In this section
@article{Chamberlain:2018:10.1371/journal.pone.0188702,
author = {Chamberlain, B and Levy-Kramer, J and Humby, C and Deisenroth, MP},
doi = {10.1371/journal.pone.0188702},
journal = {PLoS ONE},
title = {Real-time community detection in full social networks on a laptop},
url = {http://dx.doi.org/10.1371/journal.pone.0188702},
volume = {13},
year = {2018}
}
TY - JOUR
AB - For a broad range of research and practical applications it is important to understand the allegiances, communities and structure of key players in society. One promising direction towards extracting this information is to exploit the rich relational data in digital social networks (the social graph). As global social networks (e.g., Facebook and Twitter) are very large, most approaches make use of distributed computing systems for this purpose. Distributing graph processing requires solving many difficult engineering problems, which has lead some researchers to look at single-machine solutions that are faster and easier to maintain. In this article, we present an approach for analyzing full social networks on a standard laptop, allowing for interactive exploration of the communities in the locality of a set of user specified query vertices. The key idea is that the aggregate actions of large numbers of users can be compressed into a data structure that encapsulates the edge weights between vertices in a derived graph. Local communities can be constructed by selecting vertices that are connected to the query vertices with high edge weights in the derived graph. This compression is robust to noise and allows for interactive queries of local communities in real-time, which we define to be less than the average human reaction time of 0.25s. We achieve single-machine real-time performance by compressing the neighborhood of each vertex using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines operating on the full graph to milliseconds on standard laptops. Our method allows exploration of strongly associated regions (i.e., communities) of large graphs in real-time on a laptop. It has been deployed in software that is actively used by social network analysts and offers another channel for media owners to monetize their data, helping them to continue to provide
AU - Chamberlain,B
AU - Levy-Kramer,J
AU - Humby,C
AU - Deisenroth,MP
DO - 10.1371/journal.pone.0188702
PY - 2018///
SN - 1932-6203
TI - Real-time community detection in full social networks on a laptop
T2 - PLoS ONE
UR - http://dx.doi.org/10.1371/journal.pone.0188702
UR - http://hdl.handle.net/10044/1/56397
VL - 13
ER -