In this paper, we analyze the character networks extracted from three popular
television series and explore the relationship between a TV show episode's
character network metrics and its review from IMDB. Character networks are
graphs created from the plot of a TV show that represents the interactions of
characters in scenes, indicating the presence of a connection between them. We
calculate various network metrics for each episode, such as node degree and
graph density, and use these metrics to explore the potential relationship
between network metrics and TV series reviews from IMDB. Our results show that
certain network metrics of character interactions in episodes have a strong
correlation with the review score of TV series. Our research aims to provide
more quantitative information that can help TV producers understand how to
adjust the character dynamics of future episodes to appeal to their audience.
By understanding the impact of character interactions on audience engagement
and enjoyment, producers can make informed decisions about the development of
their shows.

本文通过分析三个流行电视剧中提取的角色网络并探索 TV show 在 IMDB 评价中的关系，计算每集的各种网络指标，结果表明角色交互的某些网络指标与电视系列的评论得分有很强的相关性，以此为依据可以帮助 TV producer 更好地了解如何调整未来集数的角色动态来吸引观众以及提升娱乐体验。

电视剧流行度的网络分析视角解读

Decoding the Popularity of TV Series: A Network Analysis Perspective

Time series and signals are attracting more attention across statistics,
machine learning and pattern recognition as it appears widely in the industry
especially in sensor and IoT related research and applications, but few
advances has been achieved in effective time series visual analytics and
interaction due to its temporal dimensionality and complex dynamics. Inspired
by recent effort on using network metrics to characterize time series for
classification, we present an approach to visualize time series as complex
networks based on the first order Markov process in its temporal ordering. In
contrast to the classical bar charts, line plots and other statistics based
graph, our approach delivers more intuitive visualization that better preserves
both the temporal dependency and frequency structures. It provides a natural
inverse operation to map the graph back to raw signals, making it possible to
use graph statistics to characterize time series for better visual exploration
and statistical analysis. Our experimental results suggest the effectiveness on
various tasks such as pattern discovery and classification on both synthetic
and the real time series and sensor data.

利用一阶马尔可夫过程基于复杂网络来可视化时间序列，以更好地保留时间依赖性和频率结构，并提供更好的可视化探索和统计分析。