{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T12:10:10Z","timestamp":1751717410225,"version":"3.41.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2018,6,1]],"date-time":"2018-06-01T00:00:00Z","timestamp":1527811200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2018,6,28]],"date-time":"2018-06-28T00:00:00Z","timestamp":1530144000000},"content-version":"vor","delay-in-days":27,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000083","name":"Directorate for Computer and Information Science and Engineering","doi-asserted-by":"publisher","award":["IIS-1149851"],"award-info":[{"award-number":["IIS-1149851"]}],"id":[{"id":"10.13039\/100000083","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Sci. Eng."],"published-print":{"date-parts":[[2018,6]]},"DOI":"10.1007\/s41019-018-0065-5","type":"journal-article","created":{"date-parts":[[2018,6,28]],"date-time":"2018-06-28T08:42:45Z","timestamp":1530175365000},"page":"150-165","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Representing Graphs as Bag of Vertices and Partitions for Graph Classification"],"prefix":"10.1007","volume":"3","author":[{"given":"Mansurul","family":"Bhuiyan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8279-1023","authenticated-orcid":false,"given":"Mohammad","family":"Al Hasan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,6,28]]},"reference":[{"key":"65_CR1","doi-asserted-by":"crossref","unstructured":"Berlingerio M, Koutra D, Eliassi-Rad T, Faloutsos C (2013) Network similarity via multiple social theories. In: Proceedings of the 2013 IEEE\/ACM international conference on advances in social networks analysis and mining (ASONAM\u201913), pp 1439\u20131440","DOI":"10.1145\/2492517.2492582"},{"key":"65_CR2","unstructured":"Bordes A, Glorot X, Weston J, Bengio Y (2012) Joint learning of words and meaning representations for open-text semantic parsing. In: International conference on artificial intelligence and statistics, pp 127\u2013135"},{"key":"65_CR3","unstructured":"Borgwardt KM, Kriegel HP (2005) Shortest-path kernels on graphs. In: Proceedings of the fifth IEEE international conference on data mining (ICDM\u201905), pp 74\u201381"},{"key":"65_CR4","unstructured":"Borgwardt KM, Schraudolph NN, Vishwanathan S (2007) Fast computation of graph kernels. In: Sch\u00f6lkopf B, Platt J, Hoffman T (eds) Advances in neural information processing systems, vol 19, pp 1449\u20131456"},{"key":"65_CR5","volume-title":"Structural holes: the social structure of competition","author":"RS Burt","year":"2009","unstructured":"Burt RS (2009) Structural holes: the social structure of competition. Harvard University Press, Harvard"},{"key":"65_CR6","doi-asserted-by":"crossref","unstructured":"Cheng H, Lo D, Zhou Y, Wang X, Yan X (2009) Identifying bug signatures using discriminative graph mining. In: Proceedings of the eighteenth international symposium on software testing and analysis, pp 141\u2013152","DOI":"10.1145\/1572272.1572290"},{"key":"65_CR7","doi-asserted-by":"crossref","unstructured":"Ciresan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR), pp 3642\u20133649","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"65_CR8","volume-title":"Individual interests and collective action: selected essays","author":"JS Coleman","year":"1986","unstructured":"Coleman JS (1986) Individual interests and collective action: selected essays. Cambridge University Press, Cambridge"},{"issue":"8","key":"65_CR9","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1109\/TKDE.2005.127","volume":"17","author":"M Deshpande","year":"2005","unstructured":"Deshpande M, Kuramochi M, Wale N, Karypis G (2005) Frequent substructure-based approaches for classifying chemical compounds. IEEE Trans Knowl Data Eng 17(8):1036\u20131050","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"65_CR10","doi-asserted-by":"crossref","unstructured":"Dhillon I, Guan Y, Kulis B (2005) A fast kernel-based multilevel algorithm for graph clustering. In: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining, pp 629\u2013634","DOI":"10.1145\/1081870.1081948"},{"issue":"1","key":"65_CR11","doi-asserted-by":"publisher","first-page":"4:1","DOI":"10.1145\/2629328","volume":"9","author":"H Fei","year":"2014","unstructured":"Fei H, Huan J (2014) Structured sparse boosting for graph classification. ACM Trans Knowl Discov Data 9(1):4:1\u20134:22","journal-title":"ACM