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Embeddings in deep neural networks are essential for processing high-dimensional and categorical data by converting it into compact, low-dimensional vectors. This conversion enables the model to capture complex semantic relationships and improves its ...

Community detection in social networks remains a challenging yet crucial task in social network analysis, offering insights into the underlying structures and dynamics of social systems. This study introduces QB-COM, a novel integration of reinforcement ...

Topic modeling plays an important role in text mining for semantic mining. Existing topic models remain semantic loss, fuzzy topic concepts, and topic overlap issues because the sparsity of semantic information. For addressing above issues, in ...

Community detection can reveal the structural properties of real social networks. In community detection, label propagation is an effective typical method which has the advantage of nearly linear time complexity. However, it usually random selects ...

The formalism of multilayer networks (MLN) makes possible to model and understand the multiple relationships between entities in a system. Indeed, this representation has found its way into a wide range of disciplines, particularly in the fields ...

The iOS App Store is widely recognized as a trustworthy source for applications, primarily because of the strict regulations enforced by Apple. However, despite these measures, the presence of scamwares and the prevalence of crowdturfing (fake) reviews ...

With the primary goal of raising readers’ awareness of misinformation phenomena, extensive efforts have been made by both academic institutions and independent organizations to develop methodologies for assessing the trustworthiness of online news ...

Context: The Brazilian Information Systems (BIS) community has gained importance in national and international scenarios. Understanding its evolution aids in defining contributions to future research. Problem: Gaining knowledge of large communities is ...

Lifestyle transformations in the US are creating distinct regional visitation behaviours, particularly in terms of time spent at various drinking Points of Interest (POIs) for alcoholic beverages, an aspect often overlooked in the current literature. This ...

Many studies on community detection are mainly based on the similarity in friendship between users. Recent studies have started to explore node contents to identify semantically meaningful communities. However, the sentimental interaction information ...

Sharded blockchain provides a better linear scalability and scalability compared to traditional blockchain by dividing the blockchain into several disjoint shards, making it a more suitable blockchain solution for the Internet of Things (IoT). ...

Chronic hepatitis B virus (HBV) infection is still a global health problem, with over 296 million chronically HBV-infected individuals worldwide. The merging data about clinical parameters, immune phenotyping data, and genetic information, together with ...

Community detection in complex signed networks is a significant challenge, traditionally addressed using the Louvain method directly applied to the correlation matrix. This study introduces a two-tier approach that integrates a Hebbian learning ...

Community Detection (CD) in large social networks is a highly active research area due to its immense practical value in many real-world applications. Genetic algorithms (GAs) are widely used to solve the CD problem due to their strong ability to ...

Computed Tomography (CT) images are widely used due to their low cost and high effectiveness. However, artifacts caused by human motion lead to a decline in image quality, which affects diagnostic accuracy and prognosis. Recently, significant ...

Much work in the literature has studied different types of cyber security related users and communities on OSNs, such as activists, hacktivists, hackers, cyber criminals. A few studies also covered no-expert users who discussed cyber security ...

Community detection becomes an important problem with the booming of social networks. The Medoid-Shift algorithm preserves the benefits of Mean-Shift and can be applied to problems based on distance matrix, such as community detection. One ...

Social network analysis is an emerging field of research that is an amalgamation of various disciplines such as social science, network science, data analytics and behavioral science. It enables us to investigate the microscopic as well as macroscopic ...

Shared travel has gradually become one of the hot topics discussed on social networking platforms such as Micro Blog. In a timely manner, deeper network community detection on the evaluation content of shared travel in social networks can ...

Graph mining and analytics refer to analyzing and extracting meaningful information from graph data structures. Graph mining includes techniques such as community detection, centrality analysis, and graph pattern matching to identify significant ...