10 citations · 10 across the 6 of their papers we have counts for
6 papers
Context-KG: Context-Aware Knowledge Graph Visualization with User Preferences and Ontological Guidance
Rumali Perera, Xiaoqi Wang, Han-wei Shen
Knowledge Graphs (KGs) are increasingly used to represent and explore complex, interconnected data across diverse domains. However, existing KG visualization systems remain limited…
Beauty in the Eye of AI: Aligning LLMs and Vision Models with Human Aesthetics in Network Visualization
Peng Zhang, Xuefeng Li, Xiaoqi Wang +2
Network visualization has traditionally relied on heuristic metrics, such as stress, under the assumption that optimizing them leads to aesthetic and informative layouts. However,…
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction
Ziwei Li, Xiaoqi Wang, Hong-You Chen +2
Federated learning (FL) has rapidly evolved as a promising paradigm that enables collaborative model training across distributed participants without exchanging their local data. D…
GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
Xiaoqi Wang, Han-Wei Shen
Recently, Graph Neural Networks (GNNs) have significantly advanced the performance of machine learning tasks on graphs. However, this technological breakthrough makes people wonder…
SmartGD: A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goals
Xiaoqi Wang, Kevin Yen, Yifan Hu +1
While a multitude of studies have been conducted on graph drawing, many existing methods only focus on optimizing a single aesthetic aspect of graph layouts, which can lead to sub-…
DeepGD: A Deep Learning Framework for Graph Drawing Using GNN
Xiaoqi Wang, Kevin Yen, Yifan Hu +1
In the past decades, many graph drawing techniques have been proposed for generating aesthetically pleasing graph layouts. However, it remains a challenging task since different la…