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An Efficient Computation Framework for Connection Discovery using Shared Images

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.