Incremental Community Mining in Location-based Social Network

A social network can be defined as a set of social entities connected by a set of social relations. These relations often change and differ in time. Thus, the fundamental structure of these networks is dynamic and increasingly developing. Investigating how the structure of these networks evolves over the observation time affords visions into their evolution structure, elements that initiate the changes, and finally foresee the future structure of these networks. One of the most relevant properties of networks is their community structure – set of vertices highly connected between each other and loosely connected with the rest of the network. Subsequently networks are dynamic, their underlying community structure changes over time as well, i.e they have social entities that appear and disappear which make their communities shrinking and growing over time. The goal of this paper is to study community detection in dynamic social network in the context of location-based social network. In this respect, we extend the static Louvain method to incrementally detect communities in a dynamic scenario following the direct method and considering both overlapping and non-overlapping setting. Finally, extensive experiments on real datasets and comparison with two previous methods demonstrate the effectiveness and potential of our suggested method

Location-based Social Network for Cities & Neighbourhood Sustainable Development

Online Social Network (OSN) is categorized as Web 2.0 which is defined by O'Reilly in 2004, is the idea of mutually maximizing collective intelligence and added value for each participant by dynamic information sharing and creation. Current trend sbows that the next big thing in OSN is Location-based Social Networking (LBSN) which is the composite of OSN and Location-based Service (LBS). The goal of this paper is to study on Malaysian online social behaviour and to explore what are the key technologies of LBSN to support the development of neighbourhoods where residents feel a sense of connection to their local community and ability to engage in that community. Problems and opportunities identified are: I) Lack of research has been done to nnderstand Malaysian online social behavior in the context of cities & neighbourhood development, 2) Modem societies are said to lives in a condition of individualism and 3) Malaysia has strong networked community and there are a number of social Application Programming Interface (API) which provide a great opportunities for developers to create an application which can support the idea of smart, liveable and sustainable cities. The objectives of the research are: 1) To study on Malaysian social behavior in using Location-based Social Network (LBSN) , motivation for participation and pattern of use, 2) To identifY and understand key technologies of LBSN, and 3) To design an engaging LBSN which leverage on key technologies for neighbourhood and cities' sustainable development. Survey instrument is used as data collection tool to investigate the Malaysian online social behaviour and gauge their views on civil issues such as crime in their residential. Interview also is carried out to the owner of existing crime mapping system to identifY the gaps and opportunities for improvements. This research discovers that Malaysians are socially active in online community network and have strong civic conscious to make our neighbourhood works better. Govermnent shonld look forward into open data for beneficial of public. With proper neighbourhood planning, it will contribute to sustainable community which can help country's development

Semantically Oriented Sentiment Mining in Location-Based Social Network Spaces

In this paper we describe a system that performs sentiment classification of reviews from social network sites using natural language techniques. The pattern-based method used in our system, applies classification rules for positive or negative sentiments depending on its overall score, calculated with the aid of SentiWordNet. We investigate several classifier models created from a combination of different methods applied at word and review levels. Our experimental results show that using part-of-speech helps to achieve better accuracy

Research on Potential Friend Recommendation Algorithm in Location-Based Social Network

伴随着移动互联网技术与地理定位技术的崛起，基于位置的服务迅速地渗透到互联网的各类网站和应用中。其中，融合基于位置的服务与传统社交网络结构的位置社交网络发展迅猛，许多结合位置服务的社交类应用受到了许多互联网用户的青睐。用户在位置社交网络上分享自己的位置信息，在自己喜欢的地点进行签到，发表包含位置信息的状态和微博，通过关注周围的人寻找与自己有相同兴趣爱好的人。 随着位置社交网络应用中用户数据和地理位置数据的迅速膨胀，用户想要快速、准确地发现自己需要的信息变得越来越困难，基于位置社交网络的推荐系统开始出现。研究者们致力于根据用户数据和地理位置数据中包含的信息，分析用户的喜好，并针对用户的喜好对用...With the rise of mobile Internet technology and geograohic positioning technology, location-based services rapidly penetrate into various types of Internet sites and applications. Among them, the location-based social networks which contained location-based services and traditional social network structure developed rapidly. A lot of social networking applications combined with location-based serv...学位：工学硕士院系专业：软件学院_计算机软件与理论学号：2432012115227

