ABSTRACT
Blockchain technology provides a groundbreaking computing paradigm that tackles problems in a completely decentralised manner. As the underlying infrastructure and protocol of blockchain, blockchain networks convey communications and coordination across all involving participants. In extensive application scenarios, conducting community detection over blockchain networks has potential effects on both discovering hidden information and enhancing communicating efficiency. However, the decentralised nature poses a restriction on community detection over blockchain networks. In coping with this restriction, we propose a distributed community detection method based on the Propose-Select-Adjust (PSA) framework that runs in an asynchronous way. We extend the PSA framework using the concept of structural entropy and aim to detect a community structure with low entropy. We test our entropy-based distributed community detection algorithm on both benchmark networks and bitcoin trust networks. Experimental results reveal that our algorithm successfully detects communities with low structural entropy.
- Ronald S Burt. 2004. Structural Holes and Good Ideas. Amer. J. Sociology, Vol. 110, 2 (2004), 349--399.Google ScholarCross Ref
- Kousik Das, Sovan Samanta, and Madhumangal Pal. 2018. Study on centrality measures in social networks: a survey. Social Network Analysis and Mining, Vol. 8, 1 (2018), 13.Google ScholarCross Ref
- Michael Fleder, Michael S Kester, and Sudeep Pillai. 2015. Bitcoin transaction graph analysis. arXiv preprint arXiv:1502.01657 (2015).Google Scholar
- Santo Fortunato. 2010. Community detection in graphs. Physics reports, Vol. 486, 3--5 (2010), 75--174.Google Scholar
- Michelle Girvan and Mark EJ Newman. 2002. Community structure in social and biological networks. Proceedings of the national academy of sciences, Vol. 99, 12 (2002), 7821--7826.Google ScholarCross Ref
- Bakhadyr Khoussainov, Jiamou Liu, and Imran Khaliq. 2009. A dynamic algorithm for reachability games played on trees. In International Symposium on Mathematical Foundations of Computer Science. Springer, 477--488. Google ScholarDigital Library
- Srijan Kumar, Bryan Hooi, Disha Makhija, Mohit Kumar, Christos Faloutsos, and VS Subrahmanian. 2018. Rev2: Fraudulent user prediction in rating platforms. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM, 333--341. Google ScholarDigital Library
- Srijan Kumar, Francesca Spezzano, VS Subrahmanian, and Christos Faloutsos. 2016. Edge weight prediction in weighted signed networks. In Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 221--230.Google ScholarCross Ref
- Angsheng Li and Yicheng Pan. 2016. Structural information and dynamical complexity of networks. IEEE Transactions on Information Theory, Vol. 62, 6 (2016), 3290--3339.Google ScholarDigital Library
- Angsheng Li and Yicheng Pan. 2018. Structure Entropy and Resistor Graphs. arXiv preprint arXiv:1801.03404 (2018).Google Scholar
- Jiamou Liu and Ziheng Wei. 2014a. Community detection based on graph dynamical systems with asynchronous runs. In Computing and Networking (CANDAR), 2014 Second International Symposium on. IEEE, 463--469. Google ScholarDigital Library
- Jiamou Liu and Ziheng Wei. 2014b. From a local to a global perspective of community detection in networks. In Pacific Rim International Conference on Artificial Intelligence. Springer, 1036--1049.Google ScholarCross Ref
- David Lusseau and Mark EJ Newman. 2004. Identifying the role that animals play in their social networks. Proceedings of the Royal Society of London B: Biological Sciences, Vol. 271, Suppl 6 (2004), S477--S481.Google ScholarCross Ref
- Anastasia Moskvina and Jiamou Liu. 2016a. Integrating networks of equipotent nodes. In International Conference on Computational Social Networks. Springer, 39--50.Google ScholarCross Ref
- Anastasia Moskvina and Jiamou Liu. 2016b. Togetherness: an algorithmic approach to network integration. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 223--230. Google ScholarDigital Library
