Consumer perceptions of fresh leafy vegetables in Japan

2018 ◽  
Vol 120 (11) ◽  
pp. 2554-2568
Author(s):  
Yuki Yano ◽  
David Blandford ◽  
Atsushi Maruyama ◽  
Tetsuya Nakamura
Keyword(s):  
Community Detection ◽  
Detection Algorithm ◽  
Consumer Perceptions ◽  
Leafy Vegetables ◽  
Centrality Measures ◽  
Bulletin Board ◽  
Content Type ◽  
Consumer Responses ◽  
Online Bulletin Board ◽  
Community Detection Algorithm

Purpose The purpose of this paper is to investigate Japanese consumer perceptions of the benefits of consuming fresh leafy vegetables. Design/methodology/approach An online bulletin board survey was conducted in Japan to collect responses to an open-ended question about reasons for consuming fresh leafy vegetables. A total of 897 responses were analysed using word co-occurrence network analysis. A community detection method and centrality measures were used to interpret the resulting network map. Findings Using a community detection algorithm, the authors identify six major groups of words that represent respondents’ core motives for consuming leafy vegetables. While Japanese consumers view health benefits to be most important, sensory factors, such as texture, colour, and palatability, and convenience factors also influence attitudes. The authors find that centrality measures can be useful in identifying keywords that appear in various contexts of consumer responses. Originality/value This is the first paper to use a quantitative text analysis to examine consumer perceptions for fresh leafy vegetables. The analysis also provides pointers for creating visually interpretable co-occurrence network maps from textual data and discusses the role of community structure and centrality in interpreting such maps.

Characterization of Online Social Network

2019 ◽  
pp. 272-299 ◽  
Author(s):  
Paramita Dey
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Online Social Network ◽  
Social Network Sites ◽  
Detection Algorithm ◽  
Centrality Measures ◽  
Large Network ◽  
Twitter Account ◽  
Community Detection Algorithm

The rapid growth of internet with large number of social network sites makes it easy to interconnect people from all over the world on a shared platform. Social network can be represented by a graph, where individual users are represented as nodes/vertices and connections between them are represented as edges of the graph. As social network inherits the properties of graph, its characterization includes centrality and community detection. In this chapter we discuss three centrality measures and its effects for information propagation. We discuss three popular hierarchical community detection measures and make a comparative analysis of them. Moreover we propose a new ego-based community detection algorithm which can be very efficient in terms of time complexity for very large network like online social network. In this chapter, a network is formed based on the data collected from Twitter account using hashtag(#).

Centrality and community detection: a co-marketing multilayer network

2019 ◽  
Vol 34 (8) ◽  
pp. 1749-1762
Author(s):  
Andreia Fernandes ◽  
Patrícia C.T. Gonçalves ◽  
Pedro Campos ◽  
Catarina Delgado
Keyword(s):  
Community Detection ◽  
Design Methodology ◽  
Detection Algorithm ◽  
Marketing Strategies ◽  
Consumption Patterns ◽  
Multilayer Network ◽  
Business Decisions ◽  
Content Type ◽  
Socioeconomic Profile ◽  
Community Detection Algorithm

Purpose Based on the data obtained from a questionnaire of 595 people, the authors explore the relative importance of consumers, checking whether socioeconomic variables influence their centrality, detecting the communities within the network to which they belong, identifying consumption patterns and checking whether there is any relationship between co-marketing and consumer choices. Design/methodology/approach A multilayer network is created from data collected through a consumer survey to identify customers’ choices in seven different markets. The authors focus the analysis on a smaller kinship and cohabitation network and apply the LART network community detection algorithm. To verify the association between consumers’ centrality and variables related to their respective socioeconomic profile, the authors develop an econometric model to measure their impact on consumer’s degree centrality. Findings Based on 595 responses analysing individual consumers, the authors find out which consumers invest and which variables influence consumers’ centrality. Using a smaller sample of 70 consumers for whom they know kinship and cohabitation relationships, the authors detect communities with the same consumption patterns and verify that this may be an adequate way to establish co-marketing strategies. Originality/value Network analysis has become a widely used technique in the extraction of knowledge on consumers. This paper’s main (and novel) contribution lies in providing a greater understanding on how multilayer networks represent hidden databases with potential knowledge to be considered in business decisions. Centrality and community detection are crucial measures in network science which enable customers with the highest potential value to be identified in a network. Customers are increasingly seen as multidimensional, considering their preferences in various markets.

scholarly journals Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 680
Author(s):  
Hanyang Lin ◽  
Yongzhao Zhan ◽  
Zizheng Zhao ◽  
Yuzhong Chen ◽  
Chen Dong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Adjustment Strategy ◽  
Topology Information ◽  
Overlapping Community ◽  
Real World Datasets ◽  
Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

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