Geographic Social Network Research Articles

Time Series Geographic Social Network Dynamic Preference Group Query

Driven by experience and social impact of the new life, user preferences continue to change over time. In order to make up for the shortcomings of existing geographic social network models that often cannot obtain user dynamic preferences, a time-series geographic social network model was constructed to detect user dynamic preferences, a dynamic preference value model was built for user dynamic preference evaluation, and a dynamic preferences group query (DPG) was proposed in this paper . In order to optimize the efficiency of the DPG query algorithm, the UTC-tree index user timing check-in record is designed. UTC-tree avoids traversing all user check-in records in the query, accelerating user dynamic preference evaluation. Finally, the DPG query algorithm is used to implement a well-interacted DPG query system. Through a large number of comparative experiments, the validity of UTC-tree and the scalability of DPG query are verified.

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.

The emotional cost of distance: Geographic social network dispersion and post-traumatic stress among survivors of Hurricane Katrina

RationaleSocial networks offer important emotional and instrumental support following natural disasters. However, displacement may geographically disperse network members, making it difficult to provide and receive support necessary for psychological recovery after trauma. ObjectivesWe examine the association between distance to network members and post-traumatic stress using survey data, and identify potential mechanisms underlying this association using in-depth qualitative interviews. MethodsWe use longitudinal, mixed-methods data from the Resilience in Survivors of Katrina (RISK) Project to capture the long-term effects of Hurricane Katrina on low-income mothers from New Orleans. Baseline surveys occurred approximately one year before the storm and follow-up surveys and in-depth interviews were conducted five years later. We use a sequential explanatory analytic design. With logistic regression, we estimate the association of geographic network dispersion with the likelihood of post-traumatic stress. With linear regressions, we estimate the association of network dispersion with the three post-traumatic stress sub-scales. Using maximal variation sampling, we use qualitative interview data to elaborate identified statistical associations. ResultsWe find network dispersion is positively associated with the likelihood of post-traumatic stress, controlling for individual-level socio-demographic characteristics, exposure to hurricane-related trauma, perceived social support, and New Orleans residency. We identify two social-psychological mechanisms present in qualitative data: respondents with distant network members report a lack of deep belonging and a lack of mattering as they are unable to fulfill obligations to important distant ties. ConclusionResults indicate the importance of physical proximity to emotionally-intimate network ties for long-term psychological recovery.

The United States Heart and Lung Transplant “Social” Network. Insights from the UNOS Database

<h3>Purpose</h3> Although the demand for donor organs increased substantially, the number of organ donors remains relatively stable. With increasing availability of large-scale data and methodologies to decode complexity, it is possible to model connected systems as networks to improve their understanding. We hypothesized that the social structure of the Heart and Lung Transplant network can be modeled as a Geographical Social Network (GSN). <h3>Methods and Materials</h3> We studied a social structure of a network constructed using data of over 150,000 donors and recipients collected by the UNOS database between 1987 and 2010 to recreate a GSN. In this weighted network, geographical regions defined by US states or Zip Codes are nodes linked by organ transplants occurring between them, involving a social relation between two individuals. We used a weighed network reconstruction, community unfolding algorithms and a geo-graph theoretical approach to reconstruct the social network of the UNOS database. <h3>Results</h3> The social structure of the UNOS-database Heart and Lung Transplant Network (Figure 1A) reveals a community organization resembling UNOS regional divisions (1B). Regional organ sharing follows scale-free characteristics (1C) Within the heart transplant network (1D), African American and Hispanic donors show least defined communities and topology suggesting disparity in the distribution and a differential effect of organ donation practices in the minority. <h3>Conclusions</h3> Our study identifies the Social Network of organ donation in the US, links link demographic factors and patterns in the context of complex networks, identify disparity and opens a new chapter in understanding organ practices through the study of Complex Networks.

Understanding organ transplantation in the USA using geographical social networks

As of December 2011, 110,629 Americans are waiting for an organ transplant and yet only 28,664 people received organ transplants in 2010. This fact alone demonstrates the USA is facing an organ shortage crisis. Added to this, there is strong evidence that the organs made available are not being used efficiently, with 20 % of them going unused (for kidneys). It is tempting to investigate how new allocation policies could be implemented, but it would be more prudent to first understand the structure of the organ donation system in the USA. In spite of availability of data on transplants, to our knowledge no proper analysis has been done using the available data. This paper looks at organ transplantation data and what its structure may reveal about the allocation process currently in place. In order to structure the data, we used techniques from network sciences to build a network of locations (henceforth called a geographical social network, GSN) representing all transplants in the USA since 1987—locations represent states or zipcodes in the USA. This social structure is then analyzed using techniques from network sciences to bring clarity to the organ donation process. One of the main items in organ donation policies is the issue of locality which argues that allocation mechanisms should try to prioritize patients who are near the location where the organ became available. This is an important issue because the longer the organ takes to be transplanted the worse its quality. We show that network science techniques may shed some light on this process and demonstrate inconsistencies across different organs.

Bunjil – A social network for proactive monitoring of tropical rainforests

A tool is proposed to help protect tropical rainforests through early detection of deforestation. The solution automates delivery of the latest satellite images into a collaborative geographic social network. This connects local conservation groups in remote tropical regions with a network of volunteers who share the timely analysis of satellite images. Volunteers are prompted to review the latest images of an area from various sources and mark-up any recent changes observed, such as new roads or clearings. The solution captures the coordinates and sends concise reports to the local group to respond to the observed threats.The solution is called Bunjil Forest Watch. Copyright 2010 Chris Goodman. No part of this article may be reproduced by any means without the written consent of the publisher.

Bunjil A social network for proactive monitoring of tropical rainforests

A tool is proposed to help protect tropical rainforests through early detection of deforestation. The solution automates delivery of the latest satellite images into a collaborative geographic social network. This connects local conservation groups in remote tropical regions with a network of volunteers who share the timely analysis of satellite images. Volunteers are prompted to review the latest images of an area from various sources and mark-up any recent changes observed, such as new roads or clearings. The solution captures the coordinates and sends concise reports to the local group to respond to the observed threats.The solution is called Bunjil Forest Watch. Copyright 2010 Chris Goodman. No part of this article may be reproduced by any means without the written consent of the publisher.

THE DIFFUSION OF ILLEGAL INNOVATIONS AMONG MANAGEMENT ELITES.

Top managers learn about innovative business practices in many ways; they observe other firms, maintain memberships in trade associations, discuss methods with other executives and outside board members, and read the business press. But how do illegal innovations diffuse among management elites? Illegality requires stealth and opaqueness; one cannot simply vicariously observe others' illegal innovations from a distance, or learn about them from trade associations. To address this issue we examine the spread of the illegal innovation of backdating CEO stock options. We find that board of director interlocks, geographical social networks, and CEO power all play a role in the rise and spread of this illegal innovation.