15 research outputs found
Scaling Laws in Human Language
Zipf's law on word frequency is observed in English, French, Spanish,
Italian, and so on, yet it does not hold for Chinese, Japanese or Korean
characters. A model for writing process is proposed to explain the above
difference, which takes into account the effects of finite vocabulary size.
Experiments, simulations and analytical solution agree well with each other.
The results show that the frequency distribution follows a power law with
exponent being equal to 1, at which the corresponding Zipf's exponent diverges.
Actually, the distribution obeys exponential form in the Zipf's plot. Deviating
from the Heaps' law, the number of distinct words grows with the text length in
three stages: It grows linearly in the beginning, then turns to a logarithmical
form, and eventually saturates. This work refines previous understanding about
Zipf's law and Heaps' law in language systems.Comment: 6 pages, 4 figure
On the Complex Network Structure of Musical Pieces: Analysis of Some Use Cases from Different Music Genres
This paper focuses on the modeling of musical melodies as networks. Notes of
a melody can be treated as nodes of a network. Connections are created whenever
notes are played in sequence. We analyze some main tracks coming from different
music genres, with melodies played using different musical instruments. We find
out that the considered networks are, in general, scale free networks and
exhibit the small world property. We measure the main metrics and assess
whether these networks can be considered as formed by sub-communities. Outcomes
confirm that peculiar features of the tracks can be extracted from this
analysis methodology. This approach can have an impact in several multimedia
applications such as music didactics, multimedia entertainment, and digital
music generation.Comment: accepted to Multimedia Tools and Applications, Springe
Tracking online topics over time: understanding dynamic hashtag communities
Abstract Background Hashtags are widely used for communication in online media. As a condensed version of information, they characterize topics and discussions. For their analysis, we apply methods from network science and propose novel tools for tracing their dynamics in time-dependent data. The observations are characterized by bursty behaviors in the increases and decreases of hashtag usage. These features can be reproduced with a novel model of dynamic rankings. Hashtag communities in time We build temporal and weighted co-occurrence networks from hashtags. On static snapshots, we infer the community structure using customized methods. On temporal networks, we solve the bipartite matching problem of detected communities at subsequent timesteps by taking into account higher-order memory. This results in a matching protocol that is robust toward temporal fluctuations and instabilities of the static community detection. The proposed methodology is broadly applicable and its outcomes reveal the temporal behavior of online topics. Modeling topic-dynamics We consider the size of the communities in time as a proxy for online popularity dynamics. We find that the distributions of gains and losses, as well as the interevent times are fat-tailed indicating occasional, but large and sudden changes in the usage of hashtags. Inspired by typical website designs, we propose a stochastic model that incorporates a ranking with respect to a time-dependent prestige score. This causes occasional cascades of rank shift events and reproduces the observations with good agreement. This offers an explanation for the observed dynamics, based on characteristic elements of online media