Search Results for author: Hang-Hyun Jo

Found 4 papers, 4 papers with code

Burst-tree decomposition of time series reveals the structure of temporal correlations

1 code implementation31 Jul 2019 Hang-Hyun Jo, Takayuki Hiraoka, Mikko Kivelä

In this paper we propose a method of decomposing an event sequence into a set of IETs and a burst tree, which exactly captures the structure of temporal correlations that is entirely missing in the analysis of IET distributions.

Data Analysis, Statistics and Probability

Sampling networks by nodal attributes

1 code implementation13 Feb 2019 Yohsuke Murase, Hang-Hyun Jo, János Török, János Kertész, Kimmo Kaski

Assuming that the nodal attributes are independently drawn from an arbitrary distribution $\rho(h)$ and that the sampling probability $r(h_i , h_j)$ for a link $ij$ of nodal attributes $h_i$ and $h_j$ is also arbitrary, we are able to derive exact analytic expressions of the sampled network for such network characteristics as the degree distribution, degree correlation, and clustering spectrum.

Physics and Society Social and Information Networks

Waveforms of molecular oscillations reveal circadian timekeeping mechanisms

1 code implementation26 Nov 2018 Hang-Hyun Jo, Yeon Jeong Kim, Jae Kyoung Kim, Mathias Foo, David E. Somers, Pan-Jun Kim

We also find that previously-enigmatic, cyclic expression of positive arm components within the mammalian and insect clocks allows both a broad range of peak time differences between protein waveforms and the symmetries of the waveforms about the peak times.

What does Big Data tell? Sampling the social network by communication channels

1 code implementation27 Nov 2015 János Török, Yohsuke Murase, Hang-Hyun Jo, János Kertész, Kimmo Kaski

For example, while it is expected that the degree distribution of the whole social network has a maximum at a value larger than one, we get a monotonously decreasing distribution as observed in empirical studies of single channel data.

Physics and Society Social and Information Networks

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