLDB 13(5):670–683\nZhang J, Dong Y, Wang Y, Tang J, Ding M (2019) Prone: fast and scalable network representation learning. In: Proceedings of the IJCAI’19, pp 4278–4284\nZhao K, Yu JX, He Z, Rong Y (2023) Learning with small data: subgraph counting queries. In: Proceedings of the DASFAA 2023. Springer, pp 308–319\nZhao K, Yu JX, Li Q, Zhang H, Rong Y (2023) Learned sketch for subgraph counting: a holistic approach. VLDB J 1–26\nZhao K, Yu JX, Zhang H, Li Q, Rong Y (2021) A learned sketch for subgraph counting. In: Proceedings of the SIGMOD’21\nZhou F, Cao C, Zhang K, Trajcevski G, Zhong T, Geng J (2019) Meta-gnn: on few-shot node classification in graph meta-learning. In: Proceedings of the CIKM 2019. ACM, pp 2357–2360",{"EN":473},"Deep Learning (DL) has been widely used in many applications, and its success is achieved with large training data. A key issue is how to provide a DL solution when there is no large training data to learn initially. In this paper, we explore a meta-learning approach for a specific problem, subgraph isomorphism counting, which is a fundamental problem in graph analysis to count the number of a given pattern graph, p, in a data graph, g, that matches p. There are various data graphs and pattern graphs. A subgraph isomorphism counting query is specified by a pair, (g, p). This problem is NP-hard and needs large training data to learn by DL in nature. We design a Gaussian Process (GP) model which combines Graph Neural Network with Bayesian nonparametric, and we train the GP by a meta-learning algorithm on a small set of training data. By meta-learning, we can obtain a generalized meta-model to better encode the information of data and pattern graphs and capture the prior of small tasks. With the meta-model learned, we handle a collection of pairs (g, p), as a task, where some pairs may be associated with the ground-truth, and some pairs are the queries to answer. There are two cases. One is there are some with ground-truth (few-shot), and one is there is none with ground-truth (zero-shot). We provide our solutions for both. In particular, for zero-shot, we propose a new data-driven approach to predict the count values. Note that zero-shot learning for our regression tasks is difficult, and there is no hands-on solution in the literature. We conducted extensive experimental studies to confirm that our approach is robust to model degeneration on small training data, and our meta-model can fast adapt to new queries by few-shot and zero-shot learning.",{"EN":475},"Learning with Small Data: Subgraph Counting 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In consequence,\n new methodologies, algorithms and tools for querying, deploying and programming data management functions have to be provided in scalable and elastic architectures that can cope with the characteristics of Big Data aware systems (intelligent systems, decision making, virtual environments, smart cities, drug personalization). These functions, must respect QoS properties (e.g., security, reliability, fault tolerance, dynamic evolution and adaptability) and behavior properties (e.g., transactional execution) according to application requirements. Mature and novel system architectures propose models and mechanisms for adding these properties to new efficient data management and processing functions delivered as services. This paper gives an overview of the different architectures in which efficient data management functions can be delivered for addressing Big Data processing challenges.",{"EN":929},"Big Data Management: What to Keep from the Past to Face Future Challenges?",{"VOID":931},"10.1007\u002Fs41019-017-0043-3","2024-12-08T22:50:29.707+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs41019-017-0043-3",[935,950,965],{"id":936,"sortIndex":114,"researcher":20,"roles":937,"affiliations":938,"properties":947},"4b07e73c-915d-4ddf-9db0-e624fa37a4dd",[171],[939],{"id":20,"sortIndex":21,"affiliation":940,"properties":20},{"id":941,"createTime":942,"updateTime":942,"relativeEntities":943,"slug":20,"properties":944,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"ff14caae-1271-4258-9af8-a3ab9705bd90","2024-01-16T23:44:40.554+00:00",[],{"title":945},{"VI":946},"Barcelona Supercomputing Center, LAFMIA, Barcelona, Spain",{"title":948},{"VI":949},"J. 