The location based social networking services (LBSNSs) are becoming very popular today. In LBSNSs, such as Foursquare, users can explore their places of interests around their current locations, check in at these places to share their locations with their friends, etc. These check-ins contain rich information and imply human mobility patterns; thus, they can greatly facilitate mining and analysis of local geographic topics driven by users' trajectories. The local geographic topics indicate the potential and intrinsic relations among the locations in accordance with users' trajectories. These relations are useful for users in both location and friend recommendations. In this paper, we focus on exploring the local geographic topics through check-ins in Pittsburgh area in Foursquare. We use the Latent Dirichlet Allocation (LDA) model to discover the local geographic topics from the checkins. We also compare the local geographic topics on weekdays with those at weekends. Our results show that LDA works well in finding the related places of interests.

Wide area backbone communication networks are subject to a variety of hazards that can result in network component failures. Hazards such as power failures and storms can lead to geographical correlated failures. Recently there has been increasing interest in determining the ability of networks to survive geographic correlated failures and a number of measures to quantify the effects of failures have appeared in the literature. This paper proposes a the use of weighted spectrum to evaluate network survivability regarding geographic correlated failures. Further we conduct a comparative analysis by finding the most vulnerable geographic cuts or nodes in the network though solving an optimization problem to determine the cut with the largest impact for a number of measures in the literature as well as weighted spectrum. Numerical results on several sample network topologies show that the worst-case geographic cuts depend on the measure used in an unweighted or weighted graph. The proposed weighted spectrum measure is shown to be more versatile than other measures in both unweighted and weighted graphs.

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Location-Based Social Networks (LBSNs), (also called as Geo-Social Networks), has been attracting more and more users by providing services that integrate social activities with location information. LBSN systems usually provide support for indicating various Points of Interest (POIs) but there is no straightforward rating mechanism for POIs in most LBSNs [1]. POI recommendations in LBSNs, thus, is an important and challenging research topic. In this paper, we first investigate the dataset crawled from Foursquare to explore the features that attract and influence users to check in at various POIs. Based on the analysis results, we propose a HITS (Hypertext Induced Topic Search)-based POI recommendation algorithm to recommend POIs to LBSN users that can also incorporate the impact of the social relationships on recommendations. We evaluate our proposed model on Foursquare dataset and compare our results with the latest POI recommendation algorithm. The experimental results show that our approach performs better.

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Current distributed Peer-to-Peer (P2P) applications o®er a variety of°exible and convenient services through the Internet to users from di®erent geographic locations and also support enhanced communications and interactions among them. However, security and trust are the key concerns in such applications as users in such an environment are typically unknown to each other. Trust management systems aim to decrease the risks in such applications and protect benign users from malicious users. In this paper, we introduce six attack models and propose a novel Bayesian Reputation Management System (BaRMS) for P2P environments using Bayesian probability and Markov Chain theories. BaRMS handles both positive and negative feedback. Through a case study, we show that this approach is better than the existing EigenTrust framework for P2P systems. Moreover, our simulation results of a P2P¯le sharing system also show that the proposed algorithm can greatly improve the performance over a system that does not include a trust management service under various attack models. We show that our proposed Bayesian Reputation Computation Algorithm (BaRCA) performs better than the EigenTrust algorithm under various models.

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