A Multi-Attributed Graph-Based Approach for Text Data Modeling and Event Detection in Twitter

Abulaish, Muhammad; Sharma, Sielvie; Fazil, Mohd

doi:10.1109/comsnets.2019.8711451

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…Due to its superior performance in recognizing events compared with other graph clustering methods, MCL has recently attracted the interest of many investigators from ED field [28], [30], [33], [34], [49]. MCL ranks the events using majority voting technique based on different meta-data, which may work well for some kind of data but not all.…”

Section: Related Workmentioning

confidence: 99%

“…However, both methods can struggle with pattern detection due to their reliance on the Markov Clustering (MCL) algorithm [27]- [29]. MCL's convergence speed is influenced by inflation (inf) and pruning (p) parameters [30]- [32]. Several strategies have been developed, including manual tuning [28], [30], [33]- [39], error and trial tuning technique [28], [30], [32], [40]- [47], and experimental planning by analysis [38], [48]- [53], and metaheuristic algorithms [27], [29], [54]- [56] have been used to set these parameters, they can be timeconsuming and dataset specific [57]- [60].…”

Section: Introductionmentioning

confidence: 99%

“…MCL's convergence speed is influenced by inflation (inf) and pruning (p) parameters [30]- [32]. Several strategies have been developed, including manual tuning [28], [30], [33]- [39], error and trial tuning technique [28], [30], [32], [40]- [47], and experimental planning by analysis [38], [48]- [53], and metaheuristic algorithms [27], [29], [54]- [56] have been used to set these parameters, they can be timeconsuming and dataset specific [57]- [60]. For such reasons, a novel ABBA-Adaptive is introduced in this research, which automatically adjust inf and p parameters.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Markov Clustering Algorithm for Optimal Event Detection From Heterogeneous News Documents

Al-Dyani

2024

IEEE Access

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Several Event Detection (ED) applications utilize various wrapper Feature Selection (FS) techniques based on wrapping the Markov Clustering Algorithm (MCL) with the Binary Bat Algorithm (BBA) or Adaptive Bat Algorithm (ABBA). These approaches have shown promising results in identifying relevant feature subset for MCL, leading to more precise event cluster from heterogeneous news articles. However, such wrapped FS methods involve coupling two methods (FS and ED) within ED model, with their performance influencing each other. While ABBA improved upon BBA's limitations, MCL's rapid convergence can hinder detection effectiveness. This fast convergence can lead to local optima and the detection of meaningless clusters. Additionally, MCL's identification ability diminishes as the feature space grows. To address these issues, this paper develops two novel adaptive techniques to control MCL's inflation (inf) and pruning (p) parameters, thereby managing its convergence behavior. Consequently, a new variant called Adaptive MCL (AMCL) is introduced and combined with ABBA. The effectiveness of the ABBA-AMCL method is evaluated using 10 benchmark datasets and two substantial Facebook news datasets. Various performance measures are employed to compare ABBA-AMCL against established methods. The empirical results demonstrate that ABBA-AMCL excels at extracting high-quality, real-world event clusters from various news text sources.INDEX TERMS Adaptive BBA, Markov Clustering (MCL), Event Detection (ED) methods, wrapper methods, heterogeneous news, adaptive techniques.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Markov Clustering Algorithm for Optimal Event Detection From Heterogeneous News Documents

Al-Dyani

2024

IEEE Access

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“…In this article, source data in different formats were converted into a graph and then different queries were performed on the graph model. Abulaish et al [1] proposed a graph-based approach for modelling tweets in which relationships among tweets were created based on the correlation among them. The network was utilised for clustering tweets for event detection.…”

Section: Graph Model-based Frameworkmentioning

confidence: 99%

“…It consists of three different types of data: (i) transactional data, (ii) product ontology, and (iii) customer social networks. We have taken transactional data from an online repository 1 . The transactional data is represented in the form of a relational model consisting of four relations: Fig.…”

Section: An Illustrationmentioning

confidence: 99%

A Unification of Heterogeneous Data Sources into a Graph Model in E-commerce

Tuteja

Kumar

2021

Data Sci. Eng.

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The incorporation of heterogeneous data models into large-scale e-commerce applications incurs various complexities and overheads, such as redundancy of data, maintenance of different data models, and communication among different models for query processing. Graphs have emerged as data modelling techniques for large-scale applications with heterogeneous, schemaless, and relationship-centric data. Models exist for mapping different types of data to a graph; however, the unification of data from heterogeneous source models into a graph model has not received much attention. To address this, we propose a new framework in this study. The proposed framework first transforms data from various source models into graph models individually and then unifies them into a single graph. To justify the applicability of the proposed framework in e-commerce applications, we analyse and compare query performance, scalability, and database size of the unified graph with heterogeneous source data models for a predefined set of queries. We also access some qualitative measures, such as flexibility, completeness, consistency, and maturity for the proposed unified graph. Based on the experimental results, the unified graph outperforms heterogeneous source models for query performance and scalability; however, it falls behind for database size.

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Query-driven graph models in e-commerce

Tuteja

Kumar

2022

Innovations Syst Softw Eng

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A Multi-Attributed Graph-Based Approach for Text Data Modeling and Event Detection in Twitter

Cited by 12 publications

References 8 publications

Adaptive Markov Clustering Algorithm for Optimal Event Detection From Heterogeneous News Documents

Adaptive Markov Clustering Algorithm for Optimal Event Detection From Heterogeneous News Documents

A Unification of Heterogeneous Data Sources into a Graph Model in E-commerce

Query-driven graph models in e-commerce

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