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With the development of wireless communications, several studies have been performed on Location based Services due to their numerous applications. Amongst those recommendations, Travel Planning and Recommendations are few of the active topics. When it comes to movement patterns and mobility, human beings are restricted in motion due to social, financial and geographical constraints. Using check-in data from a former location based social network namely Gowalla and airport flights and route data from openflights.org, authors aim to extract a graph model from time series data of user check-ins. In this study, authors have identified patterns of location (latitude, longitude) visits using directed weighted graph. In addition to it, we have mapped airports to identified maps using latitude, longitude and used routes. This has helped to identify nearest airport routes probably used by Gowalla users. Hence, by mapping two heterogeneous graphs i.e. the Gowalla check-in data and openflights.org airport and flights data, we have tried to extract the most commonly used airport routes travelled by Gowalla users.

Graph Partitioning is one of the favorite research topics among researchers since the 70s. It attracts a diverse group of researchers from various fields such as engineering, science and mathematics. In the last decade, the graphs have increased in size to billions of vertices. Despite the fact that storage devices have become cheaper, processing these huge spanning graphs is not possible for a single machine. This call for the need of partitioning the graph so a group of machines can perform various parallel calculations on them which would save time and produce quick results. The research problem is that the ratio of boundary vertices to interior vertices increases with the increase in number of partitions for existing partitioning techniques available. To address this issue, the random edge selection method of Graph Lab algorithm was replaced with four suggested edge sorting techniques. The results were compared with the random edge selection method of Graph Lab using various performance parameters.