scholarly journals Visualization and analysis of SCImago Journal & Country Rank structure via journal clustering

2016 ◽  
Vol 68 (5) ◽  
pp. 607-627 ◽  
Author(s):  
Antonio J. Gómez-Núñez ◽  
Benjamin Vargas-Quesada ◽  
Zaida Chinchilla-Rodríguez ◽  
Vladimir Batagelj ◽  
Félix Moya-Anegón
Keyword(s):  
Clustering Algorithm ◽  
Citation Network ◽  
Detection Algorithm ◽  
Bibliographic Coupling ◽  
Content Type ◽  
Alternative Approach ◽  
Level Of Aggregation ◽  
Direct Citation ◽  
Community Detection Algorithm ◽  
Visualization Techniques

Purpose The purpose of this paper is to visualize the structure of SCImago Journal & Country Rank (SJR) coverage of the extensive citation network of Scopus journals, examining this bibliometric portal through an alternative approach, applying clustering and visualization techniques to a combination of citation-based links. Design/methodology/approach Three SJR journal-journal networks containing direct citation, co-citation and bibliographic coupling links are built. The three networks were then combined into a new one by summing up their values, which were later normalized through geo-normalization measure. Finally, the VOS clustering algorithm was executed and the journal clusters obtained were labeled using original SJR category tags and significant words from journal titles. Findings The resultant scientogram displays the SJR structure through a set of communities equivalent to SJR categories that represent the subject contents of the journals they cover. A higher level of aggregation by areas provides a broad view of the SJR structure, facilitating its analysis and visualization at the same time. Originality/value This is the first study using Persson’s combination of most popular citation-based links (direct citation, co-citation and bibliographic coupling) in order to develop a scientogram based on Scopus journals from SJR. The integration of the three measures along with performance of the VOS community detection algorithm gave a balanced set of clusters. The resulting scientogram is useful for assessing and validating previous classifications as well as for information retrieval and domain analysis.

Analysis of direct citation, co-citation and bibliographic coupling in scientific topic identification

2020 ◽  
pp. 016555152096277
Author(s):  
Rajmund Kleminski ◽  
Przemysiaw Kazienko ◽  
Tomasz Kajdanowicz
Keyword(s):  
Citation Network ◽  
Scientific Paper ◽  
Added Value ◽  
Bibliographic Coupling ◽  
Science Literature ◽  
Citation Networks ◽  
Research Topics ◽  
Topic Identification ◽  
Direct Citation ◽  
The Impact

In our study, we examine the impact of citation network structures on the ability to discern valuable research topics in Computer Science literature. We use the bibliographic information available in the DBLP database to extract candidate phrases from scientific paper abstracts. Following that, we construct citation networks based on direct citation, co-citation and bibliographic coupling relationships between the papers. The candidate research topics, in the form of keyphrases and n-grammes, are subsequently ranked and filtered by a graph-text ranking algorithm. This selection of the highest ranked potential topics is further evaluated by domain experts and through the Wikipedia knowledge base. The results obtained from these citation networks are complementary, returning valid but non-overlapping output phrases between some pairs of networks. In particular, bibliographic coupling appears to capture more unique information than either direct citation or co-citation. These findings point towards the possible added value in combining bibliographic coupling analysis with other structures. At the same time, combining direct citation and co-citation is put into question. We expect our findings to be utilised in method design for research topic identification.

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.

scholarly journals An Improved Spectral Clustering Community Detection Algorithm Based on Probability Matrix

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Shuxia Ren ◽  
Shubo Zhang ◽  
Tao Wu
Keyword(s):  
Community Detection ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Transition Probability ◽  
Clustering Algorithms ◽  
Detection Algorithm ◽  
Community Information ◽  
The Mean ◽  
Community Detection Algorithm ◽  
Spectral Clustering Algorithm

The similarity graphs of most spectral clustering algorithms carry lots of wrong community information. In this paper, we propose a probability matrix and a novel improved spectral clustering algorithm based on the probability matrix for community detection. First, the Markov chain is used to calculate the transition probability between nodes, and the probability matrix is constructed by the transition probability. Then, the similarity graph is constructed with the mean probability matrix. Finally, community detection is achieved by optimizing the NCut objective function. The proposed algorithm is compared with SC, WT, FG, FluidC, and SCRW on artificial networks and real networks. Experimental results show that the proposed algorithm can detect communities more accurately and has better clustering performance.

