Location-aware Social Networking Research Articles

HFUL: a hybrid framework for user account linkage across location-aware social networks

Sources of complementary information are connected when we link user accounts belonging to the same user across different platforms or devices. The expanded information promotes the development of a wide range of applications, such as cross-platform prediction, cross-platform recommendation, and advertisement. Due to the significance of user account linkage and the widespread popularization of GPS-enabled mobile devices, there are increasing research studies on linking user account with spatio-temporal data across location-aware social networks. Being different from most existing studies in this domain that only focus on the effectiveness, we propose a novel framework entitled HFUL (A Hybrid Framework for User Account Linkage across Location-Aware Social Networks), where efficiency, effectiveness, scalability, robustness, and application of user account linkage are considered. Specifically, to improve the efficiency, we develop a comprehensive index structure from the spatio-temporal perspective, and design novel pruning strategies to reduce the search space. To improve the effectiveness, a kernel density estimation-based method has been proposed to alleviate the data sparsity problem in measuring users’ similarities. Additionally, we investigate the application of HFUL in terms of user prediction, time prediction, and location prediction. The extensive experiments conducted on three real-world datasets demonstrate the superiority of HFUL in terms of effectiveness, efficiency, scalability, robustness, and application compared with the state-of-the-art methods.

Learning Social Relations and Spatiotemporal Trajectories for Next Check-in Inference.

The proliferation of location-aware social networks (LSNs) has facilitated the research of user mobility modeling and check-in prediction, thereby benefiting various downstream applications such as precision marketing and urban management. Most of the existing studies only focus on predicting the spatial aspect of check-ins, whereas the joint inference of the spatial and temporal aspects more fits the real application scenarios. Moreover, although social relations have been extensively studied in a recommender system, only a few efforts have been observed in the next check-in location prediction, leaving room for further improvement. In this article, we study the next check-in inference problem, which demands the joint inference of the next check-in location (Where) and time (When) for a target user (Who). We devise a model named ARNPP-GAT, which combines an attention-based recurrent neural point process with a graph attention networks. The core technical insight of ARNPP-GAT is to integrate user long-term representation learning, short-term behavior modeling, and temporal point process into a unified architecture. Specifically, ARNPP-GAT first leverages graph attention networks to learn the long-term representation of users by encoding their social relations. More importantly, the ARNPP endows the model with the capability of characterizing the effects of past check-in events and performing multitask learning to yield the next check-in time and location prediction. Empirical results on two real-world data sets demonstrate that ARNPP-GAT is superior compared with several competitors, validating the contributions of multitask learning and social relation modeling.

Social Link Inference via Multiview Matching Network From Spatiotemporal Trajectories.

In this article, we investigate the problem of social link inference in a target location-aware social network (LSN), which aims at predicting the unobserved links between users within the network. This problem is critical for downstream applications, including network completion and friend recommendation. In addition to the network structures commonly used in general link prediction, the studies tailored for social link inference in an LSN leverage user trajectories from the spatial aspect. However, the temporal factor lying in user trajectories is largely overlooked by most of the prior studies, limiting the capabilities of capturing the temporal relevance between users. Moreover, effective user matching by fusing different views, i.e., social, spatial, and temporal factors, remains unresolved, which hinders the potential improvement of link inference. To this end, this article devises a novel multiview matching network (MVMN) by regarding each of the three factors as one view of any target user pair. MVMN enjoys the flexibility and completeness of modeling each factor by developing its suitable matching module: 1) location matching module; 2) time-series matching module; and 3) relation matching module. Each module learns a view-specific representation for matching, and MVMN fuses them for final link inference. Extensive experiments on two real-world data sets demonstrate the superiority of our approach against several competitive baselines for link prediction and sequence matching, validating the contribution of its key components.

Targeted Influential Nodes Selection in Location-Aware Social Networks

Given a target area and a location-aware social network, the location-aware influence maximization problem aims to find a set of seed users such that the information spread from these users will reach the most users within the target area. We show that the problem is NP-hard and present an approximate algorithm framework, namely, TarIM-SF, which leverages on a popular sampling method as well as spatial filtering model working on arbitrary polygons. Besides, for the large-scale network we also present a coarsening strategy to further improve the efficiency. We theoretically show that our approximate algorithm can provide a guarantee on the seed quality. Experimental study over three real-world social networks verified the seed quality of our framework, and the coarsening-based algorithm can provide superior efficiency.

