{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T03:13:57Z","timestamp":1775272437009,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,16]],"date-time":"2023-07-16T00:00:00Z","timestamp":1689465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sichuan Science and Technology Program","award":["2023YFH0057"],"award-info":[{"award-number":["2023YFH0057"]}]},{"name":"Sichuan Science and Technology Program","award":["G2022186003L"],"award-info":[{"award-number":["G2022186003L"]}]},{"name":"Foreign Talents Program of Ministry of China","award":["2023YFH0057"],"award-info":[{"award-number":["2023YFH0057"]}]},{"name":"Foreign Talents Program of Ministry of China","award":["G2022186003L"],"award-info":[{"award-number":["G2022186003L"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Community detection is widely used in social networks to uncover groups of related vertices (nodes). In cryptocurrency transaction networks, community detection can help identify users that are most related to known illegal users. However, there are challenges in applying community detection in cryptocurrency transaction networks: (1) the use of pseudonymous addresses that are not directly linked to personal information make it difficult to interpret the detected communities; (2) on Bitcoin, a user usually owns multiple Bitcoin addresses, and nodes in transaction networks do not always represent users. Existing works on cluster analysis on Bitcoin transaction networks focus on addressing the later using different heuristics to cluster addresses that are controlled by the same user. This research focuses on illegal community detection containing one or more illegal Bitcoin addresses. We first investigate the structure of Bitcoin transaction networks and suitable community detection methods, then collect a set of illegal addresses and use them to label the detected communities. The results show that 0.06% of communities from daily transaction networks contain one or more illegal addresses when 2,313,344 illegal addresses are used to label the communities. The results also show that distance-based clustering methods and other methods depending on them, such as network representation learning, are not suitable for Bitcoin transaction networks while community quality optimization and label-propagation-based methods are the most suitable.<\/jats:p>","DOI":"10.3390\/e25071069","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:35:04Z","timestamp":1689554104000},"page":"1069","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Illegal Community Detection in Bitcoin Transaction Networks"],"prefix":"10.3390","volume":"25","author":[{"given":"Dany","family":"Kamuhanda","sequence":"first","affiliation":[{"name":"UZH Blockchain Center, University of Zurich, 8050 Zurich, Switzerland"},{"name":"Blockchain & Distributed Ledger Technologies Group, Department of Informatics, University of Zurich, 8050 Zurich, Switzerland"},{"name":"Department of Mathematics, Science and Physical Education, University of Rwanda-College of Education, Rwamagana P.O. Box 55, Rwanda"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengtian","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Southwest Minzu University, Chengdu 610040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7733-6221","authenticated-orcid":false,"given":"Claudio J.","family":"Tessone","sequence":"additional","affiliation":[{"name":"UZH Blockchain Center, University of Zurich, 8050 Zurich, Switzerland"},{"name":"Blockchain & Distributed Ledger Technologies Group, Department of Informatics, University of Zurich, 8050 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.icte.2020.09.002","article-title":"Permissioned blockchain frameworks in the industry: A comparison","volume":"7","author":"Polge","year":"2021","journal-title":"ICT Express"},{"key":"ref_2","unstructured":"Nakamoto, S. (2022, October 01). Bitcoin: A Peer-to-Peer Electronic Cash System. Available online: https:\/\/bitcoin.org\/bitcoin.pdf."},{"key":"ref_3","unstructured":"Kalodner, H., M\u00f6ser, M., Lee, K., Goldfeder, S., Plattner, M., Chator, A., and Narayanan, A. (2020). Proceedings of the 29th USENIX Security Symposium, USENIX Association."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"286","DOI":"10.3389\/fphy.2020.00286","article-title":"Bitcoin Transaction Networks: An Overview of Recent Results","volume":"8","author":"Vallarano","year":"2020","journal-title":"Front. Phys."},{"key":"ref_5","first-page":"568","article-title":"Heterogeneous Preferential Attachment in Key Ethereum-Based Cryptoassets","volume":"9","author":"Partida","year":"2021","journal-title":"Front. Phys."