Trans Knowl Discov Data"},{"key":"65_CR12","doi-asserted-by":"crossref","unstructured":"Gascon H, Yamaguchi F, Arp D, Rieck K (2013) Structural detection of android malware using embedded call graphs. In: Proceedings of the 2013 ACM workshop on artificial intelligence and security, pp 45\u201354","DOI":"10.1145\/2517312.2517315"},{"key":"65_CR13","unstructured":"Gonzalez JA, Holder LB, Cook DJ (2002) Graph-based relational concept learning. In: Proceedings of the nineteenth international conference on machine learning (ICML\u201902), pp 219\u2013226"},{"key":"65_CR14","doi-asserted-by":"crossref","unstructured":"Hagberg AA, Schult DA, Swart PJ (2008) Exploring network structure, dynamics, and function using NetworkX. In: Proceedings of the 7th python in science conference (SciPy2008). Pasadena, CA, USA, pp 11\u201315","DOI":"10.25080\/TCWV9851"},{"key":"65_CR15","doi-asserted-by":"crossref","unstructured":"Han J, Wen JR, Pei J (2014) Within-network classification using radius-constrained neighborhood patterns. In: Proceedings of the 23rd ACM CIKM, pp 1539\u20131548","DOI":"10.1145\/2661829.2661979"},{"key":"65_CR16","doi-asserted-by":"crossref","DOI":"10.4324\/9780203781159","volume-title":"The psychology of interpersonal relations","author":"F Heider","year":"2013","unstructured":"Heider F (2013) The psychology of interpersonal relations. Wiley, London"},{"key":"65_CR17","doi-asserted-by":"crossref","unstructured":"Henderson K, Gallagher B, Li L, Akoglu L, Eliassi-Rad T, Tong H, Faloutsos C (2011) It\u2019s who you know: graph mining using recursive structural features. In: Proceedings of the 17th ACM SIGKDD, KDD\u201911","DOI":"10.1145\/2020408.2020512"},{"issue":"6","key":"65_CR18","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1086\/222355","volume":"63","author":"GC Homans","year":"1958","unstructured":"Homans GC (1958) Social behavior as exchange. Am J Sociol 63(6):597\u2013606","journal-title":"Am J Sociol"},{"key":"65_CR19","doi-asserted-by":"crossref","unstructured":"Horv\u00e1th T, G\u00e4rtner T, Wrobel S (2004) Cyclic pattern kernels for predictive graph mining. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (KDD\u201904), pp 158\u2013167","DOI":"10.1145\/1014052.1014072"},{"issue":"01","key":"65_CR20","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1017\/S0269888912000331","volume":"28","author":"C Jiang","year":"2013","unstructured":"Jiang C, Coenen F, Zito M (2013) A survey of frequent subgraph mining algorithms. Knowl Eng Rev 28(01):75\u2013105","journal-title":"Knowl Eng Rev"},{"key":"65_CR21","doi-asserted-by":"crossref","unstructured":"Jin N, Young C, Wang W (2010) Gaia: graph classification using evolutionary computation. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data. ACM, pp 879\u2013890","DOI":"10.1145\/1807167.1807262"},{"key":"65_CR22","doi-asserted-by":"crossref","unstructured":"Keneshloo Y, Yazdani S (2013) A relative feature selection algorithm for graph classification. In: Advances in databases and information systems, advances in intelligent systems and computing, vol 186, pp 137\u2013148","DOI":"10.1007\/978-3-642-32741-4_13"},{"key":"65_CR23","doi-asserted-by":"crossref","unstructured":"Kong X, Fan W, Yu PS (2011) Dual active feature and sample selection for graph classification. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining (KDD\u201911), pp 654\u2013662","DOI":"10.1145\/2020408.2020511"},{"key":"65_CR24","doi-asserted-by":"crossref","unstructured":"Koutra D, Ke TY, Kang U, Chau DH, Pao HKK, Faloutsos C (2011) Unifying guilt-by-association approaches: theorems and fast algorithms. In: Proceedings of the 2011 European conference on machine learning and knowledge discovery in databases\u2014Volume Part II, pp 245\u2013260","DOI":"10.1007\/978-3-642-23783-6_16"},{"key":"65_CR25","doi-asserted-by":"crossref","unstructured":"Kuramochi M, Karypis G (2001) Frequent subgraph discovery. In: Proceedings of the IEEE international conference on data mining, 2001 (ICDM 2001). IEEE, pp 313\u2013320","DOI":"10.1109\/ICDM.2001.989534"},{"issue":"4","key":"65_CR26","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1002\/sam.11153","volume":"5","author":"G Li","year":"2012","unstructured":"Li G, Semerci M, Yener B, Zaki MJ (2012) Effective graph classification based on topological and label attributes. Stat Anal Data Min 5(4):265\u2013283","journal-title":"Stat