Paper Info
Title
Benchmarking community detection methods on social media data
Abstract
Benchmarking the performance of community detection methods on empirical social network data has been identified as critical for improving these methods. In particular, while most current research focuses on detecting communities in data that has been digitally extracted from large social media and telecommunications services, most evaluation of this research is based on small, hand-curated datasets. We argue that these two types of networks differ so significantly that by evaluating algorithms solely on the former, we know little about how well they perform on the latter. To address this problem, we consider the difficulties that arise in constructing benchmarks based on digitally extracted network data, and propose a task-based strategy which we feel addresses these difficulties. To demonstrate that our scheme is effective, we use it to carry out a substantial benchmark based on Facebook data. The benchmark reveals that some of the most popular algorithms fail to detect fine-grained community structure.
Year
Venue
Field
2013
CoRR
Data science,Data mining,World Wide Web,Community structure,Social media,Social network,Computer science,Network data,Telecommunications service,Benchmarking
DocType
Volume
Citations 
Journal
abs/1302.0739
4
PageRank 
References 
Authors
0.44
6
2
Name
Order
Citations
PageRank
Conrad Lee191.61
Pádraig Cunningham23086218.37