Mining social applications network from business perspective using modularity maximization for community detection

Akbar, Zeeshan; Liu, Jun; Latif, Zahida

doi:10.1007/s13278-021-00798-0

Cited by 7 publications

(5 citation statements)

References 47 publications

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“…First, the modularity analysis which gauges the extent to which a network is compartmentalized into clusters, communities or groups (Blondel et al, 2008 ), can be used to determine the groups of words that have highly co-evolved. The modularity value ranges from 0 to 1, with high modularity networks having dense connections between words within a group but sparse connections between words in different groups (Akbar et al, 2021 ).…”

Section: Method: Analyzing the Dynamics Of Social Media Texts Via Coh...mentioning

confidence: 99%

Analyzing the dynamics of social media texts using coherency network analysis: a case study of the tweets with the co-hashtags of #BlackLivesMatter and #StopAsianHate

Jiang,

2023

Front. Res. Metr. Anal.

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IntroductionThis study examines the associations between time series, termed “coherency,” using spectral analysis. Coherence squared, analogous to the squared correlation coefficient, serves as a metric to quantify the degree of interdependence and co-evolution of individual nodes.MethodsWe utilized spectral analysis to compute coherence squared, unveiling relationships and co-evolution patterns among individual nodes. The resultant matrix of these relationships was subjected to network analysis.ResultsBy conducting a case study analyzing tweets associated with the co-hashtags #StopAsianHate and #BlackLivesMatter, we present a novel approach utilizing coherency network analysis to investigate the dynamics of social media text. Frequency domain analysis aided in calculating coherence squared, effectively illustrating the relationships and co-evolution of individual nodes. Furthermore, an analysis of the phase spectrum's slope facilitated the determination of time lag and potential causality direction between highly co-evolved node pairs.DiscussionOur findings underline the potential of coherency network analysis in comprehending the intricate dynamics of social media text. This approach offers valuable insights into how topics, sentiments, or movements manifest and evolve within the digital realm. Future research should explore diverse datasets and domains to broaden our understanding of this novel analytical technique.

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Section: Method: Analyzing the Dynamics Of Social Media Texts Via Coh...mentioning

confidence: 99%

Analyzing the dynamics of social media texts using coherency network analysis: a case study of the tweets with the co-hashtags of #BlackLivesMatter and #StopAsianHate

Jiang,

2023

Front. Res. Metr. Anal.

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“…These methods aim to detect the underlying structure in the network, identify nodes with strong connectivity, and assign them to different communities or groups. Some wellknown community discovery algorithms, such as Louvain algorithm [18], GN algorithm [19] and modular maximization [20]and so on, have been widely used in social networks, news networks and other fields to help reveal community structures and social relationships.…”

Section: Related Workmentioning

confidence: 99%

“…Graph partitioning methods consider the semantic network as a graph and use algorithms such as spectral graph partitioning [21], max-flow min-cut [22], and modularity maximization [20] to divide the network into subgraphs or partitions. This simplification aids in the analysis of intricate networks, enabling a targeted exploration of various network components to gain a deeper insight into the networkʹs inherent structure and correlations.…”

Section: Related Workmentioning

confidence: 99%

POS‐HC: A Part‐of‐Speech Hierarchical Clustering Approach for Normative Texts Partition

Li,

Liu,

Deng

et al. 2024

Preprint

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Chinese texts often feature a substantial volume of normative content, and their analysis has increasingly become a focal point in recent semantic analysis endeavors. Enhancing the effectiveness of text classification and clustering is pivotal in unveiling the intricate semantics within extensive textual data. Empirical research within the realm of computational social science has underscored the need for improved explanatory power when it comes to general text clustering methods applied to normative texts. This study introduces an innovative hierarchical clustering method that incorporates part-of-speech (POS) features and pioneers a novel semantic network model. It achieves this by amalgamating a POS-based feature weight calculation method with hierarchical clustering techniques. To assess its efficacy, the method is rigorously evaluated across three diverse text datasets, encompassing normative reports, news articles, and social media content. The experimental results unequivocally demonstrate the superior performance of the POS-HC method, surpassing the traditional TF and TF-IDF feature extraction methods in terms of clustering accuracy, and surpassing some existing clustering methods. Furthermore, its classification effectiveness exhibits notable advantages, particularly in the semantic classification of normative texts.

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“…Z Akbar et al [28] suggest that the use of modularity maximization for community detection in social networks can provide valuable insights for businesses and help them make more informed decisions.…”

Section: Related Workmentioning

confidence: 99%

PCMeans: Community Detection using LocalPagerank, Clustering, and K-Means

Louafi

Titouna

2023

Preprint

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With the rise of social networks, the task of community detection in networks has become increasingly difficult in recent years. In this study, weintroduce a novel approach, named PCMeans, which combines PageRank, hierarchical clustering, and K-means algorithms to tackle this issue.Our technique employs local PageRank to identify the most influential nodes, followed by a hierarchical clustering strategy that determinesthe optimal number of clusters, and ultimately applies K-means learning to swiftly converge to the final community structure. PCMeansis an unsupervised method that is easy to implement, efficient, andsimple, and it addresses three common problems, including the random selection of the initial central node, specification of the number ofclasses K, and slow convergence. Our experimental findings show thatPCMeans can efficiently identify communities and outperforms otherrecent approaches on both real networks and synthetic benchmarks.

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Mining social applications network from business perspective using modularity maximization for community detection

Cited by 7 publications

References 47 publications

Analyzing the dynamics of social media texts using coherency network analysis: a case study of the tweets with the co-hashtags of #BlackLivesMatter and #StopAsianHate

Analyzing the dynamics of social media texts using coherency network analysis: a case study of the tweets with the co-hashtags of #BlackLivesMatter and #StopAsianHate

POS‐HC: A Part‐of‐Speech Hierarchical Clustering Approach for Normative Texts Partition

PCMeans: Community Detection using LocalPagerank, Clustering, and K-Means

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