Embedding-based Silhouette community detection

Škrlj, Blaž; Kralj, Jan; Lavrač, Nada

doi:10.1007/s10994-020-05882-8

Cited by 13 publications

(5 citation statements)

References 54 publications

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“…The partition is performed while aiming to minimize the in-cluster variance and maximize the variance between the elements from different clusters. To determine the optimal number of clusters [ 77 ], we computed the silhouette scores of k-means clustering runs with k ∈ [3;6]. The silhouette scores of the clustering models were generated on the 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021, related to 141,407 n-grams with n ∈ [2;4].…”

Section: Resultsmentioning

confidence: 99%

Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study

Benis¹,

Chatsubi²,

Levner³

et al. 2021

JMIR Infodemiology

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Background Discussions of health issues on social media are a crucial information source reflecting real-world responses regarding events and opinions. They are often important in public health care, since these are influencing pathways that affect vaccination decision-making by hesitant individuals. Artificial intelligence methodologies based on internet search engine queries have been suggested to detect disease outbreaks and population behavior. Among social media, Twitter is a common platform of choice to search and share opinions and (mis)information about health care issues, including vaccination and vaccines. Objective Our primary objective was to support the design and implementation of future eHealth strategies and interventions on social media to increase the quality of targeted communication campaigns and therefore increase influenza vaccination rates. Our goal was to define an artificial intelligence–based approach to elucidate how threads in Twitter on influenza vaccination changed during the COVID-19 pandemic. Such findings may support adapted vaccination campaigns and could be generalized to other health-related mass communications. Methods The study comprised the following 5 stages: (1) collecting tweets from Twitter related to influenza, vaccines, and vaccination in the United States; (2) data cleansing and storage using machine learning techniques; (3) identifying terms, hashtags, and topics related to influenza, vaccines, and vaccination; (4) building a dynamic folksonomy of the previously defined vocabulary (terms and topics) to support the understanding of its trends; and (5) labeling and evaluating the folksonomy. Results We collected and analyzed 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021. These tweets were in English, were from the United States, and included at least one of the following terms: “flu,” “influenza,” “vaccination,” “vaccine,” and “vaxx.” We noticed that the prevalence of the terms vaccine and vaccination increased over 2020, and that “flu” and “covid” occurrences were inversely correlated as “flu” disappeared over time from the tweets. By combining word embedding and clustering, we then identified a folksonomy built around the following 3 topics dominating the content of the collected tweets: “health and medicine (biological and clinical aspects),” “protection and responsibility,” and “politics.” By analyzing terms frequently appearing together, we noticed that the tweets were related mainly to COVID-19 pandemic events. Conclusions This study focused initially on vaccination against influenza and moved to vaccination against COVID-19. Infoveillance supported by machine learning on Twitter and other social media about topics related to vaccines and vaccination against communicable diseases and their trends can lead to the design of personalized messages encouraging targeted subpopulations’ engagement in vaccination. A greater likelihood that a targeted population receives a personalized message is associated with higher response, engagement, and proactiveness of the target population for the vaccination process.

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Section: Resultsmentioning

confidence: 99%

Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study

Benis¹,

Chatsubi²,

Levner³

et al. 2021

JMIR Infodemiology

View full text Add to dashboard Cite

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“…Input: a network G(V, E), the number of nodes in the network n Output: the importance of each node (1) Initialize D � ∅, CC � ∅ (2) for i in n do (3) Calculate D(i) using formula (4) during D decomposition (4) end for (5) for i in n do (6) Calculate CC(i) using formula (5) (7) end for (8) Create matrix R using formula (6) (9) for i in 2 do (10) Calculate E i using formula (7) (11) end for (12) for i in n do (13) Calculate CLC(i) using formula (10) ( 14) end for ALGORITHM 1: Node importance evaluation algorithm.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…e community detection technology can predict the hobbies of new network users. In the protein network [3], proteins with the same or similar functions constitute each community. Community detection technology can identify the group of unfamiliar proteins and thus discover the protein function.…”

Section: Introductionmentioning

confidence: 99%

A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

Liu

Guo

2021

Complexity

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Community structure is one of the most important characteristics of complex networks, which has important applications in sociology, biology, and computer science. The community detection method based on local expansion is one of the most adaptable overlapping community detection algorithms. However, due to the lack of effective seed selection and community optimization methods, the algorithm often gets community results with lower accuracy. In order to solve these problems, we propose a seed selection algorithm of fusion degree and clustering coefficient. The method calculates the weight value corresponding to degree and clustering coefficient by entropy weight method and then calculates the weight factor of nodes as the seed node selection order. Based on the seed selection algorithm, we design a local expansion strategy, which uses the strategy of optimizing adaptive function to expand the community. Finally, community merging and isolated node adjustment strategies are adopted to obtain the final community. Experimental results show that the proposed algorithm can achieve better community partitioning results than other state-of-the-art algorithms.

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“…SCD (Silhouette Community Detection) [36] is an embedded clustering method, which reveals community structure by optimizing the contour measurement, particularly, extracting the real value representation of nodes from its neighborhood.…”

Section: Methodsmentioning

confidence: 99%

Community Detection Based on Fish School Effect

Sun

Chang

et al. 2022

IEEE Access

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Community structure has a more intuitive, physical meaning than how it has traditionally been perceived; this representation plays an important role in networks and is accordingly becoming widely adapted. Consequently, community detection has attracted increasing attention. Although scholars have proposed many community detection methods from different perspectives, due to the complexity, diversity and dynamic characteristics of networks, efficient community detection in many real networks remains a challenge. Inspired by fish school effect in real life, this paper envisions networks as an ecosystem and proposes a novel dynamic model that aims to reveal the communities in a more intuitive way. Relying on the new model, we design a community detection algorithm, known as community detection based on fish school effect (CDFSE). CDFSE has plentiful desirable properties: high-quality community detection, parameters free and notable scalability. To evaluate the performance of CDFSE, this paper employs two widely utilized evaluation metrics and eleven representative algorithms to test the effectiveness of the algorithm in both synthetic and real-world networks. The experimental results show that in most cases, CDFSE is superior to the comparison methods in terms of the quality of community detection.

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Embedding-based Silhouette community detection

Cited by 13 publications

References 54 publications

Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study

Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study

A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

Community Detection Based on Fish School Effect

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