Human mobility, social ties, and link prediction

By Dashun Wang, Dino Pedreschi, Chaoming Song, Fosca Giannotti, Albert-László Barabási. Knowledge Discovery and Data Mining, 2011

723 citations recorded by the source corpus.

Subfields: Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/117fc73987a2808a90bf093be5730e64f7514d30

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In Statistical and Nonlinear Physics

In Transportation

Citations in the corpus

Negative Link Prediction in Social Media (2014, background): “In such cases, straightforward accuracy measures are well known to be misleading [25].”
Hierarchical Edge Caching in Device-to-Device Aided Mobile Networks: Modeling, Optimization, and Design (2018, methodology): “To exploit the similarity of the preference of users, we deﬁne a content factor C uv between users u and v , which is covered by calculating the Cosine Similarity of their preference as [35], [36] Larger value of C uv indicates that user u and user v have more common interests in content.”
Link prediction in social networks: the state-of-the-art (2014, background): “By combining both mobility and network measures, they showed that the prediction accuracy can be signiﬁcantly improved in supervised learning [31].”
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, background): “As a consequence, mobility prediction is a critical issue for the operators in order to determine capacity estimation, resource allocation, and so forth [216]–[219].”
Microscopic Social Influence (2012, background): “Recent studies on human mobility [10, 11, 12] have revealed strong correlation between geographical and social distance of individuals (e.g., users tend to make more calls to closer friends within closer geographical distance).”
Clustering daily patterns of human activities in the city (2012, background): “…recent studies on human mobility and dynamics employing large-scale objective data such as mobile phone or GPS traces of individual trajectories (Wang et al. 2011a; Song et al. 2010; Gonzalez et al. 2008; Candia et al. 2008), we linked in the usually absent rich information regarding activity…”
Smart cities of the future (2012, neutral): “Developing and coupling databases which in turn are being forged using new kinds of media for collecting data through sensing, mining online transactions, and the automated recording of behaviour in the environment and communication, is one of the key foci in our project [46].”
Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach (2019, background): “Using LBSN data, positive correlations between social proximity and mobility homophily have been universally found [8, 33].”
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning (2017, background): “Patterns of movement and of colocation can be used to infer relationships and predict new social ties (Hsieh & Li 2014, Pham et al. 2013, Wang et al. 2011). et al. 2009) were able to identify friends and non-friends with a high degree of accuracy using Bluetooth sensors, which can detect other…”
Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014, background): “A first group of works tries to understand the interplay of mobility patterns and social ties [Cho et al. 2011; Crandall et al. 2010; Pan et al. 2013; Wang et al. 2011].”
Exploiting homophily effect for trust prediction (2013, background): “The huge disproportion of pairs of users with (positive samples) and without relations (negative samples) makes the classification problem extremely unbalanced and the performance of these methods are sensitive to the sampled negative samples [23].”
Unveiling the complexity of human mobility by querying and mining massive trajectory data (2011, background): “, [43]), and call for challenging extensions of our mining and querying framework.”