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"De Collibus, F.M., Pi\u0161korec, M., Partida, A., and Tessone, C.J. (2022). The Structural Role of Smart Contracts and Exchanges in the Centralisation of Ethereum-Based Cryptoassets. Entropy, 24.","DOI":"10.3390\/e24081048"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1145\/2896384","article-title":"A fistful of Bitcoins: Characterizing payments among men with no names","volume":"59","author":"Meiklejohn","year":"2016","journal-title":"Commun. ACM"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jourdan, M., Blandin, S., Wynter, L., and Deshpande, P. (2018, January 17\u201320). Characterizing Entities in the Bitcoin Blockchain. Proceedings of the 2018 IEEE International Conference on Data Mining Workshops (ICDMW), Singapore.","DOI":"10.1109\/ICDMW.2018.00016"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhao, K., Dong, G., and Bian, D. (2023). Detection of Illegal Transactions of Cryptocurrency Based on Mutual Information. Electronics, 12.","DOI":"10.3390\/electronics12071542"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1108\/JFC-12-2016-0078","article-title":"Bitcoin transactions: A digital discovery of illicit activity on the blockchain","volume":"25","author":"Turner","year":"2018","journal-title":"J. Financial Crime"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5503","DOI":"10.1007\/s00500-022-07779-1","article-title":"Illegal activity detection on bitcoin transaction using deep learning","volume":"27","author":"Nerurkar","year":"2023","journal-title":"Soft Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1016\/j.dcan.2022.09.003","article-title":"Multi-input address incremental clustering for the Bitcoin blockchain based on Petri net model analysis","volume":"8","author":"Qin","year":"2022","journal-title":"Digit. Commun. Netw."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"59","DOI":"10.23919\/JCIN.2021.9387705","article-title":"Community Detection in Blockchain Social Networks","volume":"6","author":"Wu","year":"2021","journal-title":"J. Commun. Inf. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2016.09.002","article-title":"Community detection in networks: A user guide","volume":"659","author":"Fortunato","year":"2016","journal-title":"Phys. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Su, X., Xue, S., Liu, F., Wu, J., Yang, J., Zhou, C., Hu, W., Paris, C., Nepal, S., and Jin, D. (2022). A Comprehensive Survey on Community Detection With Deep Learning. IEEE Trans. Neural Networks Learn. Syst.","DOI":"10.1109\/TNNLS.2021.3137396"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, Y., Guo, Y., Wang, M., Xu, E., Xie, H., and Bie, R. (2023). Securing IOTA Blockchain Against Tangle Vulnerability by Using Large Deviation Theory. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2023.3283788"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, Y., Guo, Y., Wang, Y., and Bie, R. (2022). Toward Prevention of Parasite Chain Attack in IOTA Blockchain Networks by Using Evolutionary Game Model. Mathematics, 10.","DOI":"10.3390\/math10071108"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7821","DOI":"10.1073\/pnas.122653799","article-title":"Community structure in social and biological networks","volume":"99","author":"Girvan","year":"2002","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1145\/990308.990313","article-title":"On clusterings: Good, bad and spectral","volume":"51","author":"Kannan","year":"2004","journal-title":"J. ACM"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"026113","DOI":"10.1103\/PhysRevE.69.026113","article-title":"Finding and evaluating community structure in networks","volume":"69","author":"Newman","year":"2004","journal-title":"Phys. Rev. E"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"18001","DOI":"10.1209\/0295-5075\/90\/18001","article-title":"Modularity measure of networks with overlapping communities","volume":"90","author":"Vicsek","year":"2010","journal-title":"Europhys. Lett."},{"key":"ref_22","unstructured":"Janczewski, C. (2022). Two Arrested for Alleged Conspiracy to Launder $4.5 Billion in Stolen Cryptocurrency."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jourdan, M., Blandin, S., Wynter, L., and Deshpande, P. (2019, January 16\u201317). A probabilistic model of the bitcoin blockchain. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00337"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fphy.2021.681798","article-title":"The Complex Community Structure of the Bitcoin Address Correspondence Network","volume":"9","author":"Fischer","year":"2021","journal-title":"Front. Phys."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"M\u00f6ser, M., and Narayanan, A. (2022, January 2\u20136). Resurrecting Address Clustering in Bitcoin. Proceedings of the International Conference on Financial Cryptography and Data Security, Grand Anse, Grenada.","DOI":"10.1007\/978-3-031-18283-9_19"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.jnca.2018.02.011","article-title":"Community detection in networks: A multidisciplinary review","volume":"108","author":"Javed","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_27","unstructured":"MacQueen, J.B. (1967). Berkeley Symposium on Mathematical Statistics and Probability, University of California Press."},{"key":"ref_28","unstructured":"Ester, M., Kriegel, H.P., Sander, J., and Xu, X. (1996, January 2\u20134). A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Portland, Oregon, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/BF02289588","article-title":"Hierarchical clustering schemes","volume":"32","author":"Johnson","year":"1967","journal-title":"Psychometrika"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"He, K., Sun, Y., Bindel, D., Hopcroft, J., and Li, Y. (2015, January 14\u201317). Detecting Overlapping Communities from Local Spectral Subspaces. Proceedings of the IEEE International Conference on Data Mining (ICDM), Atlantic City, NJ, USA.","DOI":"10.1109\/ICDM.2015.89"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","article-title":"A tutorial on spectral clustering","volume":"17","year":"2007","journal-title":"Stat. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Andersen, R., Chung, F., and Lang, K. (2006, January 21\u201324). Local Graph Partitioning using PageRank Vectors. Proceedings of the IEEE Symposium on Foundations of Computer Science (FOCS), Berkeley, CA, USA.","DOI":"10.1109\/FOCS.2006.44"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5233","DOI":"10.1038\/s41598-019-41695-z","article-title":"From Louvain to Leiden: Guaranteeing well-connected communities","volume":"9","author":"Traag","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","article-title":"Fast Unfolding of Communities in Large Networks","volume":"2008","author":"Blondel","year":"2008","journal-title":"J. Stat. Mech. Theory Exp."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"036106","DOI":"10.1103\/PhysRevE.76.036106","article-title":"Near linear time algorithm to detect community structures in large-scale networks","volume":"76","author":"Raghavan","year":"2007","journal-title":"Phys. Rev. E"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Han, J., Li, W., Zhao, L., Su, Z., Zou, Y., and Deng, W. (2017). Community detection in dynamic networks via adaptive label propagation. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0188655"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2701","DOI":"10.1038\/s41598-023-29610-z","article-title":"Large network community detection by fast label propagation","volume":"13","author":"Traag","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Xie, J., Szymanski, B.K., and Liu, X. (2011, January 11). SLPA: Uncovering Overlapping Communities in Social Networks via a Speaker-Listener Interaction Dynamic Process. Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops, Vancouver, BC, Canada.","DOI":"10.1109\/ICDMW.2011.154"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cordasco, G., and Gargano, L. (2010, January 15). Community detection via semi-synchronous label propagation algorithms. Proceedings of the 2010 IEEE International Workshop on Business Applications of Social Network Analysis, Bangalore, India.","DOI":"10.1109\/BASNA.2010.5730298"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Coscia, M., Rossetti, G., Giannotti, F., and Pedreschi, D. (2012, January 12\u201316). DEMON: A Local-First Discovery Method for Overlapping Communities. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Beijing China.","DOI":"10.1145\/2339530.2339630"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1038\/nature03607","article-title":"Uncovering the overlapping community structure of complex networks in nature and society","volume":"435","author":"Palla","year":"2005","journal-title":"Nature"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1137\/0702016","article-title":"Calculating the Singular Values and Pseudo-Inverse of a Matrix","volume":"2","author":"Golub","year":"1965","journal-title":"J. Soc. Ind. Appl. Math. Ser. B Numer. Anal."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/j.asoc.2016.12.019","article-title":"Fuzzy clustering in community detection based on nonnegative matrix factorization with two novel evaluation criteria","volume":"69","author":"Binesh","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., and Mei, Q. (2015, January 18\u201322). LINE: Large-scale Information Network Embedding. Proceedings of the World Wide Web (WWW), Florence, Italy.","DOI":"10.1145\/2736277.2741093"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., and Skiena, S. (2014, January 24\u201327). DeepWalk: Online Learning of Social Representations. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), New York, NY, USA.","DOI":"10.1145\/2623330.2623732"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Grover, A., and Leskovec, J. (2016, January 13\u201317). Node2Vec: Scalable Feature Learning for Networks. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939754"},{"key":"ref_47","unstructured":"Yang, L., Cao, X., He, D., Wang, C., Wang, X., and Zhang, W. (2016, January 9\u201315). Modularity Based Community Detection with Deep Learning. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), New York, NY, USA."},{"key":"ref_48","first-page":"52","article-title":"Representation Learning on Graphs: Methods and Applications","volume":"40","author":"Hamilton","year":"2017","journal-title":"IEEE Data Eng. Bull."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1109\/TCSS.2020.3008860","article-title":"Sparse Nonnegative Matrix Factorization for Multiple-Local-Community Detection","volume":"7","author":"Kamuhanda","year":"2020","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chiang, W.L., Liu, X., Si, S., Li, Y., Bengio, S., and Hsieh, C.J. (2019, January 4\u20138). Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330925"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Yang, J., and Leskovec, J. (2013, January 4\u20138). Overlapping community Detection at Scale: A Nonnegative Matrix Factorization Approach. Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), Rome, Italy.","DOI":"10.1145\/2433396.2433471"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/7\/1069\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:13:02Z","timestamp":1760127182000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/7\/1069"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,16]]},"references-count":51,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["e25071069"],"URL":"https:\/\/doi.org\/10.3390\/e25071069","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,16]]}}}