Clustering Algorithm for Community Detection in Complex Network: A Comprehensive Review

2020 ◽  
Vol 13 (4) ◽  
pp. 542-549
Author(s):  
Smita Agrawal ◽  
Atul Patel
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Detection Algorithm ◽  
Connected Subgraph ◽  
Comprehensive Review ◽  
Detection Algorithms ◽  
Community Detection Algorithm ◽  
Large Scale Networks

Many real-world social networks exist in the form of a complex network, which includes very large scale networks with structured or unstructured data and a set of graphs. This complex network is available in the form of brain graph, protein structure, food web, transportation system, World Wide Web, and these networks are sparsely connected, and most of the subgraphs are densely connected. Due to the scaling of large scale graphs, efficient way for graph generation, complexity, the dynamic nature of graphs, and community detection are challenging tasks. From large scale graph to find the densely connected subgraph from the complex network, various community detection algorithms using clustering techniques are discussed here. In this paper, we discussed the taxonomy of various community detection algorithms like Structural Clustering Algorithm for Networks (SCAN), Structural-Attribute based Cluster (SA-cluster), Community Detection based on Hierarchical Clustering (CDHC), etc. In this comprehensive review, we provide a classification of community detection algorithm based on their approach, dataset used for the existing algorithm for experimental study and measure to evaluate them. In the end, insights into the future scope and research opportunities for community detection are discussed.

scholarly journals Community Detection Algorithm in Social Networks through Iterative Analysis based on Degree of Nodes

2019 ◽  
Vol 9 (1) ◽  
pp. 1892-1900
Keyword(s):  
Social Networks ◽  
Rural Communities ◽  
Clustering Algorithm ◽  
Fundamental Problem ◽  
Scoring Function ◽  
Detection Algorithm ◽  
Fitness Value ◽  
Head Node ◽  
Community Identification ◽  
Community Detection Algorithm

Social networking is the grouping of individuals into specific groups, like small rural communities or a neighborhood subdivision. A fundamental problem in the analysis of social networks is the tracking of communities. A community is often defined as a group of network members with stronger ties to members within the group than to members outside the group. The traditional method for identifying communities in networks is hierarchical clustering. Recently, several works have been done in this community identification using different type of clustering algorithm and connectivity-based scoring function. In this paper Random Head Node Technique and Highest Degree Head Node Techniques are proposed to group the nodes into communities. In these techniques best set of centroids are chosen based on the fitness value to cluster the nodes into communities

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.

An overlapping community detection algorithm based on node distance of line graph

2019 ◽  
Vol 33 (26) ◽  
pp. 1950322 ◽  
Author(s):  
Guishen Wang ◽  
Yuanwei Wang ◽  
Kaitai Wang ◽  
Zhihua Liu ◽  
Lijuan Zhang ◽  
...  
Keyword(s):  
Community Detection ◽  
Clustering Algorithm ◽  
Line Graph ◽  
Detection Algorithm ◽  
Density Peak ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Normalized Mutual Information ◽  
Overlapping Community ◽  
Community Detection Algorithm

Overlapping community detection is a hot topic in research of complex networks. Link community detection is a popular approach to discover overlapping communities. Line graph is a widely used model in link community detection. In this paper, we propose an overlapping community detection algorithm based on node distance of line graph. Considering topological structure of links in graphs, we use line graph to transform links of graph into nodes of line graph. Then, we calculate node distance of line graph according to their dissimilarity. After getting distance matrix, we proposed a new [Formula: see text] measure based on nodes of line graph and combine it with clustering algorithm by fast search and density peak to identify node communities of line graph. Finally, we acquire overlapping node communities after transforming node communities of line graph back to graph. The experimental results show that our algorithm achieves a higher performance on normalized mutual information metric.

scholarly journals Article-level classification of scientific publications: A comparison of deep learning, direct citation and bibliographic coupling

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251493
Author(s):  
Maxime Rivest ◽  
Etienne Vignola-Gagné ◽  
Éric Archambault
Keyword(s):  
Deep Learning ◽  
Research Evaluation ◽  
Innovation Policy ◽  
Citation Network ◽  
Scientific Activity ◽  
Label Propagation ◽  
Bibliographic Coupling ◽  
Learning Approaches ◽  
Scientific Publications ◽  
Direct Citation

Classification schemes for scientific activity and publications underpin a large swath of research evaluation practices at the organizational, governmental, and national levels. Several research classifications are currently in use, and they require continuous work as new classification techniques becomes available and as new research topics emerge. Convolutional neural networks, a subset of “deep learning” approaches, have recently offered novel and highly performant methods for classifying voluminous corpora of text. This article benchmarks a deep learning classification technique on more than 40 million scientific articles and on tens of thousands of scholarly journals. The comparison is performed against bibliographic coupling-, direct citation-, and manual-based classifications—the established and most widely used approaches in the field of bibliometrics, and by extension, in many science and innovation policy activities such as grant competition management. The results reveal that the performance of this first iteration of a deep learning approach is equivalent to the graph-based bibliometric approaches. All methods presented are also on par with manual classification. Somewhat surprisingly, no machine learning approaches were found to clearly outperform the simple label propagation approach that is direct citation. In conclusion, deep learning is promising because it performed just as well as the other approaches but has more flexibility to be further improved. For example, a deep neural network incorporating information from the citation network is likely to hold the key to an even better classification algorithm.

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