Abstract
Detecting clusters or communities in large graphs from the real world, such as the Amazon dataset, information networks, and social networks, is of considerable interest. Extracting sets of nodes connected to the goal function and “appearing” to be appropriate communities for the application of interest requires approximation methods or heuristics. Several network community identification approaches are analyzed and compared to determine their relative performance in this research. We investigate a variety of well-known performance metrics used to formalize the idea of a good community and several approximation strategies intended to optimize these objective functions. Most widely used community detection algorithms include: Louvain, Girvan-Newman (GNM), Label Propagation (LPA), and Clauset Newman (CNM). Researchers proved that louvain gives the best overall performance in terms of modularity as well as F1-Score. This work investigates a dynamic, publicly accessible Amazon item dataset, Amazon co-purchase network dataset. In this work, four community detection algorithms are incorporated to Amazon dataset and evaluated for metrics: Modularity F1-score. GNM has the advantage of giving the best modularity but it’s not an efficient technique for large datasets as its complexity lies in the range of O(m2n). All other algorithms have nearly the same range of modularity but Louvain has the best performance in terms of F1-score.
Inder Singh and Manoj Kumar—These authors contributed equally to this work.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yang J, McAuley J,Leskovec J (2013) Community detection in networks with node attributes. In: Proceedings of the IEEE international conference on data mining, ICDM, pp 1151–1156. https://doi.org/10.1109/ICDM.2013.167
Fatemi M,Tokarchuk L (2013) A community based social recommender system for individuals groups. In: Proceedings of the social computing, pp 351–356https://doi.org/10.1109/SocialCom.2013.55
Tang J, Hu X, Liu H (2013) Social recommendation: a review 3(4)
Ghouchan R, Noor N (2022) RecMem: time aware recommender systems based on memetic, vol 2022
Leung KWT, Lee DL, Lee WC (2011) CLR: a collaborative location recommendation framework based on co-clustering. In: SIGIR’11-Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval, pp 305–314. https://doi.org/10.1145/2009916.2009960
Wang Y, Yin G, Cai Z, Dong Y, Dong H (2015) A trust-based probabilistic recommendation model for social networks. J Netw Comput Appl 55:59–67. https://doi.org/10.1016/j.jnca.2015.04.007
Moradi P,Rezaimehr F, Ahmadian S, Jalili M (2017) A trust-aware recommender algorithm based on users overlapping community structure. In: 16th international conference on advances in ICT for emerging regions. ICTer 2016, pp 162–167. https://doi.org/10.1109/ICTER.2016.7829914
Hao M, Zhou D, Liu C, Lyu MR, King I (2011) Recommender systems with social regularization. In: Proceedings of the 4th ACM international conference web search data mining, WSDM 2011, pp 287–296. https://doi.org/10.1145/1935826.1935877
Wang H, Chen D, Zhang J (2020) Group recommendation based on hybrid trust metric. Automatika 61(4):694–703. https://doi.org/10.1080/00051144.2020.1715590
Huang X, Chen D, Ren T, Wang D (2021) A survey of community detection methods in multilayer networks, vol 35, no 1. Springer US
Gasparetti F, Micarelli A, Sansonetti G (2017) Encyclopedia of social network analysis and mining. Encycl Soc Netw Anal Min, no January. https://doi.org/10.1007/978-1-4614-7163-9
Leskovec J, Adamic LA, Huberman BA (2012) The dynamics of viral marketing. ACM Trans Web 1(1). https://doi.org/10.1145/1232722.1232727
Oestreicher-Singer G, Sundararajan A (2012) Linking network structure to ecommerce demand: theory and evidence from Amazon. Com’s copurchase network. TPRC 2006
Yang L, Xin-Sheng J, Caixia L, Ding W (2014) Detecting local community structures in networks based on boundary identification. Math Probl Eng, vol 2014. https://doi.org/10.1155/2014/682015
Oestreicher-Singer G, Sundararajan A (2007) Linking network structure to ecommerce demand: theory and evidence from Amazon. com’s copurchase network. In: 34th telecommunications policy research conference, pp 1–14
Jebabli M, Cherifi H, Cherifi C, Hamouda A (2016) Overlapping community detection versus ground-truth in AMAZON co-purchasing network. In: Proceedings of the international conference on signal-image technology & internet-based systems. SITIS 2015, pp 328–336. https://doi.org/10.1109/SITIS.2015.47
Basuchowdhuri P, Shekhawat MK, Saha SK (2014) Analysis of product purchase patterns in a co-purchase network. In: Proceedings of the 4th international conference on emerging applications of information technology. EAIT 2014, pp 355–360. https://doi.org/10.1109/EAIT.2014.11
Jia Y, Zhang Q, Zhang W, Wang X (2019) CommunityGan: community detection with generative adversarial nets. In: Web conference 2019-Proceedings of the world wide web conference. WWW 2019, pp 784–794. https://doi.org/10.1145/3308558.3313564
Prakash GL, Prateek M, Singh I (2015) Graph structured data security using trustedthird party query process in cloud computing. Int J Comput Netw Inf Secur 7(7):30–36. https://doi.org/10.1145/5815/ijcnis.2015.07.04
Chaudhary L, Singh B (2019) Community-driven collaborative recommendation system. Int J Recent Technol Eng 8(4):3722–3726. https://doi.org/10.35940/ijrte.d8112.118419
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Choudhary, C., Singh, I., Kumar, M. (2023). An Empirical Comparison of Community Detection Techniques for Amazon Dataset. In: Yadav, A., Gupta, G., Rana, P., Kim, J.H. (eds) Proceedings on International Conference on Data Analytics and Computing. ICDAC 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 175. Springer, Singapore. https://doi.org/10.1007/978-981-99-3432-4_15
Download citation
DOI: https://doi.org/10.1007/978-981-99-3432-4_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-3431-7
Online ISBN: 978-981-99-3432-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)