Geo-Social Influence Spanning Maximization

Influence maximization is a recent but well-studied problem which helps identify a small set of users that are most likely to “influence” the maximum number of users in a social network. The problem has attracted a lot of attention as it provides a way to improve marketing, branding, and product adoption. However, existing studies rarely consider the physical locations of the users, but location is an important factor in targeted marketing. In this paper, we propose and investigate the problem of influence maximization in location-aware social networks, or, more generally, Geo-social Influence Spanning Maximization . Given a query $q$ composed of a region $R$ , a regional acceptance rate $\rho$ , and an integer $k$ as a seed selection budget, our aim is to find the maximum geographic spanning regions (MGSR). We refer to this as the MGSR problem. Our approach differs from previous work as we focus more on identifying the maximum spanning geographical regions within a region $R$ , rather than just the number of activated users in the given network like the traditional influence maximization problem [14] . Our research approach can be effectively used for online marketing campaigns that depend on the physical location of social users. To address the MGSR problem, we first prove NP-Hardness. Next, we present a greedy algorithm with a $1-1/e$ approximation ratio to solve the problem, and further improve the efficiency by developing an upper bounded pruning approach. Then, we propose the OIR*-Tree index, which is a hybrid index combining ordered influential node lists with an R*-tree. We show that our index based approach is significantly more efficient than the greedy algorithm and the upper bounded pruning algorithm, especially when $k$ is large. Finally, we evaluate the performance for all of the proposed approaches using three real datasets.

Exploiting location-aware social networks for efficient spatial query processing

In this paper, we introduce two watchtower-based parameter-tunable frameworks for efficient spatial processing with sparse distributions of Points of Interest (POIs) by exploiting mobile users' check-in data collected from the location-aware social networks. In our proposed frameworks, the network traversal can terminate earlier by retrieving the distance information stored in watchtowers. More important, by observing that people's movement often exhibits a strong spatial pattern, we employ Bayesian Information Criterion-based cluster analysis to model mobile users' check-in data as a mixture of 2-dimensional Gaussian distributions, where each cluster corresponds to a geographical hot zone. Afterwards, POI watchtowers are established in the hot zones and non-hot zones discriminatorily. Moreover, we discuss the optimal watchtower deployment mechanism in order to achieve a desired balance between the off-line pre-computation cost and the on-line query efficiency. Finally, the superiority of our solutions over the state-of-the-art approaches is demonstrated using the real data collected from Gowalla with large-scale road networks.

Protecting Against Velocity-Based, Proximity-Based, and External Event Attacks in Location-Centric Social Networks

Mobile devices with positioning capabilities allow users to participate in novel and exciting location-based applications. For instance, users may track the whereabouts of their acquaintances in location-aware social networking applications (e.g., Foursquare). Furthermore, users can request information about landmarks in their proximity. Such scenarios require users to report their coordinates to other parties, which may not be fully trusted. Reporting precise locations may result in serious privacy violations, such as disclosure of lifestyle details, sexual orientation, and so forth. A typical approach to preserve location privacy is to generate a cloaking region (CR) that encloses the user position. However, if locations are continuously reported, an attacker can correlate CRs from multiple timestamps to accurately pinpoint the user position within a CR. In this work, we protect against a broad range of attacks that breach location privacy using knowledge about (1) maximum user velocity, (2) external events that may occur outside the process of self-reporting locations (e.g., social network posts tagged by peers), and (3) information about mutual proximity between users. Assume user u who reports two consecutive cloaked regions A and B . We consider two distinct protection scenarios: in the first case, the attacker does not have information about the sensitive locations on the map, and the objective is to ensure that u can reach some point in B from any point in A ; in the second case, the attacker knows the placement of sensitive locations, and the objective is to ensure that u can reach any point in B from any point in A . We propose spatial and temporal cloaking transformations to preserve user privacy, and we show experimentally that privacy can be achieved without significant quality-of-service deterioration.

A location-aware TV show recommendation with localized sementaic analysis

Nowadays, microblogging platforms such as Twitter and Weibo can also be seen as a good media to present reviews about topics. More and more people tend to share their thoughts through various microblogging sites. For example, when a TV show is being shown, the users would like to share and discuss their opinions about it on these platforms. However, one phenomenon is the popularity of the TV usually varies for different regions due to the cultural differences, custom and some other factors. Predicting whether a TV show will be popular at certain locations is then desirable. In this paper, a location-aware TV show recommendation scheme is proposed. By incorporating the social network information of users from different locations, a location-based user profile is obtained. Then, the scheme conducts prediction of TV show popularity for different regions based on the profile and similar shows. For a new TV show, the popularity of the similar shows is utilized to get the initial location-show matrix. Then, the location profiles and physical distance are used as regularizer into the collaborative filtering framework to further refine the prediction. A TV show dataset with location-aware social network information has been collected. Experiments have been conducted on real data and encouraging results have been achieved.

