Real-time association mining in large social networks.

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Title: Real-time association mining in large social networks.
Authors: Chamberlain, BP
Levy-Kramer, J
Humby, C
Deisenroth, MP
Item Type: Working Paper
Abstract: There is a growing realisation that to combat the waning effectiveness of traditional marketing, social media platform owners need to find new ways to monetise their data. Social media data contains rich information describing how real world entities relate to each other. Understanding the allegiances, communities and structure of key entities is of vital importance for decision support in a swathe of industries that have hitherto relied on expensive, small scale survey data. In this paper, we present a real-time method to query and visualise regions of networks that are closely related to a set of input vertices. The input vertices can define an industry, political party, sport etc. The key idea is that in large digital social networks measuring similarity via direct connections between nodes is not robust, but that robust similarities between nodes can be attained through the similarity of their neighbourhood graphs. We are able to achieve real-time performance by compressing the neighbourhood graphs using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines to milliseconds on standard laptops. Our method allows analysts to interactively explore strongly associated regions of large networks in real time. Our work has been deployed in software that is actively used by analysts to understand social network structure.
Issue Date: 31-Dec-2016
URI: http://hdl.handle.net/10044/1/32280
Copyright Statement: © 2016 The Authors
Keywords: cs.SI
stat.ML
Appears in Collections:Faculty of Engineering
Computing



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