scholarly journals Culture under Complex Perspective: A Classification for Traditional Chinese Cultural Elements Based on NLP and Complex Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lin Qi ◽  
Yuwei Wang ◽  
Jindong Chen ◽  
Mengjie Liao ◽  
Jian Zhang
Keyword(s):  
Complex Networks ◽  
Song Dynasty ◽  
Chinese Culture ◽  
Detection Algorithm ◽  
Primary Data ◽  
Cultural Elements ◽  
Scale Free ◽  
Northern Song ◽  
Reliable Classification ◽  
Community Detection Algorithm

The cultural element is the minimum unit of a cultural system. The systematic categorizing, organizing, and retrieval of the traditional Chinese cultural elements are essential prerequisites for the realization of effective extracting and rational utilization, as well as the prerequisite for exploiting the contemporary value of the traditional Chinese culture. To build an objective, integrated, and reliable classification method and a system of traditional Chinese cultural elements, this study takes the text of Taiping Imperial Encyclopedia in Northern Song Dynasty as the primary data source. The unsupervised word segmentation methods are used to detect Out-of-Vocabulary (OOV), and then the segmentation results by the THULAC tool with and without custom dictionary are compared. The TF-IDF algorithm is applied to extract the keywords of cultural elements and the Ochiia coefficient is introduced to create complex networks of traditional Chinese cultural elements. After analyzing the topological characteristics of the network, the community detection algorithm is used to identify the topics of cultural elements. Finally, a “Means-Ends” two-dimensional orthogonal classification system is established to categorize the topics. The results showed that the degree distribution in the complex network of Chinese traditional cultural elements is a scale-free network with γ = 2.28. The network shows a structure of community and hierarchy features. The top 12 communities have taken up to 91.77% of the scale of the networks. Those 12 topics of the traditional Chinese cultural elements are circularly distributed in the orthogonal system of cultural elements’ categorization.

scholarly journals Complex networks, streamflow, and hydrometric monitoring system design

2014 ◽  
Vol 11 (12) ◽  
pp. 13663-13710 ◽  
Author(s):  
M. Halverson ◽  
S. Fleming
Keyword(s):  
Small World ◽  
Detection Algorithm ◽  
Clustering Coefficient ◽  
Network Stability ◽  
Meteorological Forcing ◽  
Coast Mountains ◽  
Scale Free ◽  
Seasonal Flow ◽  
High Betweenness ◽  
Community Detection Algorithm

Abstract. Network theory is applied to an array of streamflow gauges located in the Coast Mountains of British Columbia and Yukon, Canada. The goal of the analysis is to assess whether insights from this branch of mathematical graph theory can be meaningfully applied to hydrometric data, and more specifically, whether it may help guide decisions concerning stream gauge placement so that the full complexity of the regional hydrology is efficiently captured. The streamflow data, when represented as a complex network, has a global clustering coefficient and average shortest path length consistent with small-world networks, which are a class of stable and efficient networks common in nature, but the results did not clearly suggest a scale-free network. Stability helps ensure that the network is robust to the loss of nodes; in the context of a streamflow network, stability is interpreted as insensitivity to station removal at random. Community structure is also evident in the streamflow network. A community detection algorithm identified 10 separate communities, each of which appears to be defined by the combination of its median seasonal flow regime (pluvial, nival, hybrid, or glacial, which in this region in turn mainly reflects basin elevation) and geographic proximity to other communities (reflecting shared or different daily meteorological forcing). Betweenness analyses additionally suggest a handful of key stations which serve as bridges between communities and might therefore be highly valued. We propose that an idealized sampling network should sample high-betweenness stations, as well as small-membership communities which are by definition rare or undersampled relative to other communities, while retaining some degree of redundancy to maintain network robustness.

A fast local community detection algorithm in complex networks

2021 ◽  
Author(s):  
Zhikang Tang ◽  
Yong Tang ◽  
Chunying Li ◽  
Jinli Cao ◽  
Guohua Chen ◽  
...  
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Local Community ◽  
Detection Algorithm ◽  
Local Community Detection ◽  
Community Detection Algorithm

Constant community discovery in network based on consensus clustering

2014 ◽  
Vol 28 (19) ◽  
pp. 1450126
Author(s):  
Zongwen Liang ◽  
Athina Petropulu ◽  
Fan Yang ◽  
Jianping Li
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Detection Algorithm ◽  
Consensus Clustering ◽  
Community Discovery ◽  
Structural And Functional Properties ◽  
Consensus Matrix ◽  
Internal Properties ◽  
Community Detection Algorithm ◽  
Vertex Order

Community detection is a fundamental work to analyze the structural and functional properties of complex networks. There are many algorithms proposed to find the optimal communities of network. In this paper, we focus on how vertex order influences the results of community detection. By using consensus clustering, we discover communities and get a consensus matrix under different vertex orders. Based on the consensus matrix, we study the phenomenon that some nodes are always allocated in the same community even with different vertex permutations. We call this group of nodes as constant community and propose a constant community detection algorithm (CCDA) to find constant communities in network. We also further study the internal properties of constant communities and find constant communities play a guiding role in community detection. Finally, a discussion of constant communities is given in the hope of being useful to others working in this field.

Sign in / Sign up

Export Citation Format

Share Document