Geosocial Networks Research Articles

Influence Spread in Geo-Social Networks: A Multiobjective Optimization Perspective.

As an emerging social dynamic system, geo-social network can be used to facilitate viral marketing through the wide spread of targeted advertising. However, unlike traditional influence spread problem, the heterogeneous spatial distribution has to incorporated into geo-social network environment. Moreover, from the perspective of business managers, it is indispensable to balance the tradeoff between the objective of influence spread maximization and objective of promotion cost minimization. Therefore, these two goals need to be seamlessly combined and optimized jointly. In this paper, considering the requirements of real-world applications, we develop a multiobjective optimization-based influence spread framework for geo-social networks, revealing the full view of Pareto-optimal solutions for decision makers. Based on the reverse influence sampling (RIS) model, we propose a similarity matching-based RIS sampling method to accommodate diverse users, and then transform our original problem into a weighted coverage problem. Subsequently, to solve this problem, we propose a greedy-based incrementally approximation approach and heuristic-based particle swarm optimization approach. Extensive experiments on two real-world geo-social networks clearly validate the effectiveness and efficiency of our proposed approaches.

Enabling Probabilistic Differential Privacy Protection for Location Recommendations

The sequential pattern in the human movement is one of the most important aspects for location recommendations in geosocial networks. Existing location recommenders have to access users’ raw check-in data to mine their sequential patterns that raises serious location privacy breaches. In this paper, we propose a new P rivacy-preserving LO cation RE commendation framework (PLORE) to address this privacy challenge. First, we employ the $n$ n th-order additive Markov chain to exploit users’ sequential patterns for location recommendations. Further, we contrive the probabilistic differential privacy mechanism to reach a good trade-off between high recommendation accuracy and strict location privacy protection. Finally, we conduct extensive experiments to evaluate the performance of PLORE using three large-scale real-world data sets. Extensive experimental results show that PLORE provides efficient and highly accurate location recommendations, and guarantees strict privacy protection for user check-in data in geosocial networks.

Business location planning based on a novel geo-social influence diffusion model

In modern smart cities, an increasing number of businesses rely on social network marketing to capture potential customers and third-party delivery systems to serve them; this system is called “online to offline”. Consequently, the well-known business location planning problem, which is used to attract nearby offline users, must be redefined. Thus, we propose a novel influence diffusion model to simulate the spread of advertisements for a given location within the geo-social networks; this model considers the distance from the social network users to the location and other existing competitors. We present an approximation algorithm to evaluate the maximum influence that can be achieved for a location based on the diffusion model. Moreover, to efficiently select the optimal location (with the largest maximum influence spread) from multiple candidates, a clustering-based pruning strategy is proposed. Our experimental results demonstrated the effectiveness and efficiency of our approach.

Efficient Similarity-Aware Influence Maximization in Geo-Social Network

With the explosion of GPS-enabled smartphones and social media platforms, geo-social networks are increasing as tools for businesses to promote their products or services. Influence maximization, which aims to maximize the expected spread of influence in the networks, has drawn increasing attention. However, most recent work tries to study influence maximization by only considering geographic distance, while ignoring the influence of users’ spatio-temporal behavior on information propagation or location promotion, which can often lead to poor results. To relieve this problem, we propose a Similarity-aware Influence Maximization (SIM) model to efficiently maximize the influence spread by taking the effect of users’ spatio-temporal behavior into account, which is more reasonable to describe the real information propagation. We first calculate the similarity between users according to their historical check-ins, and then we propose a Propagation to Consumption (PTC) model to capture both online and offline behaviors of users. Finally, we propose two greedy algorithms to efficiently maximize the influence spread. The extensive experiments over real datasets demonstrate the efficiency and effectiveness of the proposed algorithms.

