Geosocial Networks Research Articles

User profile visualisation for privacy awareness on Geo-Social Networks

ABSTRACT Geo-Social Networks (GeoSNs) enable user interaction by sharing personal location and contextual information, leading to the accumulation of large databases of users’ physical presence and experiences in geographic places. These data form the basis of building (geo)graphic profiles for users on GeoSNs. Users’ awareness of the data they share is limited. Without active reminders of the contents of their geoprofiles, users may fail to recognise possible privacy risks associated with their location sharing behaviour. In this work, we argue that users’ awareness on GeoSNs should consider the spatial, temporal, and social dimensions of the data, as well as their combinations. We propose a basic strategy for representing and visualizing geoprofiles that aggregates the interaction of users in space over time to summarise the degree of relatedness of users to place and to other users. We conducted a user study with 26 participants to assess the perception of their geoprofiles on a typical GeoSN against the proposed approach. Results demonstrate the potential value of the proposal for improving user awareness of their data and privacy implications, compared to basic search and retrieval functions offered by typical GeoSNs. This work highlights the need for considering and improving user awareness on these platforms.

LSADEN: Local Spatial-Aware Community Detection in Evolving Geo-Social Networks

LSADEN: Local Spatial-Aware Community Detection in Evolving Geo-Social Networks

Local Overlapping Spatial-aware Community Detection

Local spatial-aware community detection refers to detecting a spatial-aware community for a given node using local information. A spatial-aware community means that nodes in the community are tightly connected in structure, and their locations are close to each other. Existing studies focus on detecting the local non-overlapping spatial-aware community, i.e., detecting a spatial-aware community containing the given node. However, many geosocial networks often contain overlapping spatial-aware communities. Therefore, we propose a local overlapping spatial-aware community detection (LOSCD) problem, which aims to detect all spatial-aware communities that contain a given node with local information. To address LOSCD problem, we design an algorithm based on Spatial Modularity and Edge Similarity, called SMES. SMES contains two processes: spatial expansion and structure detection. The spatial expansion process involves using spatial modularity to identify nodes that are spatially close, while the structural detection process employs edge similarity to identify nodes that are structurally close. Experimental results demonstrate that SMES outperforms comparison algorithms in terms of both structural and spatial cohesiveness.

GeSoN: A Geo-Social Network model applying bounded rationality to farmers in socio-ecological simulations

GeSoN: A Geo-Social Network model applying bounded rationality to farmers in socio-ecological simulations

Spatial-Aware Local Community Detection Guided by Dominance Relation

The problem of finding the spatial-aware community for a given node has been defined and investigated in geosocial networks. However, existing studies suffer from two limitations: 1) the criteria of defining communities are determined by parameters, which are difficult to set, and 2) algorithms may require global information and are not suitable for situations where the network is incomplete. Therefore, we propose spatial-aware local community detection (SLCD), which finds the spatial-aware local community with only local information and defines the community based on the difference in terms of the sparseness of edges inside and outside the community. Specifically, to address the SLCD problem, we design a novel spatial aware local community detection algorithm based on dominance relation, but this algorithm incurs high cost. To further improve the efficiency, we propose a greedy algorithm. Experimental results demonstrate that the proposed greedy algorithm outperforms the comparison algorithms.

Agent-based simulation of COVID-19 containment measures: the case of lockdowns in cities.

The online version contains supplementary material available at 10.1007/s12076-023-00336-w.

Risk and Protective Profile of Men Who Have Sex With Men Using Mobile Voluntary HIV Counseling and Testing: Latent Class Analysis.

