Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …

Node classification is an important task in many graph-related applications. Recently, graph
neural networks like Graph Convolutional Networks (GCNs) have provided low-dimensional …

One of the crucial research areas in the analysis of complex social networks is the
identification of communities. Since community detection is an NP-complete problem …

Abstract Graph Neural Networks (GNNs) have exhibited significant success in various
applications, but they face challenges when labeled nodes are limited. A novel self …

Social bot detection is crucial for ensuring the active participation of digital twins and edge
intelligence in future social media platforms. Nevertheless, the performance of existing …

Interactive Video Object Segmentation (IVOS) addresses the problem of pixel-wise video
annotation in collaboration with a human supervisor. Deep convolutional network-based …

Graph structure data is created in many real applications. Graphs represent data by
displaying entities (nodes) and their relationships (edges). Several methods are available …

Numerous real-world applications generate graph structure data. Graphs represent data by
showing the connections between entities (nodes) and their relationships (edges). If graphs …