Trust-aware location recommendation in location-based social networks: A graph-based approach

With the rapidly growing location-based social networks (LBSNs), personalized geo-social recommendation becomes an important feature for LBSNs. Personalized geo-social recommendation not only helps users explore new places but also makes LBSNs more prevalent to users. In LBSNs, aside from user preference and social influence, geographical influence has also been intensively exploited in the process of location recommendation based on the fact that geographical proximity significantly affects users' check-in behaviors. Although geographical influence on users should be personalized, current studies only model the geographical influence on all users' check-in behaviors in a universal way. In this paper, we propose a new framework called iGSLR to exploit personalized social and geographical influence on location recommendation. iGSLR uses a kernel density estimation approach to personalize the geographical influence on users' check-in behaviors as individual distributions rather than a universal distribution for all users. Furthermore, user preference, social influence, and personalized geographical influence are integrated into a unified geo-social recommendation framework. We conduct a comprehensive performance evaluation for iGSLR using two large-scale real data sets collected from Foursquare and Gowalla which are two of the most popular LBSNs. Experimental results show that iGSLR provides significantly superior location recommendation compared to other state-of-the-art geo-social recommendation techniques.

Friend recommendation is an important real-world application in Location-based Social Networks (LBSN), helping users discover potential friends and enhance their overall happiness. LBSN mainly comprises two distinct data structures: spatio-temporal data for human mobility and graph data for social networks. These two data structures make it challenging to model the complex relationships between them, which are essential for comprehensively understanding users’ lives. Previous studies have either modeled user trajectories and social networks separately or used classical simple graph-based methods, where a simple edge links only two nodes, failing to capture the multiple relationships inherent in LBSN. Furthermore, most studies have relied on Euclidean space to train their graph models, which could result in significant distortion because of tree-like social network data structure. To address these limitations, we propose a novel heterogeneous LBSN hypergraph that represents user check-in records and continuous trajectories—comprising multiple Points of Interest (POI)—as hyperedges, enabling the representation of complex spatio-temporal relationships. This approach enables us to link multiple nodes of different types by hyperedges and use hyperbolic spaces to create more efficient graph representations. Additionally, we devise a new type-specific attention mechanism for our Heterogeneous Hyperbolic Hypergraph Neural Network (H 3 GNN), which is end-to-end trainable and employs supervised contrastive learning to learn hypergraph node embeddings for the subsequent friend recommendation task with the help of hyperbolic space. Finally, our model H 3 GNN achieves better results than existing methods on six real-world city datasets, and our ablation studies demonstrate the effectiveness of each component. Additionally, our experiments indicate that H 3 GNN requires less data storage and training time compared to previous methods.

Several online social networks collect information from their users' interactions (co-tagging of photos, co-rating of products, etc.) producing a large amount of activity-based data. As a consequence, this kind of information is used by these social networks to provide their users with recommendations about new products or friends. Moreover, recommendation systems (RS) become capable to predict a person's activity with no special infrastructure or hardware, such as RFID tags, or by using video and audio. In that sense, we propose a technique to provide personalised points-of-interest (POIs) recommendations for users of location-based social networks (LBSNs). Our technique assumes users' preferences can be characterised by their visited locations, which is shared by them on LBSN, collaboratively exposing important features as, for instance, areas-of-interest (AOIs) and POIs popularity. Therefore, our technique, named fuzzy areas-based collaborative filtering, uses users' activities to model their preferences and recommend the next visits to them. We have performed experiments over two real LBSN datasets and the obtained results have shown our technique outperforms location collaborative filtering at almost all of the experimental evaluation. Therefore, by fuzzy clustering of AOIs, FACF is suitable to check the popularity of POIs to improve POIs recommendation.