Estructura espacial de la movilidad residencial en la Región Metropolitana de Santiago de Chile. 2012-2017

Most empirical research on spatial patterns of intra-urban residential mobility has been based on the study of the inflow and outflow recorded in various spatial units. This approach does not allow for the definition of migratory areas by the origin, destination and intensity of residential mobility. Here the spatial types and structure of residential mobility in the communes of the Santiago Metropolitan Region (RMS) are studied using individual data on changes of residence between communes. Combining the application of spatial analysis methods and the analysis of geosocial networks has demonstrated: a) the complex and fairly well-integrated nature of the network of residential movements; b) the major influence of geographical proximity on changes of residence; and c) notable territorial differences in the effects of changes of residence on socio-spatial diversification. As a whole, the functional and spatial structure of residential mobility is consistent with the processes of expansion and physical, social and functional fragmentation of the RMS.

Efficient Radius-Bounded Community Search in Geo-Social Networks

Driven by real-life applications in geo-social networks, we study the problem of computing radius-bounded <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -cores (RB- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -cores) that aims to find communities satisfying both social and spatial constraints. In particular, the model <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -core (i.e., the subgraph where each vertex has at least <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> neighbors) is used to ensure the social cohesiveness, and a radius-bounded circle is used to restrict the locations of users in an RB- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -core. We explore several algorithmic paradigms to compute RB- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -cores, including a triple-vertex-based paradigm, a binary-vertex-based paradigm, and a paradigm utilizing the concept of rotating circles. The rotating-circle-based paradigm is further enhanced by several pruning techniques to achieve better efficiency. In addition, to find representative RB- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -cores, we study the diversified radius-bounded <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -core search problem, which finds <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> RB- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -cores to cover the most number of vertices. We first propose a baseline algorithm that identifies the distinctive RB- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -cores after finding all the RB- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -cores. Beyond this, we design algorithms that can efficiently maintain the top- <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> candidate RB- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -cores and also achieve a guaranteed approximation ratio. Experimental studies on both real and synthetic datasets demonstrate that our proposed techniques can efficiently compute (diversified) RB- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -cores. Moreover, our techniques can be used to compute the minimum-circle-bounded <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -core and significantly outperform the existing techniques.

Connecting family trees to construct a population-scale and longitudinal geo-social network for the U.S

ABSTRACT We collected 92,832 user-contributed and publicly available family trees from rootsweb.com, including 250 million individuals who were born in North America and Europe between 1630 and 1930. We cleaned and connected the family trees to create a population-scale and longitudinal family tree dataset using a workflow of data collection and cleaning, geocoding, fuzzy record linkage and a relation-based iterative search for connecting trees and deduplication of records. Given the largest connected component of nearly 40 million individuals, and a total of 80 million individuals, we generated, to date, the largest population-scale and longitudinal geo-social network over centuries. We evaluated the representativeness of the family tree dataset for historical population demography and mobility by comparing the data to the 1880 Census. Our results showed that the family trees were biased towards males, the elderly, farmers, and native-born white segments of the population. Individuals were highly mobile – in our 1880 sample of parent-child pairs where both were born in the U.S., 47% were born in different states. Our findings agreed with prior studies that people migrated from East to West in horizontal bands, and the trend was reflected in the dialects and regional structure of the U.S.

A Proactive and Practical COVID-19 Testing Strategy

To reopen the economy safely during the COVID-19 pandemic, governments need the capability to proactively identify new and often asymptomatic infections, as well as contact tracing. Policymakers and public health professionals need a sampling-testing method that can achieve broad population coverage without overwhelming medical workers. We observe that COVID-19 high-risk groups are located in the hubs and cliques of our geo-social network, formed by the close encounters of people during daily life. These individuals are the de facto “canary in a coal mine”. We propose that nations offer free and anonymous testing service to them. With open-source computer algorithms and datasets, only a small fraction of the population selected for COVID-19 testing can cover the majority of high-exposure-risk individuals. A 0.3% sampled testing for a megacity covers 3/4 of its entire population. A 3% sampled testing for a rural town covers 3/4 of its entire population. With government oversight and public consent, this approach can serve each province/state or city/township for decentralized daily testing planning. However, to protect privacy, we recommend constructing the geo-social network of anonymized cellphones, not named individuals. This infrastructure should be dismantled once the pandemic is largely over. This can be achieved by policymakers, health workers, and engineers together in solidarity.

