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An Efficient Computation Framework for Connection Discovery using Shared Images

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Published:29 August 2017Publication History
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Abstract

With the advent and popularity of the social network, social graphs become essential to improve services and information relevance to users for many social media applications to predict follower/followee relationship, community membership, and so on. However, the social graphs could be hidden by users due to privacy concerns or kept by social media. Recently, connections discovered from user-shared images using machine-generated labels are proved to be more accessible alternatives to social graphs. But real-time discovery is difficult due to high complexity, and many applications are not possible. This article proposes an efficient computation framework for connection discovery using user-shared images, which is suitable for any image processing and computer vision techniques for connection discovery on the fly. The framework includes the architecture of online computation to facilitate real-time processing, offline computation for a complete processing, and online/offline communication. The proposed framework is implemented to demonstrate its effectiveness by speeding up connection discovery through user-shared images. By studying 300K+ user-shared images from two popular social networks, it is proven that the proposed computation framework reduces 90% of runtime with a comparable accurate with existing frameworks.

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