Inferring Geographic Coincidence in Ephemeral Social Networks (original) (raw)

Uncovering the Spatio-temporal Structure of Social Networks Using Cell Phone Records

2012 IEEE 12th International Conference on Data Mining Workshops, 2012

Although research in the areas of human mobility and social networks is extensive, our knowledge of the relationship between the mobility and the social network of an individual is very limited, mainly due to the complexity of accessing adequate data to be able to capture both mobility and social interactions. In this paper we present and characterize some of the spatio-temporal features of social networks extracted from a large-scale dataset of cell phone records. Our goal is to measure to which extent individual mobility shapes the characteristics of a social network. Our results show a nontrivial dependence between social network structure and the spatial distribution of its elements. Additionally, we quantify with detail the probability of a contact to be at a certain distance, and find that it may be described in the framework of gravity models, with different decaying rates for urban and interurban scales.

Bridging the gap between physical location and online social networks

2010

This paper examines the location traces of 489 users of a location sharing social network for relationships between the users' mobility patterns and structural properties of their underlying social network. We introduce a novel set of location-based features for analyzing the social context of a geographic region, including location entropy, which measures the diversity of unique visitors of a location. Using these features, we provide a model for predicting friendship between two users by analyzing their location trails. Our model achieves significant gains over simpler models based only on direct properties of the co-location histories, such as the number of co-locations. We also show a positive relationship between the entropy of the locations the user visits and the number of social ties that user has in the network. We discuss how the offline mobility of users can have implications for both researchers and designers of online social networks.

Predicting encounter and colocation events in metropolitan areas

ArXiv, 2016

Despite an extensive literature has been devoted to mine and model mobility features, forecasting where, when and whom people will encounter/colocate still deserve further research efforts. Forecasting people's encounter and colocation features is the key point for the success of many applications ranging from epidemiology to the design of new networking paradigms and services such as delay tolerant and opportunistic networks. While many algorithms which rely on both mobility and social information have been proposed, we propose a novel encounter and colocation predictive model which predicts user's encounter and colocation events and their features by exploiting the spatio-temporal regularity in the history of these events. We adopt weighted features Bayesian predictor and evaluate its accuracy on two large scales WiFi and cellular datasets. Results show that our approach could improve prediction accuracy w.r.t standard naive Bayesian and some of the state-of-the-art predic...

Social Events in a Time-Varying Mobile Phone Graph

ArXiv, 2017

The large-scale study of human mobility has been significantly enhanced over the last decade by the massive use of mobile phones in urban populations. Studying the activity of mobile phones allows us, not only to infer social networks between individuals, but also to observe the movements of these individuals in space and time. In this work, we investigate how these two related sources of information can be integrated within the context of detecting and analyzing large social events. We show that large social events can be characterized not only by an anomalous increase in activity of the antennas in the neighborhood of the event, but also by an increase in social relationships of the attendants present in the event. Moreover, having detected a large social event via increased antenna activity, we can use the network connections to infer whether an unobserved user was present at the event. More precisely, we address the following three challenges: (i) automatically detecting large s...

Data Analysis on Location-Based Social Networks

Mobile Social Networking, 2013

The rapid growth of location-based social networks (LBSNs) has greatly enriched people's urban experience through social media and attracted increasing number of users in recent years. Typical location-based social networking sites allow users to "check in" at a physical place and share the location with their online friends, and therefore bridge the gap between the real world and online social networks. The availability of large amounts of geographical and social data on LBSNs provides an unprecedented opportunity to study human mobile behavior through data analysis in a spatial-temporal-social context, enabling a variety of locationbased services, from mobile marketing to disaster relief. In this chapter, we first introduce the background and framework of location-based mobile social networking. We next discuss the distinct properties, data analysis and research issues of location-based social networks, and present two illustrative examples to show the application of data mining to real-world location-based social networks.

Entangling Mobility and Interactions in Social Media

PLoS ONE, 2014

Daily interactions naturally define social circles. Individuals tend to be friends with the people they spend time with and they choose to spend time with their friends, inextricably entangling physical location and social relationships. As a result, it is possible to predict not only someone's location from their friends' locations but also friendship from spatial and temporal co-occurrence. While several models have been developed to separately describe mobility and the evolution of social networks, there is a lack of studies coupling social interactions and mobility. In this work, we introduce a model that bridges this gap by explicitly considering the feedback of mobility on the formation of social ties. Data coming from three online social networks (Twitter, Gowalla and Brightkite) is used for validation. Our model reproduces various topological and physical properties of the networks not captured by models uncoupling mobility and social interactions such as: i) the total size of the connected components, ii) the distance distribution between connected users, iii) the dependence of the reciprocity on the distance, iv) the variation of the social overlap and the clustering with the distance. Besides numerical simulations, a mean-field approach is also used to study analytically the main statistical features of the networks generated by a simplified version of our model. The robustness of the results to changes in the model parameters is explored, finding that a balance between friend visits and long-range random connections is essential to reproduce the geographical features of the empirical networks.

PREDICTING VENUES IN LOCATION BASED SOCIAL NETWORK

The circulation of the social networks and the evolution of the mobile phone devices has led to a big usage of location based social networks application such as Foursquare, Twitter, Swarm and Zomato on mobile phone devices mean that huge dataset which is containing a blend of information about users behaviour's, social society network of each users and also information about each of venues, all these information available in mobile location recommendation system .These datasets are much more different from those which is used in online recommender systems, these datasets have more information and details about the users and the venues which is allowing to have more clear result with much more higher accuracy of the analysing in the result. In this paper we examine the users behaviour's and the popularity of the venue through a large check-ins dataset from a location based social services, Foursquare: by using large scale dataset containing both user check-in and location information .Our analysis expose across 3 different cities.On analysis of these dataset reveal a different mobility habits, preferring places and also location patterns in the user personality. This information about the users behaviour's and each of the location popularity can be used to know the recommendation systems and to predict the next move of the users depending on the categories that the users attend to visit and according to the history of each users check-ins.

GEOSO - A Geo-Social Model: From Real-World Co-occurrences to Social Connections

Lecture Notes in Computer Science, 2011

As the popularity of social networks is continuously growing, collected data about online social activities is becoming an important asset enabling many applications such as target advertising, sale promotions, and marketing campaigns. Although most social interactions are recorded through online activities, we believe that social experiences taking place offline in the real physical world are equally if not more important. This paper introduces a geo-social model that derives social activities from the history of people's movements in the real world, i.e., who has been where and when. In particular, from spatiotemporal histories, we infer real-world co-occurrences-being there at the same time-and then use co-occurrences to quantify social distances between any two persons. We show that straightforward approaches either do not scale or may overestimate the strength of social connections by giving too much weight to coincidences. The experiments show that our model well captures social relationships between people, even on partially available data.

Friendship and mobility: user movement in location-based social networks

2011

Abstract Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks.

Mining Human Mobility in Location-Based Social Networks

Synthesis Lectures on Data Mining and Knowledge Discovery, 2015

e series publishes 50-to 150-page publications on topics pertaining to data mining, web mining, text mining, and knowledge discovery, including tutorials and case studies. Potential topics include: data mining algorithms, innovative data mining applications, data mining systems, mining text, web and semi-structured data, high performance and parallel/distributed data mining, data mining standards, data mining and knowledge discovery framework and process, data mining foundations, mining data streams and sensor data, mining multi-media data, mining social networks and graph data, mining spatial and temporal data, pre-processing and post-processing in data mining, robust and scalable statistical methods, security, privacy, and adversarial data mining, visual data mining, visual analytics, and data visualization.