A hybrid ridesharing algorithm based on GIS and ant colony optimization through geosocial networks

The increasing use of private cars in large cities is accompanied by adverse ramifications such as severe shortage of parking spaces, traffic congestion, air pollution, a high level of fuel consumption, and travel cost. Ridesharing is one of the emerging solutions that facilitate the simultaneous match of drivers and passengers with similar travel schedules. In this paper, ridesharing equals carsharing which involves a cooperative trip of at least two passengers who share an automobile and must match their itineraries. The main objective of this paper is to develop a ridesharing system based on the geosocial network to be employed in Tehran, capital of Iran. In this regard, a new hybrid approach based on GIS and ant colony is developed to provide optimal shared-routes through integrating three main procedures sequentially. First, the spatio-temporal clustering of passengers is carried out using the K-means algorithm, second spatio-temporal matching of passengers ‘clusters, and drivers’ has been carried out by combining Voronoi continuous range query (VCRQ), a region connected calculus (RCC5) and Allen’s temporal interval algebra. Third, the optimum shared-route is found by the ant colony optimization (ACO) algorithm. The proposed hybrid model integrates metric and topological GIS-based methods with a metaheuristic algorithm. It is implemented via a bot “@Hamsafar” within the platform of a robot Telegram messenger. The proposed ridesharing application is applied with 220 passengers and 70 drivers with 61 shared trips in District # 6 of Tehran, Iran. The system are evaluated based on the statistical results, usability questionnaire, time performance, and comparison to some other metaheuristic approaches which in turn demonstrate the efficiency of the proposed algorithm.

Geosocial Co-Clustering

As location-based services using mobile devices have become globally popular these days, social network analysis (especially, community detection) increasingly benefits from combining social relationships with geographic preferences. In this regard, this article addresses the emerging problem of geosocial community detection. We first formalize the problem of geosocial co-clustering , which co-clusters the users in social networks and the locations they visited. Geosocial co-clustering detects higher-quality communities than existing approaches by improving the mapping clusterability , whereby users in the same community tend to visit locations in the same region. While geosocial co-clustering is soundly formalized as non-negative matrix tri-factorization , conventional matrix tri-factorization algorithms suffer from a significant computational overhead when handling large-scale datasets. Thus, we also develop an efficient framework for geosocial co-clustering, called GEOsocial COarsening and DEcomposition (GEOCODE) . To achieve efficient matrix tri-factorization, GEOCODE reduces the numbers of users and locations through coarsening and then decomposes the single whole matrix tri-factorization into a set of multiple smaller sub-matrix tri-factorizations. Thorough experiments conducted using real-world geosocial networks show that GEOCODE reduces the elapsed time by 19–69 times while achieving the accuracy of up to 94.8% compared with the state-of-the-art co-clustering algorithm. Furthermore, the benefit of the mapping clusterability is clearly demonstrated through a local expert recommendation application.

The Question of Solving Municipal Economy’s Problems using Electroniс Service’s Tools

