Optimal initial partitionning for high quality hybrid hierarchical community detection in social networks

Toujani, Radhia; Akaichi, Jalel

doi:10.1109/codit.2017.8102624

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…In fact, the introduced mixed hierarchical citizens clustering provided low energy function of the elements of SNOW Data Challenge, indicating good clustering quality. This high clustering performance can result from the fact that the detected citizens communities were very dependent on the initial citizens community structure relying on the detection of the optimal initial structure (Toujani and Akaichi, 2017). Besides, because the introduced mixed process allowed adjusting the number of clusters, the hierarchical classification process was more flexible for noise and burst detection.…”

Section: Discussionmentioning

confidence: 99%

“…It does not change the number of partitions, but it modifies the initial distribution. In our case, the generation of the initial community structure was obtained using the method suggested in Toujani and Akaichi (2017), which considers the initial partition as a combinatorial optimization issue and solves it by employing Tabu Search metaheuristic. Therefore, the initial generated citizens’ partition was injected as input to MCCM .…”

Section: Proposed Approachmentioning

confidence: 99%

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Event news detection and citizens community structure for disaster management in social networks

Toujani

Akaichi

2019

OIR

Self Cite

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Purpose Nowadays, the event detection is so important in gathering news from social media. Indeed, it is widely employed by journalists to generate early alerts of reported stories. In order to incorporate available data on social media into a news story, journalists must manually process, compile and verify the news content within a very short time span. Despite its utility and importance, this process is time-consuming and labor-intensive for media organizations. Because of the afore-mentioned reason and as social media provides an essential source of data used as a support for professional journalists, the purpose of this paper is to propose the citizen clustering technique which allows the community of journalists and media professionals to document news during crises. Design/methodology/approach The authors develop, in this study, an approach for natural hazard events news detection and danger citizen’ groups clustering based on three major steps. In the first stage, the authors present a pipeline of several natural language processing tasks: event trigger detection, applied to recuperate potential event triggers; named entity recognition, used for the detection and recognition of event participants related to the extracted event triggers; and, ultimately, a dependency analysis between all the extracted data. Analyzing the ambiguity and the vagueness of similarity of news plays a key role in event detection. This issue was ignored in traditional event detection techniques. To this end, in the second step of our approach, the authors apply fuzzy sets techniques on these extracted events to enhance the clustering quality and remove the vagueness of the extracted information. Then, the defined degree of citizens’ danger is injected as input to the introduced citizens clustering method in order to detect citizens’ communities with close disaster degrees. Findings Empirical results indicate that homogeneous and compact citizen’ clusters can be detected using the suggested event detection method. It can also be observed that event news can be analyzed efficiently using the fuzzy theory. In addition, the proposed visualization process plays a crucial role in data journalism, as it is used to analyze event news, as well as in the final presentation of detected danger citizens’ clusters. Originality/value The introduced citizens clustering method is profitable for journalists and editors to better judge the veracity of social media content, navigate the overwhelming, identify eyewitnesses and contextualize the event. The empirical analysis results illustrate the efficiency of the developed method for both real and artificial networks.

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Section: Discussionmentioning

confidence: 99%

Section: Proposed Approachmentioning

confidence: 99%

Event news detection and citizens community structure for disaster management in social networks

Toujani

Akaichi

2019

OIR

Self Cite

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“…Its running time on a network with n vertices is O(nlog 2 n). Toujani and Akaichi [13] propose a hybrid method to uncover the hierarchical community structure of complex networks. However, the method assumes the existence of an initial partition, which has significance influence to the final results.…”

Section: Related Workmentioning

confidence: 99%

“…Because of its excellent performance, agglomerative method has attracted many attentions. Typical agglomerative algorithms include: modularity optimization [13]- [16], density-based methods [17]- [21], game theory-based methods [22], [23], random walk-based methods [24]- [26], and NMF (Nonnegative Matrix Factorization)-based methods [27], [28]. These methods have achieved good performance in community detection, but there are still some defects among them.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Community Detection in Social Networks Based on Micro-Community and Minimum Spanning Tree

Wang

Hou

Yuan

et al. 2019

IEICE Trans. Inf. & Syst.

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Social networks often demonstrate hierarchical community structure with communities embedded in other ones. Most existing hierarchical community detection methods need one or more tunable parameters to control the resolution levels, and the obtained dendrograms, a tree describing the hierarchical community structure, are extremely complex to understand and analyze. In the paper, we propose a parameterfree hierarchical community detection method based on micro-community and minimum spanning tree. The proposed method first identifies microcommunities based on link strength between adjacent vertices, and then, it constructs minimum spanning tree by successively linking these microcommunities one by one. The hierarchical community structure of social networks can be intuitively revealed from the merging order of these microcommunities. Experimental results on synthetic and real-world networks show that our proposed method exhibits good accuracy and efficiency performance and outperforms other state-of-the-art methods. In addition, our proposed method does not require any pre-defined parameters, and the output dendrogram is simple and meaningful for understanding and analyzing the hierarchical community structure of social networks.

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An approach based on mixed hierarchical clustering and optimization for graph analysis in social media network: toward globally hierarchical community structure

Toujani¹,

Akaichi

2019

Knowl Inf Syst

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Optimal initial partitionning for high quality hybrid hierarchical community detection in social networks

Cited by 4 publications

References 13 publications

Event news detection and citizens community structure for disaster management in social networks

Event news detection and citizens community structure for disaster management in social networks

Hierarchical Community Detection in Social Networks Based on Micro-Community and Minimum Spanning Tree

An approach based on mixed hierarchical clustering and optimization for graph analysis in social media network: toward globally hierarchical community structure

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