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Comput Linguist 16(1):22–29\nDai AM, Le QV (2015) Semi-supervised sequence learning. In: Advances in neural information processing systems 28: annual conference on neural information processing systems 2015, December 7–12, Montreal, Quebec, Canada, pp 3079–3087\nDeng ZH, Luo KH, Yu HL (2014) A study of supervised term weighting scheme for sentiment analysis. Expert Syst Appl 41(7):3506–3513\nDenil M, Demiraj A, Kalchbrenner N, Blunsom P, de Freitas N (2014) Modelling, visualising and summarising documents with a single convolutional neural network. arXiv preprint arXiv:1406.3830\nFan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) Liblinear: a library for large linear classification. J Mach Learn Res 9:1871–1874\nGoldberg Y (2016) A primer on neural network models for natural language processing. J Artif Intell Res 57:345–420\nIyyer M, Manjunatha V, Boyd-Graber JL, Daumé III H (2015) Deep unordered composition rivals syntactic methods for text classification. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing of the asian federation of natural language processing, ACL 2015, July 26–31, Beijing, China, volume 1: long papers, pp 1681–1691\nJohnson R, Zhang T (2015) Semi-supervised convolutional neural networks for text categorization via region embedding. In: Advances in neural information processing systems 28: annual conference on neural information processing systems 2015, December 7–12, Montreal, Quebec, Canada, pp 919–927\nKim Y (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882\nKim Y, Zhang O (2014) Credibility adjusted term frequency: A supervised term weighting scheme for sentiment analysis and text classification. arXiv preprint arXiv:1405.3518\nLe QV, Mikolov T (2014) Distributed representations of sentences and documents. ICML 14:1188–1196\nLevy O, Goldberg Y (2014) Neural word embedding as implicit matrix factorization. In: Advances in neural information processing systems 27: annual conference on neural information processing systems 2014, December 8–13, Montreal, Quebec, Canada, pp 2177–2185\nLevy O, Goldberg Y, Dagan I (2015) Improving distributional similarity with lessons learned from word embeddings. Trans Assoc Comput Linguist 3:211–225\nLi B, Liu T, Du X, Zhang D, Zhao Z (2015) Learning document embeddings by predicting n-grams for sentiment classification of long movie reviews. arXiv preprint arXiv:1512.08183\nLi J (2014) Feature weight tuning for recursive neural networks. arXiv preprint arXiv:1412.3714\nMaas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, Vol 1. pp 142–150. Association for Computational Linguistics\nMartineau J, Finin T (2009) Delta tfidf: an improved feature space for sentiment analysis. Icwsm 9:106\nMesnil G, Mikolov T, Ranzato M, Bengio Y (2014) Ensemble of generative and discriminative techniques for sentiment analysis of movie reviews. arXiv preprint arXiv:1412.5335\nMikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems 26: 27th annual conference on neural information processing systems 2013, December 5–8, Lake Tahoe, NV, USA, pp 3111–3119\nPaltoglou G, Thelwall M (2010) A study of information retrieval weighting schemes for sentiment analysis. In: Proceedings of the 48th annual meeting of the association for computational linguistics, pp 1386–1395. Association for Computational Linguistics\nPang B, Lee L, Vaithyanathan S (2002) Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on Empirical methods in natural language processing, Vol 10, pp 79–86. Association for Computational Linguistics\nPennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. EMNLP 14:1532–1543\nSocher R, Huval B, Manning CD, Ng AY (2012) Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of the 2012 Joint Conference on empirical methods in natural language processing and computational natural language learning, pp 1201–1211. Association for Computational Linguistics\nWang S, Manning CD (2012) Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of the 50th annual meeting of the asssociation for computational linguistics: short papers, Vol 2, pp 90–94. Association for Computational Linguistics\nZhao Z, Liu T, Hou X, Li B, Du X (2016) Distributed text representation with weighting scheme guidance for sentiment analysis. In: Asia-Pacific web conference, Springer, pp 41–52",{"EN":1026},"With the rapid growth of social media, sentiment analysis has received growing attention from both academic and industrial fields. One line of researches for sentiment analysis is to feed bag-of-words (BOW) text representation into classifiers. Usually, raw BOW requires weighting schemes to obtain better performance, where important words are given more weights while unimportant ones are given less weights.\n Another line of researches focuses on neural models, where distributed text representations are learned from raw texts automatically. In this paper, we take advantages of techniques in both lines of researches.\n We use words’ weights to guide neural models to focus on important words. Various supervised weighting schemes are explored in this work. We discover that better text features are learned for sentiment analysis when suitable weighting schemes are applied upon neural models.",{"EN":1028},"Guiding the Training of Distributed Text Representation with Supervised Weighting Scheme for Sentiment Analysis",{"VOID":1030},"10.1007\u002Fs41019-017-0040-6","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs41019-017-0040-6",[1033,1048,1060,1077,1089],{"id":1034,"sortIndex":130,"researcher":20,"roles":1035,"affiliations":1036,"properties":1045},"71736c53-c9bd-43d1-874a-801be504bb7c",[171],[1037],{"id":20,"sortIndex":21,"affiliation":1038,"properties":20},{"id":1039,"createTime":1040,"updateTime":1040,"relativeEntities":1041,"slug":20,"properties":1042,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"b1d1a68b-a4a1-4d8b-ab15-6c36f39da9a1","2023-12-02T19:45:02.508+00:00",[],{"title":1043},{"VI":1044},"School of Information, Renmin University of China, Beijing, China",{"title":1046},{"VI":1047},"Tao Liu",{"id":1049,"sortIndex":203,"researcher":20,"roles":1050,"affiliations":1051,"properties":1057},"64daa72b-33a6-4122-a851-0167fb7955f5",[171],[1052],{"id":20,"sortIndex":21,"affiliation":1053,"properties":20},{"id":1039,"createTime":1040,"updateTime":1040,"relativeEntities":1054,"slug":20,"properties":1055,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1056},{"VI":1044},{"title":1058},{"VI":1059},"Bofang 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Li",{"id":1078,"sortIndex":21,"researcher":20,"roles":1079,"affiliations":1080,"properties":1086},"d1d638a6-9e19-416d-b978-87dfb479daa8",[171],[1081],{"id":20,"sortIndex":21,"affiliation":1082,"properties":20},{"id":1039,"createTime":1040,"updateTime":1040,"relativeEntities":1083,"slug":20,"properties":1084,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1085},{"VI":1044},{"title":1087},{"VI":1088},"Zhe 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S, Agrawal A, Lu J, Mitchell M, Batra D, Zitnick CL, Parikh D (2015) VQA: visual question answering. In: Proceedings of the IEEE international conference on computer vision, pp 2425–2433\nWeiss M, Chamorro S, Girgis R, Luck M, Kahou SE, Cohen JP, Nowrouzezahrai D, Precup D, Golemo F, Pal C (2020) Navigation agents for the visually impaired: a sidewalk simulator and experiments. In: Conference on robot learning. PMLR, pp 1314–1327\nBghiel A, Dahdouh Y, Allaouzi I, Ben Ahmed M, Anouar Boudhir A (2019) Visual question answering system for identifying medical images attributes. In: The proceedings of the third international conference on smart city applications. Springer, pp 483–492\nMalinowski M, Rohrbach M, Fritz M (2015) Ask your neurons: a neural-based approach to answering questions about images. In: Proceedings of the IEEE international conference on computer vision, pp 1–9\nZhou B, Tian Y, Sukhbaatar S, Szlam A, Fergus R (2015) Simple baseline for visual question answering. arXiv preprint arXiv:1512.02167\nLu J, Yang J, Batra D, Parikh D (2016) Hierarchical co-attention for