The purpose of research is to examine the current state of the urban environment in Russia, to analyze the satisfaction with the quality of housing and communal services and to suggest the mechanism for the formation of a comfortable urban environment. Electronic service use-case diagram was developed. The next step is developing of the logical scheme for building an electronic service network for the population based on the technology of mobile rapid response applications. To realize access to information operated by an electronic service, the database server interacts with file storage organized using the tools for creating file-based storage systems using cloud technologies. The software implementation of the electronic service has a complex composite structure, consisting of subsystems and modules interacting with each other. The prototype of the program interface was developed as the state transition network. Research’s results: The proposed concept of mobile rapid response applications aimed at prompt notification of city services about the detected problem. This technology is an integral part of the "safe city". The definition of the responsible organization that needs to be sent to the application is carried out on the server for processing orders using a system of keywords or hash tags. Having in the mobile application a certain pre-formed, at the stage of launching an electronic service, a set of hash tags, with the possibility of its expansion and replenishment, the user can quickly and accurately describe the nature of the problem. The prototype of a system for solving urban problems was developed. Conclusions: The most promising approach is the use of distributed automated information centers, functioning according to territorial principles. In this case, it is proposed to process the geoinformation transmitted to the situational center (data from citizens and organizations appeals containing information about the location of the treatment object) in automatic mode, the person is included in the process only in case of emergency situations or when malfunctions occur. Scientific value of the research is the approach to the development of the situation center based on geo-service. Practical value of the research is the pilot operation of a system for solving urban problems, built with the use of a geosocial network was launched Orel in 2017 and has a positive feedback.

3rd ACM SIGSPATIAL Workshop on Analytics for Local Events and News (LENS 2019)

The 3rd ACM SIGSPATIAL Workshop on Analytics for Local Events and News (LENS 2019) was held in conjunction with the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2019). The workshop is intended to bring together experts in the workshop scope to exchange ideas on opportunities, challenges and cutting-edge techniques for local events and news analytics. The workshop has attracted ten submissions, all full-length research papers. Six submissions were accepted for publications; with 60% acceptance rate. The papers are reviewed by 7 program committee members, where each paper is assigned to two reviewers. Total 8 attendants registered for the workshop. The actual number of attendants along the day fluctuated from 8 to 12, with an average of 9 attendants maintained almost all the time. The paper titled DeepSpot: Understanding Online Opinion Spam by Text Augmentation using Sentiment Encoder-Decoder Networks , by Avik Nayak, Haiquan Chen, Xiaojun Ruan, and Jinsong Ouyang was awarded the best paper award. The paper titled Scalable Community Detection over Geo-Social Network , by Xiuwen Zheng, Qiyu Liu, and Amarnath Gupta was awarded the best presentation award. The workshop has achieved its goal in bringing up together experts and conducted a set of productive discussions and talks.

A Location Privacy Attack Based on the Location Sharing Mechanism with Erroneous Distance in Geosocial Networks.

Geographical social networks (GSN) is an emerging research area. For example, Foursquare, Yelp, and WeChat are all well-known service providers in this field. These applications are also known as location-based services (LBS). Previous studies have suggested that these location-based services may expose user location information. In order to ensure the privacy of the user’s location data, the service provider may provide corresponding protection mechanisms for its applications, including spatial cloaking, fuzzy location information, etc., so that the user’s real location cannot be easily cracked. It has been shown that if the positioning data provided by the user is not accurate enough, it is still difficult for an attacker to obtain the user’s true location. Taking this factor into consideration, our attack method is divided into two stages for the entire attack process: (1) Search stage: cover the area where the targeted user is located with unit discs, and then calculate the minimum dominating set. Use the triangle positioning method to find the minimum precision disc. (2) Inference phase: Considering the existence of errors, an Error-Adjusted Space Partition Attack Algorithm (EASPAA) was proposed during the inference phase. Improved the need for accurate distance information to be able to derive the user’s true location. In this study, we focus on the Location Sharing Mechanism with Maximal Coverage Limit to implement the whole attack. Experimental results show that the proposed method still can accurately infer the user’s real location even when there is an error in the user’s location information.

