Humans are social animals, they interact with different com-munities of friends to conduct different activities. The lit-erature shows that human mobility is constrained by their social relations. In this paper, we investigate the social im-pact of a person’s communities on his mobility, instead of all friends from his online social networks. This study can be particularly useful, as certain social behaviors are influ-enced by specific communities but not all friends. To achieve our goal, we first develop a measure to characterize a per-son’s social diversity, which we term ‘community entropy’. Through analysis of two real-life datasets, we demonstrate that a person’s mobility is influenced only by a small frac-tion of his communities and the influence depends on the social contexts of the communities. We then exploit ma-chine learning techniques to predict users ’ future movement based on their communities ’ information. Extensive experi-ments demonstrate the prediction’s effectiveness.

Abstract—With the emerging of online social network services, quantitative studies on social influence become achievable. Lead-ership is one of the most intuitive and common forms for social influence; understanding it could result in appealing applications such as targeted advertising and viral marketing. In this work, we focus on investigating leaders ’ influence for event prediction in social networks. We propose an algorithm based on events that users conduct to discover leaders in social communities. Analysis on the leaders that we found on a real-life social network dataset leads us to several interesting observations, such as that leaders do not have significantly higher number of friends but are more active than other community members. We demonstrate the effectiveness of leaders ’ influence on users ’ behaviors by learning tasks: given a leader has conducted one event, whether and when a user will perform the event. Experimental results show that with only a few leaders in a community the event predictions are always very effective. I.

Abstract—With the ubiquity of GPS-enabled devices and location-based social network services, research on human mo-bility becomes quantitatively achievable. Understanding it could lead to appealing applications such as city planning and epi-demiology. In this paper, we focus on predicting whether two individuals are friends based on their mobility information. Intuitively, friends tend to visit similar places, thus the number of their co-occurrences should be a strong indicator of their friendship. Besides, the visiting time interval between two users also has an effect on friendship prediction. By exploiting machine learning techniques, we construct two friendship prediction models based on mobility information. The first model focuses on predicting friendship of two individuals with only one of their co-occurred places ’ information. The second model proposes a solution for predicting friendship of two individuals based on all their co-occurred places. Experimental results show that both of our models outperform the state-of-the-art solutions. I.

Abstract. Nowadays, people conduct a lot of activities with their online social networks. With the large amount of social data available, quanti-tative analysis of social influence becomes feasible. In this PhD project, we aim to study users ’ social influence at the community level, mainly because users in social networks are naturally organized in communities and communities play fundamental roles in understanding social behav-iors and social phenomenons. Through experiments with a location-based social networks dataset, we start by demonstrating communities ’ influ-ence on users ’ mobility, and then we focus on the influence of leaders in the communities. As a next step, we intend to detect users that act as structural hole spanners and analyze their social influence across different communities. Based on these studies, we plan to propose a unified ap-proach to quantify users ’ social influence and investigate its applications, for example, in social interaction and behavior analysis. 1