• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2587-2605
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• 2022

In order to efficiently detect community structure in complex networks, community detection algorithms can be designed from the perspective of node similarity. However, the appropriate parameters should be chosen to achieve community division, furthermore, these existing algorithms based on the similarity of common neighbors have low discrimination between node pairs. To solve the above problems, a noval community detection algorithm using closeness similarity based on common neighbor node clustering entropy is proposed, shorted as CSCDA. Firstly, to improve detection accuracy, common neighbors and clustering coefficient are combined in the form of entropy, then a new closeness similarity measure is proposed. Through the designed similarity measure, the closeness similar node set of each node can be further accurately identified. Secondly, to reduce the randomness of the community detection result, based on the closeness similar node set, the node leadership is used to determine the most closeness similar first-order neighbor node for merging to create the initial communities. Thirdly, for the difficult problem of parameter selection in existing algorithms, the merging of two levels is used to iteratively detect the final communities with the idea of modularity optimization. Finally, experiments show that the normalized mutual information values are increased by an average of 8.06% and 5.94% on two scales of synthetic networks and real-world networks with real communities, and modularity is increased by an average of 0.80% on the real-world networks without real communities.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1055-1067
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• 2019

In view of the deficiencies of existing weighted similarity indexes, a hierarchical clustering method initialize-expand-merge (IEM) is proposed based on the similarity of common neighbors for community discovery in weighted networks. Firstly, the similarity of the node pair is defined based on the attributes of their common neighbors. Secondly, the most closely related nodes are fast clustered according to their similarity to form initial communities and expand the communities. Finally, communities are merged through maximizing the modularity so as to optimize division results. Experiments are carried out on many weighted networks, which have verified the effectiveness of the proposed algorithm. And results show that IEM is superior to weighted common neighbor (CN), weighted Adamic-Adar (AA) and weighted resources allocation (RA) when using the weighted modularity as evaluation index. Moreover, the proposed algorithm can achieve more reasonable community division for weighted networks compared with cluster-recluster-merge-algorithm (CRMA) algorithm.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.3
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• pp.35-51
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• 2014

A phytosociological vegetation survey was conducted in July to September 2011 in order to examine the vegetation community structure in Mt. Ohseo area. It was aimed to provide basic data for the effective vegetation conservation by analyzing the importance, species diversity and community similarity of the forest community in Mt. Ohseo for each layer, followed by the classification of the actual forest vegetation. According to the cluster analysis, the community type of Mt. Ohseo was classified into a total of 4 vegetation communities: Pinus densiflora community, Cornus controversa-Quercus serrata community, Miscanthus sinensis community, and Quercus mongolica community; the vegetation type 4 showed the lowest species diversity index of 0.5236, and vegetation type-2 showed the highest species diversity index of 0.6606. The community similarity between Quercus mongolica community and Pinus densiflora community showed the highest 0.679, and the community similarity between Quercus serrata community and Pinus densiflora community and between Quercus serrata community and Quercus mongolica community showed the levels of 0.5, respectively.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.100-104
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• 2006

Fuzzy techniques can be applied in many domains of computer vision community. The definition of an adequate similarity measure for measuring the similarity between fuzzy sets is of great importance in the field of image processing, image retrieval and pattern recognition. This paper proposes a new class of the similarity measures. The properties, sensitivity and effectiveness of the proposed measures are investigated and tested on real data. Experimental results show that these similarity measures can provide a useful way for measuring the similarity between fuzzy sets.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.55-66
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• 2023

Community portraits can deeply explore the characteristics of community structures and describe the personalized knowledge needs of community users, which is of great practical significance for improving community recommendation services, as well as the accuracy of resource push. The current community portraits generally have the problems of weak perception of interest characteristics and low degree of integration of topic information. To resolve this problem, the reader community portrait method based on the thematic and timeliness characteristics of interest labels (UIT) is proposed. First, community opinion leaders are identified based on multi-feature calculations, and then the topic features of their texts are identified based on the LDA topic model. On this basis, a semantic mapping including "reader community-opinion leader-text content" was established. Second, the readers' interest similarity of the labels was dynamically updated, and two kinds of tag parameters were integrated, namely, the intensity of interest labels and the stability of interest labels. Finally, the similarity distance between the opinion leader and the topic of interest was calculated to obtain the dynamic interest set of the opinion leaders. Experimental analysis was conducted on real data from the Douban reading community. The experimental results show that the UIT has the highest average F value (0.551) compared to the state-of-the-art approaches, which indicates that the UIT has better performance in the smooth time dimension.