Mobile voluntary counseling and testing (VCT) for HIV has been carried out to improve the targeting of at-risk populations and HIV case detection for men who have sex with men (MSM). However, the HIV-positive detection rate using this screening strategy has declined in recent years. This may imply unknown changes in risk-taking and protective features jointly influencing the testing results. These changing patterns in this key population remain unexplored. The aim of this study was to identify the nuanced group classification of MSM who underwent mobile VCT using latent class analysis (LCA), and to compare the difference in characteristics and testing results between subgroups. A cross-sectional research design and purposive sampling were applied between May 21, 2019, and December 31, 2019. Participants were recruited by a well-trained research assistant through social networking platforms, including the most popular instant messenger app Line, geosocial network apps dedicated to MSM, and online communities. Mobile VCT was provided to participants at an assigned time and place. Demographic characteristics and risk-taking and protective features of the MSM were collected via online questionnaires. LCA was used to identify discrete subgroups based on four risk-taking indicators-multiple sexual partners (MSP), unprotected anal intercourse (UAI), recreational drug use within the past 3 months, and history of sexually transmitted diseases-and three protective indicators-experience of postexposure prophylaxis, preexposure prophylaxis use, and regular HIV testing. Overall, 1018 participants (mean age 30.17, SD 7.29 years) were included. A three-class model provided the best fit. Classes 1, 2, and 3 corresponded to the highest risk (n=175, 17.19%), highest protection (n=121, 11.89%), and low risk and low protection (n=722, 70.92%), respectively. Compared to those of class 3, class 1 participants were more likely to have MSP and UAI within the past 3 months, to be ≥40 years of age (odds ratio [OR] 2.197, 95% CI 1.357-3.558; P=.001), to have HIV-positive results (OR 6.47, 95% CI 2.272-18.482; P<.001), and a CD4 count ≤349/μL (OR 17.50, 95% CI 1.223-250.357; P=.04). Class 2 participants were more likely to adopt biomedical preventions and have marital experience (OR 2.55, 95% CI 1.033-6.277; P=.04). LCA helped derive a classification of risk-taking and protection subgroups among MSM who underwent mobile VCT. These results may inform policies for simplifying the prescreening assessment and more precisely recognizing those who have higher probabilities of risk-taking features but remain undiagnosed targets, including MSM engaging in MSP and UAI within the past 3 months and those ≥40 years old. These results could be applied to tailor HIV prevention and testing programs.

Mining Top-k Frequent Patterns in Large Geosocial Networks: A Mnie-Based Extension Approach

Frequent pattern mining (FPM) has played an important role in many graph domains, such as bioinformatics and social networks. In this paper, we focus on geo-social graphs, a kind of social network augmented by geographical information. However, in addition to the exponential time complexity of the problem, we face the challenge of efficient subgraph retrieval since we are interested in patterns in a specific region in such a network. For this reason, we formulate the top-k FPM problem in large geo-social networks. Specifically, we devise a novel framework for subgraph retrieval and FPM mining with a series of optimizations. First, we propose a neighboring-aware R-tree (NaR-Tree) index structure to alleviate the challenge of retrieving subgraphs from a large graph. NaR-Tree is a variant of R-tree in which each nonleaf tree node further maintains some edge statistics information for the rectangle related to it. Second, we define the concept of minimum image-based support of edges (MNIE). With the help of the NaR-Tree and MNIE-based pattern extension approach, a mining algorithm that addresses the problem of exponential candidate patterns is proposed. We also present a lazy retrieval strategy to reduce the frequency of subgraph retrieval. Finally, we adopt an edge sampling approach to further accelerate the mining process. Extensive experiments on real-world and synthesized datasets are conducted to demonstrate the effectiveness and efficiency of our solution.

Maximizing the influence of bichromatic reverse k nearest neighbors in geo-social networks

Geo-social networks offer opportunities for the marketing and promotion of geo-located services. In this setting, we explore a new problem, called Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors (MaxInfBRkNN). The objective is to find a set of points of interest (POIs), which are geo-textually and socially relevant to social influencers who are expected to largely promote the POIs online. In other words, the problem aims to detect an optimal set of POIs with the largest word-of-mouth (WOM) marketing potential. This functionality is useful in various real-life applications, including social advertising, location-based viral marketing, and personalized POI recommendation. However, solving MaxInfBRkNN with theoretical guarantees is challenging because of the prohibitive overheads on BRkNN retrieval in geo-social networks, and the NP and #P-hardness of finding the optimal POI set. To achieve practical solutions, we present a framework with carefully designed indexes, efficient batch BRkNN processing algorithms, and alternative POI selection policies that support both approximate and heuristic solutions. Extensive experiments on real and synthetic datasets demonstrate the good performance of our proposed methods.