Anal Data Min"},{"key":"65_CR27","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1007\/978-3-642-42042-9_53","volume-title":"Neural information processing","author":"F Liu","year":"2013","unstructured":"Liu F, Liu B, Sun C, Liu M, Wang X (2013) Deep learning approaches for link prediction in social network services. In: Lee M, Hirose A, Hou Z-H, Kil RM (eds) Neural information processing, vol 8227. Springer, Berlin, pp 425\u2013432"},{"key":"65_CR28","doi-asserted-by":"crossref","unstructured":"Macindoe O, Richards W (2010) Graph comparison using fine structure analysis. In: Proceedings of the 2010 IEEE second international conference on social computing, pp 193\u2013200","DOI":"10.1109\/SocialCom.2010.35"},{"key":"65_CR29","first-page":"3111","volume":"26","author":"T Mikolov","year":"2013","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst 26:3111\u20133119","journal-title":"Adv Neural Inf Process Syst"},{"key":"65_CR30","unstructured":"Neville J, Jensen D (2000) Iterative classification in relational data. In: Proceedings of the AAAI, pp 13\u201320"},{"key":"65_CR31","doi-asserted-by":"crossref","unstructured":"Nguyen PC, Ohara K, Mogi A, Motoda H, Washio T (2006) Constructing decision trees for graph-structured data by chunkingless graph-based induction. In: Proceedings of the 10th Pacific-Asia conference on advances in knowledge discovery and data mining (PAKDD\u201906), pp 390\u2013399","DOI":"10.1007\/11731139_45"},{"key":"65_CR32","doi-asserted-by":"crossref","unstructured":"Nijssen S, Kok J (2004) A quickstart in frequent structure mining can make a difference. In: Proceedings of the ACM SIGKDD","DOI":"10.1145\/1014052.1014134"},{"issue":"11","key":"65_CR33","doi-asserted-by":"publisher","first-page":"2933","DOI":"10.1109\/TKDE.2015.2391115","volume":"27","author":"S Pan","year":"2015","unstructured":"Pan S, Wu J, Zhu X (2015) Cogboost: boosting for fast cost-sensitive graph classification. IEEE Trans Knowl Data Eng 27(11):2933\u20132946","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"65_CR34","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the empirical methods in natural language processing (EMNLP 2014), vol 12, pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"65_CR35","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"issue":"10","key":"65_CR36","doi-asserted-by":"publisher","first-page":"2466","DOI":"10.1109\/TKDE.2013.2297929","volume":"26","author":"M Rahman","year":"2014","unstructured":"Rahman M, Bhuiyan MA, Al Hasan M (2014) Graft: an efficient graphlet counting method for large graph analysis. IEEE Trans Knowl Data Eng 26(10):2466\u20132478","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"65_CR37","doi-asserted-by":"crossref","unstructured":"Ranu S, Hoang M, Singh A (2013) Mining discriminative subgraphs from global-state networks. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, pp 509\u2013517","DOI":"10.1145\/2487575.2487692"},{"issue":"1","key":"65_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/0095-8956(92)90002-F","volume":"56","author":"J Reiterman","year":"1992","unstructured":"Reiterman J, R\u00f6dl V, \u0160i\u0148ajov\u00e1 E (1992) On embedding of graphs into Euclidean spaces of small dimension. J Comb Theory Ser B 56(1):1\u20138","journal-title":"J Comb Theory Ser B"},{"issue":"1","key":"65_CR39","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s10994-008-5089-z","volume":"75","author":"H Saigo","year":"2009","unstructured":"Saigo H, Nowozin S, Kadowaki T, Kudo T, Tsuda K (2009) gboost: a mathematical programming approach to graph classification and regression. Mach Learn 75(1):69\u201389","journal-title":"Mach Learn"},{"key":"65_CR40","doi-asserted-by":"crossref","unstructured":"Shaw B, Jebara T (2009) Structure preserving embedding. In: Proceedings of the 26th annual international conference on machine learning. ACM, pp 937\u2013944","DOI":"10.1145\/1553374.1553494"},{"key":"65_CR41","unstructured":"Shervashidze N, Petri T, Mehlhorn K, Borgwardt KM, Vishwanathan S (2009) Efficient graphlet kernels for large graph comparison. In: Proceedings of the twelfth international conference on artificial intelligence and statistics (AISTATS-09), vol\u00a05, pp 488\u2013495"},{"key":"65_CR42","doi-asserted-by":"crossref","unstructured":"Simard P, Steinkraus D, Platt JC (2003) Best practices for convolutional neural networks