A User Proprietary Obfuscate System for Positions Sharing in Location-Aware Social Networks

A user’s trajectory can be maliciously monitored by adversaries when they share the positions in location-aware social networking applications which require users to update their own locations continuously. An adversary infers user’s locations from the trajectories, and gleans user’s private information through them via location-aware social networking applications and public available geographic data. In this paper, we propose a user proprietary obfuscate system to suit situations for position sharing and location privacy preserving in location-aware social network. Users transform the public available geographic data into personal obfuscate region maps with pre-defined profile to prevent the location leaking in stationary status. Our obfuscation with size restricted regions method tunes user’s transformed locations fitting into natural movement and prevents unreasonable snapshot locations been recorded in the trajectory.

Seal

Location-based services (LBS) have become more and more ubiquitous recently. Existing methods focus on finding relevant points-of-interest (POIs) based on users' locations and query keywords. Nowadays, modern LBS applications generate a new kind of spatio-textual data, regions-of-interest (ROIs), containing region-based spatial information and textual description, e.g., mobile user profiles with active regions and interest tags. To satisfy search requirements on ROIs, we study a new research problem, called spatio-textual similarity search: Given a set of ROIs and a query ROI, we find the similar ROIs by considering spatial overlap and textual similarity. Spatio-textual similarity search has many important applications, e.g., social marketing in location-aware social networks. It calls for an efficient search method to support large scales of spatio-textual data in LBS systems. To this end, we introduce a filter-and-verification framework to compute the answers. In the filter step, we generate signatures for the ROIs and the query, and utilize the signatures to generate candidates whose signatures are similar to that of the query. In the verification step, we verify the candidates and identify the final answers. To achieve high performance, we generate effective high-quality signatures, and devise efficient filtering algorithms as well as pruning techniques. Experimental results on real and synthetic datasets show that our method achieves high performance.

Supporting the 'chain of survival': how ICT can facilitate rapid response for out-of-hospital cardiac emergencies

Sudden cardiac death is a serious public health issue throughout the Western world, especially within rural communities that cannot be quickly reached by emergency ambulance services. When a person suffers a cardiac arrest, it is vital to invoke the ‘chain of survival’ process, involving early access to the emergency services hotline, early CPR, early defibrillation, and early advanced paramedic support. It is vital that this process be executed quickly and efficiently. This paper proposes ideas on how the early stages of the ‘chain of survival’ may potentially be enhanced and made more agile through the application of innovative ICT solutions designed in accordance with principles of interactive systems design and based on the new generation of mobile applications such as location-aware devices and social networking software.

An empirical investigation of attitude towards location-aware social network service

There is currently an abundance of social network services available on the internet. In addition, examples of location-aware social network services are emerging. The use of such services presents interesting consequences for users' privacy and behaviour, and ultimately the adoption of such services. Yet, not a lot of explicit knowledge is available that addresses these issues. The work presented here tries to answer this by investigating the willingness to use location-aware service in a population of students during a three-week festival. The main findings show that most users are willing to use such systems, also on a larger scale. However, some reservations, particularly with regard to privacy, are uncovered.

Identification of preconditions for an emerging mobile LBS market

Location information has become a very important horizontal service component for a wide range of vertical mobile applications. This enabled multiple innovative mobile location-based services (LBS) that range from conventional mapping and navigation services featuring millions of points of interest (POI) to location-aware content delivery and mobile social networking. Providing a whole new world of opportunities for device and application vendors, network operators, content providers and prospective consumers, mobile LBS have potential to become one of the core businesses in today's mobile arena. However, the future of this market is not fully transparent due to several uncertainties associated with a mixture of technologies, service demand, consumer attitude, privacy aspects of using LBS and a complex value network. In this study, we identify and analyse a set of preconditions for an emerging mobile LBS market in its present evolution phase. The history of LBS motivates us to take a closer look at this emerging market and try to understand what the main reasons for LBS initial failure have been and what is still missing in this market today to emerge successfully. To focus on the most relevant issues, we construct our own research framework containing three main dimensions: technological advance and standardisation, value network and value proposition. Within these dimensions, we compare the early state of mobile LBS with the present situation and analyse the key factors presently crucial for LBS proliferation.