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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 holistic influence maximization, denoted as HIM query, for targeted advertisements in a spatial social network. The HIM 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 HIM 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. [ABSTRACT FROM AUTHOR]

The widespread availability of GPS and the growing popularity of location based social networking applications such as Flickr, Yelp, etc., enable more and more users to share their route activities or trajectories. At the same time, the recent advancement in large-scale 3D modeling has inspired applications that combine visibility and spatial queries, which in turn can be integrated with user trajectories to provide answers for many real-life user queries, such as "How can I choose the route which provides the best view of a historic site?". In this work, we propose and investigate the k Aggregate Maximum Visibility Trajectory (k AMVT) query and its variants. Given sets of targets, obstacles, and trajectories, the k AMVT query finds top-k trajectories that provide the best view of the targets. We extend the k AMVT query to incorporate different weights (or preferences) with trajectories and targets. To provide an efficient solution to our problem, we employ obstacle and trajectory pruning mechanisms. We also employ an effective target ordering technique, which can further improve query efficiency. Furthermore, we extend the proposed queries to introduce preferences on trajectories in situations where smaller trajectories are preferred due to time constraints, or trajectories closer to the query user are preferred. To verify the efficiency and effectiveness of our solutions, we conduct an extensive experimental study using large synthetic and real datasets. [ABSTRACT FROM AUTHOR]

The ever increasing size of graphs makes them difficult to query and store. In this paper, we present Shrink , a compression method that reduces the size of the graph while preserving the distances between the nodes. The compression is based on the iterative merging of the nodes. During each merging, a system of linear equations is solved to define new edge weights in a way that the new weights have the least effect on the distances. Merging nodes continues until the desired size for the compressed graph is reached. The compressed graph, also known as the coarse graph, can be queried without decompression. As the complexity of distance-based queries such as shortest path queries is highly dependent on the size of the graph, Shrink improves the performance in terms of time and storage. Shrink not only provides the length of the shortest path but also identifies the nodes on the path. The approach has been applied to both weighted and unweighted graphs including road network, friendship network, collaboration network, web graph and social network. In the experiment, a road network with more than 2.5 million nodes is reduced to fifth while the average relative error is less than 1%. [ABSTRACT FROM AUTHOR]

Influential community search (ICS) on a graph finds a closely connected group of vertices having a dominance over other groups of vertices. The ICS has many applications in recommendations, event organization, and so on. In this paper, we introduce a new variant of ICS, namely keyword-aware influential community query (KICQ), that finds the communities with the highest influential scores and whose keywords match with the query terms (a set of keywords) and predicates (AND or OR). It is challenging to find such communities from a large network as the traditional pre-computation approach is not applicable with the change of query terms at every instance of the search. To solve this problem, we design two efficient algorithms: (i) a branch-and-bound approach that exploits the bounds computed from already explored communities to prune the search space, and (ii) a novel index based approach that hierarchically organizes sub-communities and keywords with associated bounds to quickly identify the desired communities. We propose a new influence measure for a community that considers both the cohesiveness and influence of the community and eliminates the need for specifying values of internal parameters of a network. We present detailed experiments and a case study to demonstrate the effectiveness and efficiency of the proposed approaches. • The query considers user preference (as keywords) for influential community search. • Considering the semantics of the keywords adds flexibility for the users. • The proposed influence measure eliminates the need for internal parameters as input. • A novel index is proposed to efficiently retrieve the most influential communities. • The datasets are shared publicly to help future works in this direction. [ABSTRACT FROM AUTHOR]

To meet the requirement of social influence analytics in various applications, the problem of influence maximization has been studied in recent years. The aim is to find a limited number of nodes (i.e., users) which can activate (i.e. influence) the maximum number of nodes in social networks. However, the community diversity of influenced users is largely ignored even though it has unique value in practice. For example, the higher community diversity reduces the risk of marketing campaigns as you should not put all your eggs in one basket; the diversity can also prolong the effect of a marketing campaign in the future promotion. Motivated by this observation, this paper investigates Community-diversified Influence Maximization (CDIM) problem to efficiently find k nodes such that, if a message is initiated and spread by the k nodes, the number as well as the community diversity of the activated nodes will be maximized at the end of propagation process. This work proposes a metric to measure the community-diversified influence and addresses a series of computational challenges. Two algorithms and an innovative CPSP-Tree index have been developed. This study also investigates the situation that community definition is not specified. The effectiveness and efficiency of the proposed solutions have been verified through extensive experimental studies on five real-world social network datasets. • A new problem, named community-diversified influence maximization (CDIM), is introduced in this paper. • A deliberately designed metric has been proposed in this paper to evaluate the CDIM. • Two algorithms (greedy and upper bound) are developed in this paper to solve the CDIM problem effectively. [ABSTRACT FROM AUTHOR]