LOCATION CHEATING: A SECURITY CHALLENGE TO LOCATION-BASED SOCIAL NETWORK SERVICES

Location-based mobile social network services such as Foursquare and Gowalla have grown exponentially over the past several years. These location-based services utilize the geographical position to enrich user experiences in a variety of contexts, including location-based searching and location-based mobile advertising. To attract more users, the location-based mobile social network services provide real-world rewards to the user, when a user checks in at a certain venue or location. This gives incentives for users to cheat on their locations

Location Cheating: A Security Challenge to Location-based Social Network Services

Location-based mobile social network services such as foursquare and Gowalla have grown exponentially over the past several years. These location-based services utilize the geographical position to enrich user experiences in a variety of contexts, including location-based searching and location-based mobile advertising. To attract more users, the location-based mobile social network services provide real-world rewards to the user, when a user checks in at a certain venue or location. This gives incentives for users to cheat on their locations. In this report, we investigate the threat of location cheating attacks, find the root cause of the vulnerability, and outline the possible defending mechanisms. We use foursquare as an example to introduce a novel location cheating attack, which can easily pass the current location verification mechanism (e.g., cheater code of foursquare). We also crawl the foursquare website. By analyzing the crawled data, we show that automated large scale cheating is possible. Through this work, we aim to call attention to location cheating in mobile social network services and provide insights into the defending mechanisms.Comment: 10 pages, 8 figures, accepted by the 31st International Conference on Distributed Computing Systems (ICDCS 2011

Urban Regional Social Community Detection Using Location Based Social Network Big Data

In this paper, we propose a methodology of applying location based social network (LBSN) Big Data to detect urban regional social communities (URSCs) and analyze their activation levels. For this, we first construct a social spatial network (SSN) based on the LBSN Big Data of a city. Then, by applying a modularity optimization algorithm to the SSN constructed, where modularity is a measure to check the strength of clustered networks, we detect the boundaries of the URSCs. The activation level of each detected URSC is further analyzed based on a diversity index, i.e., Shannon entropy. For experiments, we apply the proposed methodology to the city of Seoul where the LBSN Big Data is collected from Foursquare social networks. Through the experimental results, we observe that the detected URSCs match well with the URSCs known by the Seoul citizen from which we can confirm the effectiveness of our proposed methodology in detecting USRCs and analyzing their activation levels

Location-based Social Network for Cities & Neighbourhood Sustainable Development

Online Social Network (OSN) is categorized as Web 2.0 which is defined by O'Reilly in 2004, is the idea of mutually maximizing collective intelligence and added value for each participant by dynamic information sharing and creation. Current trend sbows that the next big thing in OSN is Location-based Social Networking (LBSN) which is the composite of OSN and Location-based Service (LBS). The goal of this paper is to study on Malaysian online social behaviour and to explore what are the key technologies of LBSN to support the development of neighbourhoods where residents feel a sense of connection to their local community and ability to engage in that community. Problems and opportunities identified are: I) Lack of research has been done to nnderstand Malaysian online social behavior in the context of cities & neighbourhood development, 2) Modem societies are said to lives in a condition of individualism and 3) Malaysia has strong networked community and there are a number of social Application Programming Interface (API) which provide a great opportunities for developers to create an application which can support the idea of smart, liveable and sustainable cities. The objectives of the research are: 1) To study on Malaysian social behavior in using Location-based Social Network (LBSN) , motivation for participation and pattern of use, 2) To identifY and understand key technologies of LBSN, and 3) To design an engaging LBSN which leverage on key technologies for neighbourhood and cities' sustainable development. Survey instrument is used as data collection tool to investigate the Malaysian online social behaviour and gauge their views on civil issues such as crime in their residential. Interview also is carried out to the owner of existing crime mapping system to identifY the gaps and opportunities for improvements. This research discovers that Malaysians are socially active in online community network and have strong civic conscious to make our neighbourhood works better. Govermnent shonld look forward into open data for beneficial of public. With proper neighbourhood planning, it will contribute to sustainable community which can help country's development