applied to visual document analysis. In: Proceedings of the seventh international conference on document analysis and recognition, 2003, pp 958\u2013963","DOI":"10.1109\/ICDAR.2003.1227801"},{"key":"65_CR43","unstructured":"Socher R, Huang EH, Pennin J, Manning CD, Ng AY (2011) Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: Advances in neural information processing systems, pp 801\u2013809"},{"key":"65_CR44","unstructured":"Spielman DA, Teng SH (2004) Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems. In: Proceedings of the thirty-sixth annual ACM symposium on theory of computing, STOC\u201904, pp 81\u201390"},{"key":"65_CR45","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2014) Going deeper with convolutions. CoRR arXiv:1409.4842"},{"key":"65_CR46","unstructured":"The Koblenz Network Collection (konect) (2015) http:\/\/konect.uni-koblenz.de\/networks\/"},{"key":"65_CR47","doi-asserted-by":"crossref","unstructured":"Tang L, Liu H (2009) Scalable learning of collective behavior based on sparse social dimensions. In: Proceedings of the 18th ACM conference on information and knowledge management (CIKM\u201909), pp 1107\u20131116","DOI":"10.1145\/1645953.1646094"},{"issue":"3","key":"65_CR48","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/s10618-010-0210-x","volume":"23","author":"L Tang","year":"2011","unstructured":"Tang L, Liu H (2011) Leveraging social media networks for classification. Data Min Knowl Discov 23(3):447\u2013478","journal-title":"Data Min Knowl Discov"},{"key":"65_CR49","doi-asserted-by":"crossref","unstructured":"Thoma M, Cheng H, Gretton A, Han J, Kriegel HP, Smola A, Song L, Yu PS, Yan X, Borgwardt K (2009) Near-optimal supervised feature selection among frequent subgraphs. In: Proceedings of the 2009 SIAM international conference on data mining. SIAM, pp 1076\u20131087","DOI":"10.1137\/1.9781611972795.92"},{"issue":"5","key":"65_CR50","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1002\/sam.10084","volume":"3","author":"M Thoma","year":"2010","unstructured":"Thoma M, Cheng H, Gretton A, Han J, Kriegel HP, Smola A, Song L, Yu PS, Yan X, Borgwardt KM (2010) Discriminative frequent subgraph mining with optimality guarantees. Stat Anal Data Min 3(5):302\u2013318","journal-title":"Stat Anal Data Min"},{"issue":"19","key":"65_CR51","doi-asserted-by":"publisher","first-page":"6075","DOI":"10.1021\/jm800867g","volume":"51","author":"M Wawer","year":"2008","unstructured":"Wawer M, Peltason L, Weskamp N, Teckentrup A, Bajorath J (2008) Structure\u2013activity relationship anatomy by network-like similarity graphs and local structure\u2013activity relationship indices. J Med Chem 51(19):6075\u20136084","journal-title":"J Med Chem"},{"key":"65_CR52","doi-asserted-by":"crossref","unstructured":"Yan X, Cheng H, Han J, Yu PS (2008) Mining significant graph patterns by leap search. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data. ACM, pp 433\u2013444","DOI":"10.1145\/1376616.1376662"},{"issue":"1","key":"65_CR53","doi-asserted-by":"publisher","first-page":"2291","DOI":"10.1016\/j.artint.2011.10.002","volume":"176","author":"ZH Zhou","year":"2012","unstructured":"Zhou ZH, Zhang ML, Huang SJ, Li YF (2012) Multi-instance multi-label learning. Artif Intell 176(1):2291\u20132320","journal-title":"Artif Intell"}],"container-title":["Data Science and Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s41019-018-0065-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s41019-018-0065-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s41019-018-0065-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T11:31:25Z","timestamp":1751715085000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s41019-018-0065-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6]]},"references-count":53,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2018,6]]}},"alternative-id":["65"],"URL":"https:\/\/doi.org\/10.1007\/s41019-018-0065-5","relation":{},"ISSN":["2364-1185","2364-1541"],"issn-type":[{"type":"print","value":"2364-1185"},{"type":"electronic","value":"2364-1541"}],"subject":[],"published":{"date-parts":[[2018,6]]},"assertion":[{"value":"15 October 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2018","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2018","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2018","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}