Organizational Geosocial Network: A Graph Machine Learning Approach Integrating Geographic and Public Policy Information for Studying the Development of Social Organizations in China

This study aims to give an insight into the development trends and patterns of social organizations (SOs) in China from the perspective of network science integrating geography and public policy information embedded in the network structure. Firstly, we constructed a first-of-its-kind database which encompasses almost all social organizations established in China throughout the past decade. Secondly, we proposed four basic structures to represent the homogeneous and heterogeneous networks between social organizations and related social entities, such as government administrations and community members. Then, we pioneered the application of graph models to the field of organizations and embedded the Organizational Geosocial Network (OGN) into a low-dimensional representation of the social entities and relations while preserving their semantic meaning. Finally, we applied advanced graph deep learning methods, such as graph attention networks (GAT) and graph convolutional networks (GCN), to perform exploratory classification tasks by training models with county-level OGNs dataset and make predictions of which geographic region the county-level OGN belongs to. The experiment proves that different regions possess a variety of development patterns and economic structures where local social organizations are embedded, thus forming differential OGN structures, which can be sensed by graph machine learning algorithms and make relatively accurate predictions. To the best of our knowledge, this is the first application of graph deep learning to the construction and representation learning of geosocial network models of social organizations, which has certain reference significance for research in related fields.

IM2Vec: Representation learning-based preference maximization in geo-social networks

Recent advancements in mobile technology have facilitated location-based social networks. The location-based influence maximization problem, which aims to find top influential seed users for promoting a target location to attract the most individuals, has drawn increasing attention. However, the existing studies largely neglect the importance of user preference, which considerably hinders their practicability. In addition, time efficiency is a critical issue for handling large-scale datasets. To address the above problems, we propose a new framework named IM2Vec, which incorporates representation learning into location-based influence maximization problem. Specifically, we first propose a representation learning model, All2Vec, to capture user preferences for the target location from check-in records, which takes both user preference and geographical location influence into consideration. Then, based on the learned user preferences, we extend the reverse influence sampling (RIS) model and propose a highly efficient preference maximization algorithm, which ensures a (1-1/e-∊)-approximate solution with a substantially lower sample size. The experimental results of the two tasks (future visitor prediction and influence maximization) on two real geo-social networks show that the All2Vec model achieves considerably higher accuracy in future visitor prediction, and IM2Vec exhibits a higher influence spread and a lower running time than the state-of-the-art baselines.

Group homophily based facility location selection in geo-social networks

Group homophily based facility location selection in geo-social networks

Nearest close friend query in road-social networks

Nearest close friend query (k?NCF) in geo-social networks, aims to find the k nearest user objects from among the ?-hop friends of the query user. Existing efforts on k?-NCF find the user objects in the Euclidean space. In this paper, we study the problem of nearest close friend query in road-social networks.We propose two methods. One is based on Dijkstra algorithm, and the other is based on ISLabel. For the Dijkstra-based method, Dijkstra algorithm is used to traverse the user objects needed. For the label-based method, we make use of IS-Label to calculate the distance between two vertices to avoid traversing the edges that do not contain the desired user object. For each method, we propose effective termination conditon to terminate the query process early. Finally, we conduct a variety of experiments on real and synthetic datasets to verify the efficiency of the proposed methods.