Survey on user location prediction based on geo-social networking data

With the popularity of smart mobile terminals and advances in wireless communication and positioning technologies, Geo-Social Networks (GSNs), which combine location awareness and social service functions, have become increasingly prevalent. The increasing amount of user and location information in GSNs makes the information overload phenomenon more and more serious. Although massive user-generated data brings convenience to users’ social and travel activities, it also causes certain trouble for their daily life. In this context, users are expecting smarter mobile applications, so that the location information can be employed to perceive their surrounding environment intelligently and further mine their behavior patterns in GSNs, which ultimately provides personalized location-based services for users. Therefore, research on user location prediction comes into existence and has received extensive and in-depth attention from researchers. Through systematically analyzing the location data carried by user check-ins and comments, user location prediction can mine various user behavior patterns and personal preferences, thus determining the visiting location of users in the future. Research on user location prediction is still in the ascendant and it has become an important topic of common concern in both academia and industry. This survey takes Geo-social networking data as the focal point to elaborate the recent progress in user location prediction from multiple aspects such as problem categories, data sources, feature extraction, mathematical models and evaluation metrics. Besides, the difficulties to be studied and the future developmental trends of user location prediction are discussed.

Location-based Access Control in Neighborhood Geosocial Network: a Case of Emergency Assistance

To enforce safer virtual neighborhood environment, access control is essential to specify privileges or access rights to resources in neighborhood geosocial network applications. In the case of emergency circumstances, several concerns in implementing access control have been identified: (1) inefficient message sending to irrelevant recipients (2) location spoofing (3) gossip and rumors due to disclosing information to wrong audience. To address such potential risks, access control specifically tailored for neighborhood geosocial network is required. The objective of this paper is two-fold: First, to propose an access control using density-based and position-based location conditions for neighborhood geosocial network. Second, to design mySOS, a proof-of-concept prototype that incorporate the proposed access control to improve access control for emergency assistance in neighborhood geosocial network. To achieve the objectives, a combination of proposed self-organized segmentation algorithms (density-based) and resident engagement incentives (position-based) are implemented. This study contributes to a new way for access control that will determine user profile in accessing emergency assistance features in a neighborhood geosocial network application.

Cohesive Ridesharing Group Queries in Geo-Social Networks

Ridesharing has gained much attention as a solution for mitigating societal, environmental, and economic problems. For example, commuters can reduce traffic jams by sharing their rides with others. Notwithstanding many advantages, the proliferation of ridesharing also brings some crucial issues. One of them is to rideshare with strangers. It makes someone feel uncomfortable or untrustworthy. Another complication is the high-latency of ridesharing group search because users may want to receive the result of their requests in a short time. Despite continuous efforts of academia and industry, the issues still remain. In this paper, for resolving the obstacles, we define a new problem, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-cohesive</i> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-ridesharing group</i> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell m$ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-CRG</i> ) query, which retrieves a cohesive ridesharing group by considering spatial, social, and temporal information. The problem is based on the three underlying assumptions: people tend to rideshare with socially connected friends, people are willing to walk but not too much, and optimization of finding good groups is essential for both drivers and passengers. In our ridesharing framework, queries are processed by efficiently taking geo-social network data into account. For this purpose, we propose an efficient method for processing the queries using a new concept, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exact</i> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-friend set</i> , with its efficient update. Moreover, we further improve our method by utilizing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inverted timetable</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ITT</i> ), which grasps crucial time information. Specifically, we devise <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time-constrained and incremental personalized-proximity search</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TIPS</i> ). Finally, the performance of the proposed method is evaluated by extensive experiments on several data sets.

Exploratory Spatiotemporal Language Analysis of Geo-Social Network Data for Identifying Movements of Refugees

Refugee movements in recent years have caused enormous challenges for relief organizations and public authorities, but especially for refugees themselves. Organizations which have to allocate their resources to regions where large groups of arrivals are expected struggle to prepare the refugees’ admission, transfer, care and accommodation in time. Events like the refugee movement of 2015/16 in Austria and Germany in the wake of the Syrian civil war have shown that many of these issues are caused by a lack of up-to-date information about logistical requirements. We evaluate various methods to acquire this information that utilize semantic, spatial and temporal features to analyse geo-social network data. A multimodal analysis of these features leads to information about refugee movements across borders and regions. Approaches based on user trajectories and attempts to identify refugees by the language they used showed little promise, whereas using spatiotemporal aggregation and hotspot analysis of keyword-based filtered data allowed us to retrace refugees’ collective movement patterns. Using temporal bins, we were able to detect changes in these patterns caused by external factors such as border closures.

