Parallel social behavior-based algorithm for identification of influential users in social network

Mnasri, Wassim; Azaouzi, Mehdi; Romdhane, Lotfi

doi:10.1007/s10489-021-02203-x

Cited by 15 publications

(7 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is worth noting that the graph of users' relations such as following relations can be exploited too to enhance the medical IE. Thus, some important tasks such as community detection and influence identification [45,56,63,99] can be combined with medical IE tasks in social media.…”

Section: Experimental Analysismentioning

confidence: 99%

Information extraction from electronic medical documents: state of the art and future research directions

2022

View full text Add to dashboard Cite

In the medical field, a doctor must have a comprehensive knowledge by reading and writing narrative documents, and he is responsible for every decision he takes for patients. Unfortunately, it is very tiring to read all necessary information about drugs, diseases and patients due to the large amount of documents that are increasing every day. Consequently, so many medical errors can happen and even kill people. Likewise, there is such an important field that can handle this problem, which is the information extraction. There are several important tasks in this field to extract the important and desired information from unstructured text written in natural language. The main principal tasks are named entity recognition and relation extraction since they can structure the text by extracting the relevant information. However, in order to treat the narrative text we should use natural language processing techniques to extract useful information and features. In our paper, we introduce and discuss the several techniques and solutions used in these tasks. Furthermore, we outline the challenges in information extraction from medical documents. In our knowledge, this is the most comprehensive survey in the literature with an experimental analysis and a suggestion for some uncovered directions.

show abstract

Section: Experimental Analysismentioning

confidence: 99%

Information extraction from electronic medical documents: state of the art and future research directions

2022

View full text Add to dashboard Cite

show abstract

“…Mapping of an association rule can provide a new technique for personalizing various services market analysis. Twitter is used to advertise various campaigns, and sometimes advertisers appoint a few buzzers to make a campaign activity going more frequently [24]. Some of the researches have been done using algorithm of cluster, to find the top users base on influential aspect.…”

Section: Influential Users and Social Networkmentioning

confidence: 99%

Association Rule Analysis-Based Identification of Influential Users in爐he燬ocial Media

Iqbal¹,

Khan²,

Khan³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The exchange of information is an innate and natural process that assist in content dispersal. Social networking sites emerge to enrich their users by providing the facility for sharing information and social interaction. The extensive adoption of social networking sites also resulted in user content generation. There are diverse research areas explored by the researchers to investigate the influence of social media on users and confirmed that social media sites have a significant impact on markets, politics and social life. Facebook is extensively used platform to share information, thoughts and opinions through posts and comments. The identification of influential users on the social web has grown as hot research field because of vast applications in diverse areas for instance political campaigns marketing, e-commerce, commercial and, etc. Prior research studies either uses linguistic content or graph-based representation of social network for the detection of influential users. In this article, we incorporate association rule mining algorithms to identify the top influential users through frequent patterns. The association rules have been computed using the standard evaluation measures such as support, confidence, lift, and conviction. To verify the results, we also involve conventional metrics for example accuracy, precision, recall and F1-measure according to the association rules perspective. The detailed experiments are carried out using the benchmark College-Msg dataset extracted by Facebook. The obtained results validate the quality and visibility of the proposed approach. The outcome of propose model verify that the association rule mining is able to generate rules to identify the temporal influential users on Facebook who are consistent on regular basis. The preparation of rule set help to create knowledge-based systems which are efficient and widely used in recent era for decision making to solve real-world problems.

show abstract

“…A new parallel algorithm, called, named Parallel approach to Support the social behavior and the semantics Aware for the Influence M maximization (PSAIIM) was proposed in [1]. This method was proposed with the objective of identifying the influential users in social network.…”

Section: Introductionmentioning

confidence: 99%

Copula Tubular Quantum Based Spearman Deep Neural for Influential Node Tracing in Social Network

2024

Preprint

View full text Add to dashboard Cite

Social networks are one the foremost origins of information transmission at present. Nevertheless, not all nodes in social networks are indistinguishable. As a matter of fact, certain nodes are said to be more influential than others, or to be more specific, their information gravitates to proliferate more. Identifying the most influential nodes in a social network called as the Influence Maximization problem remains one of the hot issue with the evolution of Internet and social media. Several methods have been proposed to identify influential nodes in composite networks, ranging from parallel algorithm to distribution difference and non-overlapping communities. However, most of the previous methods do not take into account error involved in overlapping communities in social network. To address this issue, in this work, a Copula Tubular Neighborhood and Quantum-based Spearman Deep Neural Network (CTNQ-SDNN) for influential node tracing in social network is proposed. The CTNQ-SDNN method is split into three sections. First, computationally efficient nodes are identified by employing Copula Probability Node Pre-processing model. Then, with the identification of effective nodes, accurate feature extraction is made by means of Tubular Neighborhood Intensity-based Feature extraction model. Finally, influential node selection and tracing in case of tie or overlapping communities with the purpose of reducing error rate is proposed by utilizing Spearman Deep Neural Influential Node Tracing model. Experimental results on telecom dataset social networks, comparing our proposed method against state-of-the-art methods in current literature, indicates our method to be efficient and robust in tackling the influence maximization issue even in case of tie or overlapping communications in social network via error rate.

show abstract

Parallel social behavior-based algorithm for identification of influential users in social network

Cited by 15 publications

References 49 publications

Information extraction from electronic medical documents: state of the art and future research directions

Information extraction from electronic medical documents: state of the art and future research directions

Association Rule Analysis-Based Identification of Influential Users in爐he燬ocial Media

Copula Tubular Quantum Based Spearman Deep Neural for Influential Node Tracing in Social Network

Contact Info

Product

Resources

About