Several online social networks collect information from their users' interactions (co-tagging of photos, co-rating of products, etc.) producing a large amount of activity-based data. As a consequence, this kind of information is used by these social networks to provide their users with recommendations about new products or friends. Moreover, recommendation systems (RS) become capable to predict a person's activity with no special infrastructure or hardware, such as RFID tags, or by using video and audio. In that sense, we propose a technique to provide personalised points-of-interest (POIs) recommendations for users of location-based social networks (LBSNs). Our technique assumes users' preferences can be characterised by their visited locations, which is shared by them on LBSN, collaboratively exposing important features as, for instance, areas-of-interest (AOIs) and POIs popularity. Therefore, our technique, named fuzzy areas-based collaborative filtering, uses users' activities to model their preferences and recommend the next visits to them. We have performed experiments over two real LBSN datasets and the obtained results have shown our technique outperforms location collaborative filtering at almost all of the experimental evaluation. Therefore, by fuzzy clustering of AOIs, FACF is suitable to check the popularity of POIs to improve POIs recommendation.

With the proliferation of social network services (e.g., Facebook, Twitter, and Instagram), identifying cohesive subgraphs in networks is a key and fundamental problem to understand users' activity. Recently this problem has received extensive research attention. Identifying cohesive subgraph structures in networks has many applications such as recommendation, detecting fraudsters, event organization, and graph analysis. In this thesis, we study three important problems of finding cohesive subgraphs. Our studies include finding a query-based cohesive subgraph search in social networks and location-based social networks, and detecting all cohesive subgraphs in attributed bipartite networks. In our study of Density Modularity based Community Search (DMCS), we propose the modularity-based community search problem in networks. Modularity is a widely used measure of the structure of networks which checks the strength of partition of a network into communities. Most of the existing community search models only focus on the internal cohesiveness of a community. However, a high-quality community often has high modularity, which implies dense connections inside communities and sparse connections to the nodes outside the community. Hence, we conduct a pioneer study on searching for a community with high modularity. We point out that while modularity has been used in community detection (unrelated to query nodes), its application in community search (related to query nodes) brings in different challenges named free-rider effect and resolution limit problems. Both problems indicate that intrinsically optimizing the modularity can fail to find a small-sized community as a result for the community search problem. We mitigate these problems by designing a new graph modularity function named Density Modularity. To the best of our knowledge, this is the first work on the community search problem by using the graph modularity. The community search based on the density modularity, termed as DMCS, is to find a community in a social network that contains all the query nodes and has high density modularity. We prove that the DMCS problem is NP-hard, and thus there is no exact algorithm that is scalable to large graphs. To efficiently address DMCS, we present new algorithms that run in log-linear time to the graph size. We conduct extensive experimental studies in real-world and synthetic networks, which offer insights into the efficiency and effectiveness of our approximate algorithms. In our study of GeoSocial Community Search(GCS) problem, we propose the community search problem in location-based social networks. Specifically, we aim at finding a social community and a location cluster that are densely connected in a location-based social network. GCS can be useful for finding marketing targets and user/location recommendations. To the best of our knowledge, this is the first work to find a social community and a location cluster that are densely connected from location-based social networks. We prove that the GCS problem is NP-hard and is not in APX, unless P = NP. To solve GCS problem, we propose three effective and efficient algorithms: Core-based Basic Algorithm, Top-down Greedy Removing Algorithm, and Bottom-up Greedy Expansion Algorithms. Finally, we report extensive experimental studies that offer insights into the efficiency and effectiveness of the proposed solutions. In our study of Attributed Bipartite Co-clustering (ABC), we propose the co-clustering problem in attributed bipartite networks. Co-clustering in bipartite networks is a fundamental and important problem. Specifically, we unify two main concepts: (i) bipartite modularity optimization, and (ii) attribute cohesiveness. To the best of our knowledge, this is the first work to find a set of co-clusters while considering the attribute cohesiveness. We prove that ABC problem is NP-hard and propose three effective and efficient algorithms: (1) Top-Down Algorithm; (2) Bottom-Up Algorithm; and (3) Group-based Matching Algorithm. Finally, extensive experimental results on real-world attributed bipartite networks demonstrate the efficiency and effectiveness of our algorithms.