A point-of-interest suggestion algorithm in Multi-source geo-social networks

Newly emerging location-based social network (LBSN) services provide us with new platforms to share interests and individual experience based on their activity history. The problems of data sparsity and user distrust in LBSNs create a severe challenge for traditional recommender systems. Moreover, users’ behaviors in LBSNs show an obvious spatio-temporal pattern. Valuable extra information from microblog-based social networks (MBSNs) can be utilized to improve the effectiveness of POI suggestion. In this study, we propose a latent probabilistic generative model called MTAS, which can accurately capture the underlying information in users’ words extracted from both LBSNs and MBSNs by taking into consideration the decision probability, a latent variable indicating a user’s tendency to publish a review in LBSNs or MBSNs. Then, the parameters of the MTAS model can be inferred by the Gibbs sampling method in an effective manner. Based on MTAS, we design an effective framework to fulfill the top-k suggestion. Extensive experiments on two real geo-social networks show that MTAS achieves better performance than existing state-of-the-art methods.

On efficiently diversified top-k geo-social keyword query processing in road networks

With the development of geo-positioning technologies, the volumes of spatio-textual data are growing rapidly. These data are available in many applications such as location-based services and geo-social networks, and thus, various types of geo-social keyword queries have been intensively studied in the literature. A top-k geo-social keyword (TkGSK) query retrieves the objects by considering spatial, social, and textual constraints between the query and objects. Moreover, the result diversification is becoming a common practice to enhance the quality of the query result. Motivated by these, in this paper, we study the problem of diversified top-k geo-social keyword (DkGSK) query, which considers not only the relevance but also the diversity of the result. We first prove that this problem is NP-hard, and then, we present an exact algorithm with several effective pruning strategies. Also, we develop an approximate algorithm with proved approximation ratio to support the DkGSK query. Considerable experiments on real data sets demonstrate the effectiveness and efficiency of our proposed algorithms.

Rising Star Evaluation Based on Extreme Learning Machine in Geo-Social Networks

In social networks, rising stars are junior individuals who may be not so charming at first but turn out to be outstanding over time. Recently, rising star evaluation has become a popular research topic in the field of social analysis, which is helpful for decision support, cognitive computation, and other practical problems. In this paper, we study the problem of rising star evaluation in geo-social networks. Specifically, given a topic keyword Q and a time point t, we aim at evaluating the latent influence of users to find rising stars, which refer to experts who have few activities and little impact currently on the underlying geo-social network but may become influential experts in the future. To efficiently evaluate future stars, we propose a novel processing framework based on extreme learning machine (ELM) called FS-ELM. FS-ELM consists of three key components. The first component constructs features by incorporating social topology and user behavior patterns. The second component extracts supervised information by discovering topic experts of Q at time (t + Δt); that is, excluding those detected at time t, topic experts obtained at time (t + Δt) can be regarded as rising stars at time t. The third component is ELM-based future star classification that leverages ELM as a departure point to evaluate whether a user is a rising star. Our experimental studies conducted on real-world datasets show that (1) FS-ELM can effectively discover rising stars with a query topic at time t and outperform other traditional methods and (2) user social characteristics have an important impact on the rising star evaluation. This paper studies a novel problem, namely, rising star evaluation in geo-social networks. We propose an advanced processing framework based on ELM by exploiting social topology characteristics and user behavior patterns. The experimental results encouragingly demonstrate the efficiency and effectiveness of the proposed approach.