Extended Jaccard Indexive Buffalo Optimized Clustering on Geo-social Networks with Big Data

Clustering is a general task of data mining where partitioning a large dataset into dissimilar groups is done. The enormous growth of Geo-Social Networks (GeoSNs) includes users, who create millions of heterogeneous data with a variety of information. Analyzing such volume of data is a challenging task. The clustering of large volume of data is used to identify the frequently visited location information of the users in Geo-Social Networks. In order to improve the clustering of a large volume of data, a novel technique called Extended Jaccard Indexive Buffalo Optimized Data Clustering (EJIBODC) is introduced for grouping the data with high accuracy and less time consumption. The main aim of EJIBODC technique is to partition the big dataset into different groups. In this technique, many clusters with centroids are initialized to group the data. After that, Extended Jaccard Indexive Buffalo Optimization technique is applied to find the fittest cluster for grouping the data. The Extended Jaccard Index is applied in the Buffalo Optimization to measure the fitness between the data and the centroid. Based on the similarity value, using a gradient ascent function, the data finds the fittest cluster centroid for grouping. After that, the fitness value of cluster is updated and all the data gets grouped into a suitable cluster with high accuracy and minimum error rate. An experimental procedure is involved with big geo-social dataset and testing of different clustering algorithms. The series discussion is carried out on factors such as clustering accuracy, error rate, clustering time and space complexity with respect to a number of data. Experimental outcomes demonstrate that the proposed EJIBODC technique achieves improved performance in terms of higher clustering accuracy, less error rate, time consumption and space complexity when compared to previous related clustering techniques.

Efficient Sample Location Selection for Query Zone in Geo-Social Networks

While promoting a business or activity in geo-social networks, the geographical distance between its location and users is critical. Therefore, the problem of Distance-Aware Influence Maximization (DAIM) has been investigated recently. The efficiency of DAIM heavily relies on the sample location selection. Specifically, the online seeding performance is sensitive to the distance between the promoted location and its nearest sample location, and the offline precomputation performance is sensitive to the number of sample locations. However, there is no work to fully study the problem of sample location selection for DAIM in geo-social networks. To do this, we first formalize the problem under a reasonable assumption that a promoted location always adheres to the distribution of users (query zone). Then, we propose two efficient location sampling approaches based on facility location analysis, which is one of the most well-studied areas of operations research, and these two approaches are denoted by Facility Location based Sampling (FLS) and Conditional Facility Location Based Sampling (CFLS), respectively. FLS conducts one-time sample location selection, and CFLS extends the one-time sample location selection to a continuous process, so that an online advertising service can be started immediately without sampling a lot of locations. Our experimental results on two real datasets demonstrate the effectiveness and efficiency of the proposed methods. Specifically, both FLS and CFLS can achieve better performance than the existing sampling methods for the DAIM problem, and CFLS can initialize the online advertising service in a matter of seconds and achieve better objective distance than FLS after sampling a large number of sample locations.

Social-Spatial Group Queries with Keywords

Social networks with location enabling technologies, also known as geo-social networks, allow users to share their location-specific activities and preferences through check-ins. A user in such a geo-social network can be attributed to an associated location (spatial), her preferences as keywords (textual), and the connectivity (social) with her friends. The fusion of social, spatial, and textual data of a large number of users in these networks provide an interesting insight for finding meaningful geo-social groups of users supporting many real-life applications, including activity planning and recommendation systems. In this article, we introduce a novel query, namely, Top- k Flexible Socio-Spatial Keyword-aware Group Query (SSKGQ), which finds the best k groups of varying sizes around different points of interest (POIs), where the groups are ranked based on the social and textual cohesiveness among members and spatial closeness with the corresponding POI and the number of members in the group. We develop an efficient approach to solve the SSKGQ problem based on our theoretical upper bounds on distance, social connectivity, and textual similarity. We prove that the SSKGQ problem is NP-Hard and provide an approximate solution based on our derived relaxed bounds, which run much faster than the exact approach by sacrificing the group quality slightly. Our extensive experiments on real data sets show the effectiveness of our approaches in different real-life settings.

