activity
20172026
most citedMetropolitan Scale and Longitudinal Dataset of Anonymized Human Mobility Trajectories

5 citations · 7 across the 4 of their papers we have counts for

collaborators

6 papers

physics.soc-ph2026

Human mobility shapes how urban policy effects spread across cities

Bijin Joseph, Hamish Gibbs, Takahiro Yabe +1

Urban policies are often evaluated assuming their effects decay with geographic distance. However, cities are systems of human movement, and policy effects propagate through indivi…

cs.SI2026

Experienced partisan segregation across patterns of mobility behavior in US cities

Marco Tonin, Michele Tizzoni, Bruno Lepri +1

Residential and social sorting along political lines structure Americans' daily interactions, often limiting encounters with opposing viewpoints. Here, we combine high-resolution a…

physics.soc-ph2025

When Influence Misleads: Informational and Strategic Limits of Social Learning in Trading Networks

Bijin Joseph, Christoph Riedl, Alex Pentland +1

Social learning is a fundamental mechanism shaping decision-making across numerous social networks, including social trading platforms. In those platforms, investors combine tradit…

physics.soc-ph2025★ 1 cited

Community-centric modeling of citation dynamics explains collective citation patterns in science, law, and patents

Sadamori Kojaku, Robert Mahari, Sandro Claudio Lera +3

Many human knowledge systems, such as science, law, and invention, are built on documents and the citations that link them. Citations, while serving multiple purposes, primarily fu…

cs.SI2023★ 5 cited

Metropolitan Scale and Longitudinal Dataset of Anonymized Human Mobility Trajectories

Takahiro Yabe, Kota Tsubouchi, Toru Shimizu +4

Modeling and predicting human mobility trajectories in urban areas is an essential task for various applications. The recent availability of large-scale human movement data collect…

cs.AI2017★ 1 cited

Improved Learning in Evolution Strategies via Sparser Inter-Agent Network Topologies

Dhaval Adjodah, Dan Calacci, Yan Leng +3

We draw upon a previously largely untapped literature on human collective intelligence as a source of inspiration for improving deep learning. Implicit in many algorithms that atte…