TY - GEN
T1 - DE-crawler
T2 - 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
AU - Areekijseree, Katchaguy
AU - Soundarajan, Sucheta
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/24
Y1 - 2018/10/24
N2 - Over the past two decades, online social networks have attracted a great deal of attention from researchers. However, before one can gain insight into the behavior or structure of a network, one must first collect appropriate data. Data collection poses several challenges, such as API or bandwidth limits, which require the data collector to carefully consider which queries to make. Many network crawling methods have been proposed; however, their performance depends on network structure. In particular, our previous work in [1] has shown that existing algorithms tend to either (1) Do well at exploring dense areas of a network, but have difficulty in transitioning to new areas of the network, or (2) Easily move between network regions, but fail to fully explore each region. In this work, we introduce DE-Crawler, a novel network crawler that attempts to capture the best of both worlds. DE-Crawler consists of two main stages: Densification, in which the crawler aims to find as many nodes as possible in the current dense region (or community), and Expansion, in which the crawler tries to escape from its current region and move to another dense region. We show that DE-Crawler performs well across networks with different structural properties, outperforming baseline algorithms by up to 28%.
AB - Over the past two decades, online social networks have attracted a great deal of attention from researchers. However, before one can gain insight into the behavior or structure of a network, one must first collect appropriate data. Data collection poses several challenges, such as API or bandwidth limits, which require the data collector to carefully consider which queries to make. Many network crawling methods have been proposed; however, their performance depends on network structure. In particular, our previous work in [1] has shown that existing algorithms tend to either (1) Do well at exploring dense areas of a network, but have difficulty in transitioning to new areas of the network, or (2) Easily move between network regions, but fail to fully explore each region. In this work, we introduce DE-Crawler, a novel network crawler that attempts to capture the best of both worlds. DE-Crawler consists of two main stages: Densification, in which the crawler aims to find as many nodes as possible in the current dense region (or community), and Expansion, in which the crawler tries to escape from its current region and move to another dense region. We show that DE-Crawler performs well across networks with different structural properties, outperforming baseline algorithms by up to 28%.
UR - http://www.scopus.com/inward/record.url?scp=85057321980&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057321980&partnerID=8YFLogxK
U2 - 10.1109/ASONAM.2018.8508311
DO - 10.1109/ASONAM.2018.8508311
M3 - Conference contribution
AN - SCOPUS:85057321980
T3 - Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
SP - 164
EP - 169
BT - Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
A2 - Tagarelli, Andrea
A2 - Reddy, Chandan
A2 - Brandes, Ulrik
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 August 2018 through 31 August 2018
ER -