Proceedings ArticleDOI
Friendship and mobility: user movement in location-based social networks
Eunjoon Cho,Seth A. Myers,Jure Leskovec +2 more
- pp 1082-1090
TLDR
A model of human mobility that combines periodic short range movements with travel due to the social network structure is developed and it is shown that this model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance.Abstract:
Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.read more
Citations
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Geometric Deep Learning: Going beyond Euclidean data
TL;DR: In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions) and are natural targets for machine-learning techniques as mentioned in this paper.
Journal ArticleDOI
Natural Scales in Geographical Patterns
Telmo Menezes,Camille Roth +1 more
TL;DR: The detection of phase transitions constitutes the first objective method of characterising endogenous, natural scales of human movement and allows us to draw discrete multi-scale geographical boundaries, potentially capable of providing key insights in fields such as epidemiology or cultural contagion.
Proceedings ArticleDOI
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Jiaxi Tang,Ke Wang +1 more
TL;DR: A Convolutional Sequence Embedding Recommendation Model »Caser» is proposed, which is to embed a sequence of recent items into an image in the time and latent spaces and learn sequential patterns as local features of the image using convolutional filters.
Journal ArticleDOI
Geometric deep learning: going beyond Euclidean data
TL;DR: Deep neural networks are used for solving a broad range of problems from computer vision, natural-language processing, and audio analysis where the invariances of these structures are built into networks used to model them.
Proceedings ArticleDOI
Time-aware point-of-interest recommendation
TL;DR: This paper defines a new problem, namely, the time-aware POI recommendation, to recommend POIs for a given user at a specified time in a day, and develops a collaborative recommendation model that is able to incorporate temporal information.
References
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Birds of a Feather: Homophily in Social Networks
TL;DR: The homophily principle as mentioned in this paper states that similarity breeds connection, and that people's personal networks are homogeneous with regard to many sociodemographic, behavioral, and intrapersonal characteristics.
Journal ArticleDOI
Power-Law Distributions in Empirical Data
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Journal ArticleDOI
Understanding individual human mobility patterns
Marta C. González,A R Cesar Hidalgo,A R Cesar Hidalgo,Albert-László Barabási,Albert-László Barabási,Albert-László Barabási +5 more
TL;DR: In this article, the authors study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period and find that the individual travel patterns collapse into a single spatial probability distribution, indicating that humans follow simple reproducible patterns.
Journal ArticleDOI
Limits of Predictability in Human Mobility
Chaoming Song,Zehui Qu,Zehui Qu,Nicholas Blumm,Nicholas Blumm,Albert-László Barabási,Albert-László Barabási +6 more
TL;DR: Analysis of the trajectories of people carrying cell phones reveals that human mobility patterns are highly predictable, and a remarkable lack of variability in predictability is found, which is largely independent of the distance users cover on a regular basis.
Journal ArticleDOI
The scaling laws of human travel
TL;DR: It is shown that human travelling behaviour can be described mathematically on many spatiotemporal scales by a two-parameter continuous-time random walk model to a surprising accuracy, and concluded that human travel on geographical scales is an ambivalent and effectively superdiffusive process.