- Satoshi Nakamoto. 2008. Bitcoin: A peer-to-peer electronic cash system. (2008).Google Scholar
- Mark EJ Newman. 2004. Fast algorithm for detecting community structure in networks. Physical review E, Vol. 69, 6 (2004), 066133.Google Scholar
- Mark EJ Newman and Michelle Girvan. 2004. Finding and evaluating community structure in networks. Physical review E, Vol. 69, 2 (2004), 026113.Google Scholar
- Gergely Palla, Albert-László Barabási, and Tamás Vicsek. 2007. Quantifying social group evolution. Nature, Vol. 446, 7136 (2007), 664.Google Scholar
- Gergely Palla, Imre Derényi, Illés Farkas, and Tamás Vicsek. 2005. Uncovering the overlapping community structure of complex networks in nature and society. Nature, Vol. 435, 7043 (2005), 814.Google ScholarCross Ref
- Thai Pham and Steven Lee. 2016. Anomaly detection in bitcoin network using unsupervised learning methods. arXiv preprint arXiv:1611.03941 (2016).Google Scholar
- Cazabet Remy, Baccour Rym, and Latapy Matthieu. 2017. Tracking bitcoin users activity using community detection on a network of weak signals. In International Workshop on Complex Networks and their Applications. Springer, 166--177.Google Scholar
- Gokhan Sagirlar, Barbara Carminati, and Elena Ferrari. 2018. AutoBotCatcher: Blockchain-based P2P Botnet Detection for the Internet of Things. In 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC). IEEE, 1--8.Google ScholarCross Ref
- David Shrier, Weige Wu, and Alex Pentland. 2016. Blockchain & infrastructure (identity, data security). Massachusetts Institute of Technology-Connection Science, Vol. 1, 3 (2016).Google Scholar
- Melanie Swan. 2015. Blockchain: Blueprint for a new economy ." O'Reilly Media, Inc.". Google ScholarDigital Library
- Duncan J Watts. 2004. Small worlds: the dynamics of networks between order and randomness. Vol. 9. Princeton university press. Google ScholarDigital Library
- Xiwei Xu, Cesare Pautasso, Liming Zhu, Vincent Gramoli, Alexander Ponomarev, An Binh Tran, and Shiping Chen. 2016. The blockchain as a software connector. In 2016 13th Working IEEE/IFIP Conference on Software Architecture (WICSA). IEEE, 182--191.Google ScholarCross Ref
- Bo Yan, Yang Chen, and Jiamou Liu. 2017. Dynamic relationship building: exploitation versus exploration on a social network. In International Conference on Web Information Systems Engineering. Springer, 75--90.Google ScholarDigital Library
- Wayne W Zachary. 1977. An information flow model for conflict and fission in small groups. Journal of anthropological research, Vol. 33, 4 (1977), 452--473.Google ScholarCross Ref
Index Terms
- Distributed Community Detection over Blockchain Networks Based on Structural Entropy
Recommendations
Network entropy based overlapping community detection in social networks
ICC '17: Proceedings of the Second International Conference on Internet of things, Data and Cloud ComputingThe structural analysis of Social networks is gaining more importance over recent years. The most important structural property of social network is community structure and to detect such structures a novel approach is proposed by adopting the ...
A Community Detection-Based Blockchain Sharding Scheme
Blockchain – ICBC 2022AbstractSharding has been considered a promising approach to improving blockchain scalability. However, multiple shards result in a large number of cross-shard transactions, which require a long confirmation time across shards and thus restrain the ...
Distributed Community Detection in Complex Networks
CICSYN '11: Proceedings of the 2011 Third International Conference on Computational Intelligence, Communication Systems and NetworksNetwork analysis is an important and interesting area of research with many applications in different domains. One of the challenges in network analysis is community detection. Community detection is the process of partitioning the network into some ...
Comments