TY - GEN
T1 - GPU-Accelerated Maximal Bicliques Mining Framework for Large E-commerce Networks
AU - Li, Jingdong
AU - Li, Zhao
AU - Wang, Xiaoling
AU - Lu, Xingjian
AU - Zhang, Ji
AU - Chen, Hongyang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Many of Taobaos important daily data mining tasks, such as anomaly attack detection and interest group detection, require efficient algorithmic solutions for mining specific graph patterns. The most common graph pattern is biclique which has a very dense structure and often contains rich implicit information. An important question to address is whether and how we can efficiently find all the interesting bicliques in large e-commerce networks, which is coined as the Maximal Biclique Enumeration problem (MBE). MBE involves enumerating all the maximal bicliques in the given graph, which is rather computationally expensive for large networks. However, recent research works on MBE haven't made good use of GPU, a very widely used high-speed computing resource. In this paper, we propose GMBE, a novel framework that achieves an efficient utilization of the power of GPUs to parallelize the MBE algorithm to find all maximal bicliques. We design a programmable API for data analysts to meet different business needs, enabling GMBE to become the middleware to effectively support various graph mining applications in e-commerce domain. Extensive experiments show that GMBE achieves significant (12X) speedup on average over the state-of-the-art MBE algorithms.
AB - Many of Taobaos important daily data mining tasks, such as anomaly attack detection and interest group detection, require efficient algorithmic solutions for mining specific graph patterns. The most common graph pattern is biclique which has a very dense structure and often contains rich implicit information. An important question to address is whether and how we can efficiently find all the interesting bicliques in large e-commerce networks, which is coined as the Maximal Biclique Enumeration problem (MBE). MBE involves enumerating all the maximal bicliques in the given graph, which is rather computationally expensive for large networks. However, recent research works on MBE haven't made good use of GPU, a very widely used high-speed computing resource. In this paper, we propose GMBE, a novel framework that achieves an efficient utilization of the power of GPUs to parallelize the MBE algorithm to find all maximal bicliques. We design a programmable API for data analysts to meet different business needs, enabling GMBE to become the middleware to effectively support various graph mining applications in e-commerce domain. Extensive experiments show that GMBE achieves significant (12X) speedup on average over the state-of-the-art MBE algorithms.
KW - GPU framework
KW - biclique
KW - e-commerce network
KW - graph mining
UR - https://www.scopus.com/pages/publications/85191323576
U2 - 10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00102
DO - 10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00102
M3 - 会议稿件
AN - SCOPUS:85191323576
T3 - Proceedings - 2023 IEEE International Conference on Parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking, ISPA/BDCloud/SocialCom/SustainCom 2023
SP - 539
EP - 544
BT - Proceedings - 2023 IEEE International Conference on Parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking, ISPA/BDCloud/SocialCom/SustainCom 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023
Y2 - 21 December 2023 through 24 December 2023
ER -