Task Execution Quality Maximization for Mobile Crowdsourcing in Geo-Social Networks

With the rapid development of smart devices and high-quality wireless technologies, mobile crowdsourcing (MCS) has been drawing increasing attention with its great potential in collaboratively completing complicated tasks on a large scale. A key issue toward successful MCS is participant recruitment, where a MCS platform directly recruits suitable crowd participants to execute outsourced tasks by physically traveling to specified locations. Recently, a novel recruitment strategy, namely Word-of-Mouth(WoM)-based MCS, has emerged to effectively improve recruitment effectiveness, by fully exploring users' mobility traces and social relationships on geo-social networks. Against this background, we study in this paper a novel problem, namely Expected Task Execution Quality Maximization (ETEQM) for MCS in geo-social networks, which strives to search a subset of seed users to maximize the expected task execution quality of all recruited participants, under a given incentive budget. To characterize the MCS task propagation process over geo-social networks, we first adopt a propagation tree structure to model the autonomous recruitment between the referrers and the referrals. Based on the model, we then formalize the task execution quality and devise a novel incentive mechanism by harnessing the business strategy of multi-level marketing. We formulate our ETEQM problem as a combinatorial optimization problem, and analyze its NP hardness and high-dimensional characteristics. Based on a cooperative co-evolution framework, we proposed a divide-and-conquer problem-solving approach named ETEQM-CC. We conduct extensive simulation experiments and a case study, verifying the effectiveness of our proposed approach.

Hierarchical temporal–spatial preference modeling for user consumption location prediction in Geo-Social Networks

Predicting where people will consume in the future is of great significance for promoting local business. Although the prevalence of Geo-Social Networks (GSNs) has provided sufficient and desirable geo-tagged data for user mobility modeling, most studies attempt to directly fit user’s preference toward locations through exploring the complex interaction between user,location pairs, which is usually hard to incorporate temporal–spatial context and side information. Moreover, the availability of multi-modal data associated with both user and location in GSNs has not yet been comprehensively leveraged. In view of the above-mentioned situations, in this article, we propose a two-stage framework composed of a Temporal Base Model (TBM) and a Location Prediction Model (LPM) to accomplish the task of user consumption location prediction at a given time in the future. In the first stage, based on user sentimental textual reviews, we leverage the hierarchical attention mechanism to capture time-sensitive user latent preference. In the second stage, we fuse the multifaceted context to derive the user’s consumption probability toward different locations at the given time. We conduct extensive experiments over three real-world GSN datasets to verify the performance of the proposed approach. The experimental results encouragingly demonstrate the effectiveness of the two-stage framework, which outperforms multiple baselines in terms of different evaluation metrics such as accuracy, average percentile rank (APR) and coverage ratio.

On Inference of Sense of Place from Geo-Social Networks

The aim of the study is to investigate whether individuals report the places they are attached to in location-based services, and whether there is a relationship between the attachment scores of these places and their corresponding check-in frequency information. A survey is conducted to measure the degree of place attachment of individuals based on self-reported locations. Then their Foursquare log data is collected which includes their check-in and venue information. Our results show that the majority of the participants check in to locations that they are attached to. Attachment score is shown to be related to the check-in frequency. The tips left for venues include terms and phrases, suggesting place attachment.

Geo-Team Formation Model for Impromptu Activities

This research provides a new model for team formation problems for Impromptu task activities in geo-social networks, called Geo-team formation. The team formation problem (TFP) is the process of dedicating the users from Social Networks to activities as teams in a collaborative functioning environment for an effective outcome based on their skills. It was proven to be an NP-hard problem. Impromptu activities deployment requires users with required skills who are socially close to each other and spatially close to the activity location. Most existing researches tackle the geo-team formation problem as a single social constraint or skills constraints query while optimizing the spatial closeness. To cover this gap, we present a model that efficiently narrows down the search space and then applies the required constraint. Efficient processing of the geo-team formation model is challenging considering (1) required skills of users, (2) weight of user skills, (3) the maximum contribution of users skills, (4) social cohesiveness between users, and (5) spatial closeness, which needs to be carefully examined and made timely invitations. The weight of user's skills helps the model dedicate users with high expertise for each team's required activities. In this approach, the time and the search cost of the team formation process are efficiently restricted. Our preliminary results on real-world datasets determine that the proposed algorithms can efficiently apply the geo-team formation model under various parameter settings. the results confirm the effectiveness of the proposed method.