• Knowledge Management Research
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• v.10 no.2
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• pp.105-119
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• 2009

This study addresses the following questions : how does organization citizenship behavior(OCB) affect knowledge contribution in online communities? does the antecedents of OCB, cohesiveness and affection similarity, influence knowledge contribution in online communities? In order to test our hypotheses with an empirical study, we have conducted a survey which resulted in 192 valid response in the final sample. The PLS analysis results indicate that OCB affects knowledge contribution and coherence and affection similarity of online community users have influence on OCB. Further, knowledge contribution is influenced by community users' affection similarity. Practical implications of these findings and future research implications are also discussed.

• Korean Journal of Ecology and Environment
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• v.54 no.4
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• pp.291-302
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• 2021

The phytoplankton community in the estuarine system is affected by changes of physicochemical factors easily. The present study analyzed phytoplankton community distribution and similarity, in addition to exploring factors influencing variations in phytoplankton community structure in three lakes located in the Yeongsan River estuary from March 2014 to November 2017. We carried out non-multidimensional scaling (NMDS) and random forest analysis (RF) for comparing the pattern of phytoplankton distribution and the relationship between phytoplankton distribution and environmental variables. Similarity Percentage (SIMPER) and Analysis of Similarity (ANOSIM) were performed to figure out the similarity of phytoplankton community at each site of three lakes. From NMDS, Phytoplankton community distribution differed between Yeongsan and Gumho lakes, and the factors influencing the distribution of phytoplankton communities across the three lakes were water temperature, dissolved oxygen, total nitrogen (T-N), nitrate-N (NO₃-N), and conductivity. NO₃-N was a key factor influencing phytoplankton community structure in the three lakes based on RF. A total of 24 species were identified as indicator species in the three lakes studied, with the highest species numbers observed in Yeongsan Lake (13) and the lowest observed in Yeongam Lake (2). According to SIMPER and ANOSIM results, the phytoplankton community in Yeongsan and Yeongam lakes were similar, and they differed from those in Gumho Lake. In addition, the phytoplankton community structure varied across the study sites in the three lakes, indicating that water channels across the lakes a minor influence phytoplankton community distribution.

• Journal of Environmental Science International
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• v.20 no.1
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• pp.127-135
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• 2011

This study was carried out in each three study areas of Pinus densiflora community and Quercus mongolica community from March 5th, 2008 to October 15th, 2010 to analyze the relationship between seed bank and the actual vegetation of the lower layer. Based on the relationship between the lower layer of actual vegetation and the germination of seed bank, all of three study areas, the similarity of the actual vegetation of the lower layer and seed bank were high in Plot 1 (84.62%) and Plot 3 (89.91%). As for Quercus mongolica community, the similarity was high between the actual vegetation of the lower layer and seed bank in Plot 4 (82.24%) and Plot 6 (89.47%). Especially, the germination of the pine seed banks in the Pinus densiflora community compared to other tree species appeared in all. In Quercus mongolica community, Quercus mongolica did not appear among the seeds germinated in the seek bank, but the other tree species constituting the under layer of the community. In case of the restoration based on the actual vegetation, it is desirable to sue the lower layer of vegetation as the model for the making of its alternatives for restoration works of the species.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.432-436
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• 2010

Finding communities from network data including social networks can be done by clustering the nodes of the network as densely interconnected groups, where keeping interconnection between groups sparse. To exploit a clustering algorithm for community detection task, we need a well-defined similarity measure between network nodes. In this paper, we propose a new similarity measure named "Common Neighborhood Sub-graph density" and combine the similarity with affinity propagation, which is a recently devised clustering algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.366-369
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• 2022

The glut of information aggravated the process of data analysis and other procedures including data mining. Many algorithms were devised in Big Data and Data Mining to solve such an intricate problem. In this paper, we conducted research about the comparison of several similarity measures and community detection algorithms in collaborative filtering for movie recommendation systems. Movielense data set was used to do an empirical experiment. We applied three different similarity measures: Cosine, Euclidean, and Pearson. Moreover, betweenness and eigenvector centrality were used to detect communities from the network. As a result, we elucidated which algorithm is more suitable than its counterpart in terms of recommendation accuracy.