These proceedings contain the papers selected for presentation at the seventh edition of the ACM SIGSPATIAL Workshop on Location-Based Social Networks (LBSN 2014) which is being held in conjunction with the ACM SIGSPATIAL GIS conference in 2014. Social networks have been prevalent on the Internet and become a hot research topic attracting many professionals from a variety of fields. The advances in location-acquisition and mobile communication technologies empower people to use location data with existing online social networks. The dimension of location helps bridge the gap between the physical world and online social networking services. Furthermore, people in an existing social network can expand their social structure with the new interdependency derived from their locations. As location is one of the most important components of user context, extensive knowledge about an individual's interests, behaviors, and relationships with others can be learned from locations. These kinds of location-embedded and location-driven social structures are known as location-based social networks. The objective of this workshop is to provide professionals, researchers, and technologists with a single forum where they can discuss and share the state-of-the-art of LBSN development and applications, present their ideas and contributions, and set future directions in emerging innovative research for location based social networks. This year program is composed of two tracks of predominantly "social" and "spatial" views on LBSNs.

In the last two decades, the issue of Influence Maximization (IM) in traditional online social networks has been extensively studied since it was proposed. It is to find a seed set which has maximum influence spread under a specific network transmission model. However, in real life, the information can be spread not only through online social networks, but also between neighbors who are close to each other in the physical world. Location-Based Social Network (LBSN) is a new type of social network which is emerging increasingly nowadays. In a LBSN, users can not only make friends, but also share the events they participate in at different locations by checking in. In this paper, we aim to study the IM in LBSNs, where we consider both the influence of online and offline interactions. A two-layer network model and an information propagation model are proposed. Also, we formalize the IM problem in LBSNs and present an algorithm obtaining an approximation factor of \((1 - 1/e - \epsilon \)) in near-linear expected time. The experimental results show that the algorithm is efficient meanwhile offering strong theoretical guarantees.

This paper presents an adaptive e-learning system which supports collaborative learning based on a location-based social network and semantic modeling. In the system, a social network among e-learning learners is dynamically constructed on the basis of the location information of learners using GPS sensors for collaborative learning. In addition, user modeling for semantic social network and adaptive e-learning is supported. For these, a mechanism that supports the construction of a dynamic social network using the location information of smart phones is provided. The user modeling for the construction of a semantic social network and the support of adaptive e-learning considers the information such as the users' achievement, preference, and learning goal. Through this system, e-learners can find mentors who are able to facilitate their learning and create communities for collaborative learning offline and online. The support of a location-based social network service and semantic user model in our system would increase interactions among e-learners and improve satisfaction regarding their mobile learning environment.

Location-based social network (LBSN) is a highly used social network at present. Detecting the community structures in LBSN plays a significant role in analyzing the network comprehensively. Modularity optimization is a traditional social network community detection method which has excellent performance. Whereas, resolution limit is an inherent problem of it. Some existing community detection methods for LBSN fail to take account of the full network information. To solve above problems, a network weighting scheme is proposed in this paper. Firstly, we combine a network embedding method that named walk2friends with this paper. In view of the shortcoming of walk2friends, we proposes a collaborative filtering method based on social relationship to improve it. Then, the learned similarity is obtained by improved walk2friends and the structural similarity is obtained by a random walk method. These two similarities are fused and then transformed into edge weight. Finally, the undirected unweighted graph is preprocessed as an undirected weighted graph. Experimental results on real networks show that when using modularity optimization method for optimization the weighting scheme not only can avoid the resolution limit but also applicable to LBSN . The mined community is more cohesive than the existing work on LBSN.

The development of location-based social networking (LBSN) services is growing rapidly these days. Users of LBSN services are more interested in sharing tips and experiences of their visits to various locations. In this paper, we aim at a study of recommending locations to users on LBSNs by collaborative filtering (CF) recommenders based only on users' check-in data. We first design and develop a distributed crawler to collect a large amount of check-in data from Gowalla. Then, we use three ways to utilize the check-in data, namely, the binary utilization, the FIF utilization, and the probability utilization. According to different utilizations, we introduce different CF recommenders, namely, user-based, item-based and probabilistic latent semantic analysis (PLSA), to do the location recommendation. Finally, we conduct a set of experiments to compare the performances of different recommenders using different check-in utilizations. The experimental results show that PLSA recommender with the probability utilization outperforms other combinations of recommenders and utilizations for recommending locations to users on LBSN.

