52 citations · 83 across the 21 of their papers we have counts for
6 papers · 1 filter
NOCL: Node-Oriented Conceptualization LLM for Graph Tasks without Message Passing
Wei Li, Mengcheng Lan, Jiaxing Xu +1
Graphs are essential for modeling complex interactions across domains such as social networks, biology, and recommendation systems. Traditional Graph Neural Networks, particularly…
-GAN by Rényi Cross Entropy
Ni Ding, Miao Qiao, Jiaxing Xu +2
This paper proposes -GAN, a generative adversarial network using Rényi measures. The value function is formulated, by Rényi cross entropy, as an expected certainty measure incur…
Directed Homophily-Aware Graph Neural Network
Aihu Zhang, Jiaxing Xu, Mengcheng Lan +2
Graph Neural Networks (GNNs) have achieved significant success in various learning tasks on graph-structured data. Nevertheless, most GNNs struggle to generalize to heterophilic ne…
Divergent Paths: Separating Homophilic and Heterophilic Learning for Enhanced Graph-level Representations
Han Lei, Jiaxing Xu, Xia Dong +1
Graph Convolutional Networks (GCNs) are predominantly tailored for graphs displaying homophily, where similar nodes connect, but often fail on heterophilic graphs. The strategy of…
BrainOOD: Out-of-distribution Generalizable Brain Network Analysis
Jiaxing Xu, Yongqiang Chen, Xia Dong +5
In neuroscience, identifying distinct patterns linked to neurological disorders, such as Alzheimer's and Autism, is critical for early diagnosis and effective intervention. Graph N…
Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification
Jiaxing Xu, Kai He, Mengcheng Lan +5
Understanding neurological disorder is a fundamental problem in neuroscience, which often requires the analysis of brain networks derived from functional magnetic resonance imaging…