Improving Efficiency of Link Clustering on Multi-Core Machines

被引：1
|
作者：
Yan, Guanhua [1 ]
机构：
[1] SUNY Binghamton, Binghamton, NY 13902 USA
来源：
2017 IEEE 37TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2017) | 2017年
关键词：
link clustering; algorithm; modeling; multi-core;
D O I：
10.1109/ICDCS.2017.126
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Link clustering groups different edges in a graph according to their similarities. Link clustering can reveal the overlapping and hierarchical organizations in a wide spectrum of networks. This work studies how to improve efficiency of link clustering along three dimensions, algorithm, modeling, and parallelization, on multi-core machines. We evaluate the efficiency improved due to each of the three dimensions using word association graphs extracted from a twitter dataset.
引用
收藏
页码：2017 / 2024
页数：8
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