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Exploring communities for effective location prediction
- In: Proc. WWW (Companion Volume), ACM (2015
"... ABSTRACT Humans are social animals, they interact with different communities to conduct different activities. The literature has shown that human mobility is constrained by their social relations. In this work, we investigate the social impact on a user's mobility from his communities in order ..."
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ABSTRACT Humans are social animals, they interact with different communities to conduct different activities. The literature has shown that human mobility is constrained by their social relations. In this work, we investigate the social impact on a user's mobility from his communities in order to conduct location prediction effectively. Through analysis of a real-life dataset, we demonstrate that (1) a user gets more influences from his communities than from all his friends; (2) his mobility is influenced only by a small subset of his communities; (3) influence from communities depends on social contexts. We further exploit a SVM to predict a user's future location based on his community information. Experimental results show that the model based on communities leads to more effective predictions than the one based on friends.
DeepCity: A Feature Learning Framework for Mining Location Check-ins
"... Abstract Online social networks being extended to geographical space has resulted in large amount of user check-in data. Understanding check-ins can help to build appealing applications, such as location recommendation. In this paper, we propose DeepCity, a feature learning framework based on deep ..."
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Abstract Online social networks being extended to geographical space has resulted in large amount of user check-in data. Understanding check-ins can help to build appealing applications, such as location recommendation. In this paper, we propose DeepCity, a feature learning framework based on deep learning, to profile users and locations, with respect to user demographics and location category prediction. Both of the predictions are essential for social network companies to increase user engagement. The key contribution of DeepCity is the proposal of task-specific random walk which uses the location and user properties to guide the feature learning to be specific to each prediction task. Experiments conducted on 42M check-ins in three cities collected from Instagram have shown that DeepCity achieves a superior performance and outperforms state-of-the-art models significantly.
Community-driven Social Influence Analysis and Applications
"... 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 ..."
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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