visual question answering. In: Advances in neural information processing systems (NIPS) 2\nKim J-H, Jun J, Zhang B-T (2018) Bilinear attention networks. In: Advances in neural information processing systems 31\nFukui A, Park DH, Yang D, Rohrbach A, Darrell T, Rohrbach M (2016) Multimodal compact bilinear pooling for visual question answering and visual grounding. arXiv preprint arXiv:1606.01847\nMa Y, Lu T, Wu Y (2021) Multi-scale relational reasoning with regional attention for visual question answering. In: 2020 25th international conference on pattern recognition (ICPR). IEEE, pp 5642–5649\nAnderson P, He X, Buehler C, Teney D, Johnson M, Gould S, Zhang L (2018) Bottom-up and top-down attention for image captioning and visual question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6077–6086\nZhu C, Zhao Y, Huang S, Tu K, Ma Y (2017) Structured attentions for visual question answering. In: Proceedings of the IEEE international conference on computer vision, pp 1291–1300\nShih KJ, Singh S, Hoiem D (2016) Where to look: focus regions for visual question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4613–4621\nVaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems 30\nDevlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805\nDosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16 \\(\\times\\) 16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929\nYu W, Luo M, Zhou P, Si C, Zhou Y, Wang X, Feng J, Yan S (2021) Metaformer is actually what you need for vision. arXiv preprint arXiv:2111.11418\nNguyen D-K, Okatani T (2018) Improved fusion of visual and language representations by dense symmetric co-attention for visual question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6087–6096\nPatro B, Namboodiri VP (2018) Differential attention for visual question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7680–7688\nYang C, Jiang M, Jiang B, Zhou W, Li K (2019) Co-attention network with question type for visual question answering. IEEE Access 7:40771–40781\nMalinowski M, Fritz M (2014) A multi-world approach to question answering about real-world scenes based on uncertain input. In: Advances in neural information processing systems 27\nGoyal Y, Khot T, Summers-Stay D, Batra D, Parikh D (2017) Making the V in VQA matter: elevating the role of image understanding in visual question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6904–6913\nGao H, Mao J, Zhou J, Huang Z, Wang L, Xu W (2015) Are you talking to a machine? Dataset and methods for multilingual image question. In: Advances in neural information processing systems 28\nNoh H, Seo P.H, Han B (2016) Image question answering using convolutional neural network with dynamic parameter prediction. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 30–38\nRen S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems 28\nLu P, Li H, Zhang W, Wang J, Wang X (2018) Co-attending free-form regions and detections with multi-modal multiplicative feature embedding for visual question answering. In: Proceedings of the AAAI conference on artificial intelligence, vol 32\nYang Z, He X, Gao J, Deng L, Smola A (2016) Stacked attention networks for image question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 21–29\nXiong C, Merity S, Socher R (2016) Dynamic memory networks for visual and textual question answering. In: International conference on machine learning. PMLR, pp 2397–2406\nNam H, Ha J-W, Kim J (2017) Dual attention networks for multimodal reasoning and matching. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 299–307\nTeney D, Anderson P, He X, Van Den Hengel A (2018) Tips and tricks for visual question answering: learnings from the 2017 challenge. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4223–4232\nKrishna R, Zhu Y, Groth O, Johnson J, Hata K, Kravitz J, Chen S, Kalantidis Y, Li L-J, Shamma DA et al (2017) Visual genome: connecting language and vision using crowdsourced dense image annotations. Int J Comput Vis 123(1):32–73\nYu Z, Yu J, Cui Y, Tao D, Tian Q (2019) Deep modular co-attention networks for visual question answering. In: Proceedings of the IEEE\u002FCVF conference on computer vision and pattern recognition, pp 6281–6290\nHe K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778\nLei Ba J, Kiros JR, Hinton GE (2016) Layer normalization. arXiv e-prints, 1607\nLin TY, Maire M, Belongie S, Hays J, Zitnick CL (2014) Microsoft coco: common objects in context. Springer, pp 740–755\nZhang S, Chen M, Chen J, Zou F, Li Y-F, Lu P (2021) Multimodal feature-wise co-attention method for visual question answering. Inf Fusion 73:1–10\nThomee B, Elizalde B, Shamma DA, Ni K, Friedland G, Poland D, Borth D, Li LJ (2016) Yfcc100m: the new data in multimedia research. Commun ACM 59(2):64–73\nKafle K, Kanan C (2017) Visual question answering: datasets, algorithms, and future challenges. Comput Vis Image Underst 163:3–20\nKim J-H, On KW, Lim W, Kim J, Ha J-W, Zhang B-T (2017) Hadamard Product for Low-rank Bilinear Pooling. In: The 5th international conference on learning representations\nLi W, Sun J, Liu G, Zhao L, Fang X (2020) Visual question answering with attention transfer and a cross-modal gating mechanism. Pattern Recognit Lett 133:334–340\nLiu Y, Zhang X, Huang F, Cheng L, Li Z (2020) Adversarial learning with multi-modal attention for visual question answering. IEEE Trans Neural Netw Learn Syst 32(9):3894–3908\nSun Q, Xie B, Fu Y (2020) Second order enhanced multi-glimpse attention in visual question answering. In: Proceedings of the Asian conference on computer vision",{"EN":1145},"Visual question answering is a complex multimodal task involving images and text, with broad application prospects in human–computer interaction and medical assistance. Therefore, how to deal with the feature interaction and multimodal feature fusion between the critical regions in the image and the keywords in the question is an important issue. To this end, we propose a neural network based on the encoder–decoder structure of the transformer architecture. Specifically, in the encoder, we use multi-head self-attention to mine word–word connections within question features and stack multiple layers of attention to obtain multi-level question features. We propose a mutual attention module to perform information exchange between modalities for better question features and image features representation on the decoder side. Besides, we connect the encoder and decoder in a meshed manner, perform mutual attention operations with multi-level question features, and aggregate information in an adaptive way. We propose a multi-scale fusion module in the fusion stage, which utilizes feature information at different scales to complete modal fusion. We test and validate the model effectiveness on VQA v1 and VQA v2 datasets. Our model achieves better results than state-of-the-art methods.",{"EN":1147},"A Multi-level Mesh Mutual Attention Model for Visual Question Answering",{"VOID":1149},"10.1007\u002Fs41019-022-00200-9","Author affiliation is blank","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs41019-022-00200-9",[1153,1169,1181,1193,1205],{"id":1154,"sortIndex":203,"researcher":20,"roles":1155,"affiliations":1156,"properties":1166},"d1c4f40d-66b5-4e36-8c26-5c6459e338bc",[171],[1157],{"id":20,"sortIndex":21,"affiliation":1158,"properties":20},{"id":1159,"createTime":1160,"updateTime":1160,"relativeEntities":1161,"slug":1162,"properties":1163,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"15dc5d6b-182d-41c9-8c52-7528edffe430","2023-11-27T06:19:55.976+00:00",[],"Guangxi-Key-Lab-of-Multi-source-Information-Mining-Security-Guangxi-Normal-University-Guilin-China",{"title":1164},{"VI":1165},"Guangxi Key Lab of 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Proc VLDB Endow 11(11):1373–1386",{"doi":1626},"10.14778\u002F3236187.3236192",{"id":1628,"createTime":1629,"updateTime":1630,"relativeEntities":1631,"slug":1632,"properties":1633,"entityType":162,"verifyStatus":163,"verifyTime":1630,"verifyNote":164,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1642,"fullTextUrl":20,"authors":1643,"publicationType":226,"publisherRelationship":1671,"citationCount":20,"citationInfo":20,"publishDate":1704,"publishYear":719,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":265},"7c4f0bfd-0b67-4151-8aa5-b4deba209e6a","2024-02-10T06:23:16.407+00:00","2024-12-17T21:57:58.670+00:00",[],"Efficient-and-Secure-Storage-for-Outsourced-Data-A-Survey",{"references":1634,"abstract":1636,"title":1638,"doi":1640},{"VOID":1635},"Alís JB, Di Pietro R, Orfila A, Sorniotti A (2014) A tunable proof of ownership scheme for deduplication using bloom filters. 