Algorithm

World Wide Web

Theoretical computer science

Set (abstract data type)

Computer science

Mathematical optimization

Social network (sociolinguistics)

Greedy algorithm

Mathematics

Social media

Data science

Information retrieval

Programming language

Maximization

IEEE Transactions on Knowledge and Data Engineering

Influence maximization has recently received significant attention for scheduling online campaigns or advertisements on social network platforms. However, most studies only focus on user influence via cyber interactions while ignoring their physical interactions which are also essential to gauge influence propagation. Additionally, targeted campaigns or advertisements have not received sufficient attention. To address these issues, we first devise a novel holistic influence diffusion model that takes into account both cyber and physical user interactions in an effective and practical way. Based on the new diffusion model, we formulate a new problem of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">holistic influence maximization</i> , denoted as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HIM</i> query, for targeted advertisements in a spatial social network. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HIM</i> query problem aims to find a minimum set of users whose holistic influence can cover all target users in the network, which belongs to a set covering problem. Since the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HIM</i> query problem is NP-hard, we develop a greedy baseline algorithm and then improve on this algorithm to reduce the computational cost. To deal with large networks, we also design a spatial-social index to maintain the social, spatial and textual information of users, as well as developing an index-based efficient solution. Finally, we conduct extensive experiments using one synthetic and three real-world datasets to validate the efficiency and effectiveness of the proposed holistic influence diffusion model and our developed algorithms.

Target-aware Holistic Influence Maximization in Spatial Social Networks