S
Seth A. Myers
Researcher at Purdue University
Publications - 14
Citations - 5479
Seth A. Myers is an academic researcher from Purdue University. The author has contributed to research in topics: Social network & Dynamic network analysis. The author has an hindex of 11, co-authored 14 publications receiving 4836 citations. Previous affiliations of Seth A. Myers include Twitter & Stanford University.
Papers
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Proceedings ArticleDOI
Friendship and mobility: user movement in location-based social networks
TL;DR: 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.
Journal ArticleDOI
Spontaneous synchrony in power-grid networks
TL;DR: In this article, a condition for the stability of the synchronous state enables identification of network parameters that enhance spontaneous synchronization, highlighting the possibility of smart grids that operate optimally in real-world systems.
Proceedings ArticleDOI
Information diffusion and external influence in networks
TL;DR: A model in which information can reach a node via the links of the social network or through the influence of external sources is presented and an efficient model parameter fitting technique is developed and applied to the emergence of URL mentions in the Twitter network.
Proceedings ArticleDOI
Information network or social network?: the structure of the twitter follow graph
TL;DR: A characterization of the topological features of the Twitter follow graph is provided, analyzing properties such as degree distributions, connected components, shortest path lengths, clustering coefficients, and degree assortativity to hypothesize that from an individual user's perspective, Twitter starts off more like an information network, but evolves to behave more like a social network.
Posted Content
On the Convexity of Latent Social Network Inference
Seth A. Myers,Jure Leskovec +1 more
TL;DR: This work considers contagions propagating over the edges of an unobserved social network, and presents a maximum likelihood approach based on convex programming with a l1-like penalty term that encourages sparsity to identify the optimal network that best explains the observed data.