Location-based social networks (LBSNs) such as Foursquare, Brightkite, and Gowalla are a growing area where recommendation algorithms find a practical application. With an ever-increasing variety of venues to choose from deciding on a destination can be overwhelming. Recommenders aid their users in the decision-making process by providing a list of locations likely to be relevant to the user’s needs and interests. Traditional collaborative filtering algorithms consider relationships between users and locations, finding users to be similar only if their location histories overlap. However, the availability of spatial, temporal, and social information in an LBSN offers an opportunity to improve the quality of a recommendation engine. Social network data allows us to connect users who can directly influence each other’s decisions. Temporal data allows us to account for the drifting preferences of users, giving more weight to recent location visits over historical selections, and taking advantages of repetitive behaviors. Spatial information allows us to focus recommendations on locations close to the user, keeping our recommendations relevant as a user travels. We introduce a novel approach to incorporate spatial, temporal, and social context into a traditional collaborative filtering algorithm. We evaluate our method on data sets collected from three LBSNs, and demonstrate that our approach is at the least competitive with existing state-of-the-art location recommenders.

Location-Based Social Networks (LBSNs), such as Foursquare, Yelp and Facebook Place, have attracted many people, including business owners who use LBSNs to promote their businesses. A physical location is called a venue or a place of interest in an LBSN. Associated with each venue are several attributes, such as its latitude and longitude values, and the discounts/coupons users can use. LBSN users usually tend to trust venues, which is becoming a key focus of various attacks [7, 10]. By manipulating attributes related to venues, however, an attacker can deceive users, compromise their privacy and destroy the reputation of the venues in an LBSN. In this paper, we call the attacks targeting venues as venue attacks. We first investigate and characterize such attacks in Foursquare, Yelp and Facebook Place. We then study what makes such attacks successful and discuss potential defense approaches against these attacks. To the best of our knowledge, we are the first to characterize various venue attacks in LBSNs.

One of the most attractive problems of social network analysis is the link prediction. Social networks' user growth is mostly supported with data driven friend recommendations which are provided by link predictors. Previously, we had studied new features to improve prediction accuracy in Location Based Social Networks (LBSNs) where users share temporal location information with check-in interactions. In this paper, we focused on the efficiency of link predictors as the speed of prediction is as critical as its accuracy in LBSNs. Extraction time costs and prediction accuracy of individual LBSN features are mined to pick a feature subset that is achieving faster link prediction while not losing from accuracy.

With advanced development of Internet communication and ubiquitous computing, Social Networks are providing an important information channel for smart city construction. Therefore, analyzing Location-based Social Network is a very valuable work in achieving reasonable urban zoning. In Social Networks, a main purpose of prestige assessment is to extract influential users who are regarded as the key nodes for community detection from Onine Social Networks (OSNs). However, social relationships of users are rarely used to evaluate the popularity of physical locations and zone physical locations. In order to achieve urban area function zoning by evaluating the prestige of geographic regions based on user relationships in Location based Social Networks (LBSNs), this paper proposes a Prestige Density-Based Spatial Clustering of Applications with Noise algorithm (P-DBSCAN) by improving the existing DBSCAN algorithm. Specifically, the algorithm first calculates the centrality of users in the social network, and then converts the centrality of users into the location-centrality through the users’ check-in data. After the centrality of each location is obtained, the discrete locations are clustered according to four constraints of the given radius. After clustering, the result of urban area function zoning can be achieved. Extensive experiments are conducted for demonstrating the effectiveness of our proposed algorithm in this paper. In addition, the visualization results reveal the correctness of our proposed approach.

The increasing adoption of social network applications has been an important source of information nowadays. The analysis of human behaviors in social networks has been brought to the forefront of several studies. Location-Based Social Networks (LBSN) are one of the possible means that allow the prediction of human behaviors through the efficient analysis of user’s mobility patterns. Despite the remarkable progress in this research direction, however, LBSN is still hindered by the lack of literature defining the semantic aspects of the user’s mobility. This research presents a contribution to the latest knowledge representation languages and Semantic Web technologies. We focus on studying human behavior mobility which is the core in location recommendation systems. Bringing to the ridesharing context, an ontology model with its underlying description logics to efficiently annotate human mobility is presented. Finally, experimental results, performed on two location-based social networks, namely, Brightkite (https://snap.stanford.edu/data), and BlaBlaCar (https://www.blablacar.co.uk/) are presented.