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In: IEEE 31st Symposium on Mass Storage Systems and Technologies, MSST’15, pp 1–14",{"EN":1637},"With the growing popularity of cloud computing, more and more enterprises and individuals tend to store their sensitive data on the cloud in order to reduce the cost of data management. However, new security and privacy challenges arise when the data stored in the cloud due to the loss of data control by the data owner.\n This paper focuses on the techniques of verifiable data storage and secure data deduplication. We firstly summarize and classify the state-of-the-art research on cloud data storage mechanism. Then, we present some potential research directions for secure data outsourcing.",{"EN":1639},"Efficient and Secure Storage for Outsourced Data: A Survey",{"VOID":1641},"10.1007\u002Fs41019-016-0018-9","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs41019-016-0018-9",[1644,1659],{"id":1645,"sortIndex":130,"researcher":20,"roles":1646,"affiliations":1647,"properties":1656},"9780754c-4c63-4770-9261-5bb7900019bf",[171],[1648],{"id":20,"sortIndex":21,"affiliation":1649,"properties":20},{"id":1650,"createTime":1651,"updateTime":1651,"relativeEntities":1652,"slug":20,"properties":1653,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"5ba38226-98cc-4ecd-9718-3d982a98d246","2023-12-27T13:45:41.784+00:00",[],{"title":1654},{"VI":1655},"State Key Laboratory of Integrated Service Networks (ISN), Xidian University, Xi’an, People’s Republic of China",{"title":1657},{"VI":1658},"Xiaofeng Chen",{"id":1660,"sortIndex":21,"researcher":20,"roles":1661,"affiliations":1662,"properties":1668},"3b0e9db6-ad4e-4fe2-969a-b107c0d36f83",[171],[1663],{"id":20,"sortIndex":21,"affiliation":1664,"properties":20},{"id":1650,"createTime":1651,"updateTime":1651,"relativeEntities":1665,"slug":20,"properties":1666,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1667},{"VI":1655},{"title":1669},{"VI":1670},"Jianfeng Wang",{"url":1642,"publisher":1672,"properties":1700},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1673,"slug":10,"properties":1674,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1678,"manageAffiliations":1679,"indexDatabases":1680,"url":101,"thumbnailPath":20,"statistic":1695,"gsStatistic":20,"type":142,"analyzePriority":20},[],{"issn":1675,"eissn":1676,"title":1677},{"VOID":13},{"VOID":15},{"EN":17},[],[],[1681,1688],{"id":82,"indexDatabase":1682,"url":95,"indexYears":96,"academicFieldIds":1687,"indexDatabaseRanking":100},{"id":84,"createTime":85,"updateTime":86,"relativeEntities":1683,"label":1684,"description":1685,"key":92,"publicationTags":1686,"standard":20},[],{"EN":89,"VI":89},{"EN":89,"VI":91},[94],[98,99],{"id":63,"indexDatabase":1689,"url":78,"indexYears":20,"academicFieldIds":1694,"indexDatabaseRanking":20},{"id":65,"createTime":66,"updateTime":67,"relativeEntities":1690,"label":1691,"description":1692,"key":74,"publicationTags":1693,"standard":20},[],{"EN":70,"VI":70},{"VI":72,"EN":73},[76,77],[80],{"impactFactor":21,"impactFactorByYear":1696,"i10Index":110,"i10IndexLast5Year":111,"totalPublication":112,"totalPublicationByYear":1697,"totalCitation":122,"totalCitationByYear":1698,"totalCitationPerPublication":131,"totalCitationPerPublicationByYear":1699,"hindexLast5Year":141,"hindex":141},{"2017":104,"2018":105,"2019":106,"2020":107,"2021":106,"2022":108,"2023":109},{"2015":114,"2016":115,"2017":116,"2018":117,"2019":118,"2020":117,"2021":119,"2022":120,"2023":119,"2024":121},{"2016":124,"2017":125,"2018":126,"2019":127,"2020":128,"2021":129,"2022":116,"2024":130},{"2016":133,"2017":134,"2018":135,"2019":136,"2020":137,"2021":138,"2022":139,"2024":140},{"volume":1701,"pages":1702},{"VOID":708},{"VOID":1703},"178-188","2016-09-06"]