7 citations · 9 across the 9 of their papers we have counts for
10 papers
LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence
Xingxuan Zhang, Gang Ren, Hao Yuan +57
We introduce LimiX-2, a new model in the LimiX family, developed through model and data scaling guided by our previously established scaling laws. LimiX-2 adopts the Contextual Mec…
SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks
Xing Ai, Guanyu Zhu, Yulin Zhu +4
Graph Neural Networks (GNNs) have demonstrated commendable performance for graph-structured data. Yet, GNNs are often vulnerable to adversarial structural attacks as embedding gene…
Adversarial Robustness of Link Sign Prediction in Signed Graphs
Jialong Zhou, Xing Ai, Yuni Lai +7
Signed graphs serve as fundamental data structures for representing positive and negative relationships in social networks, with signed graph neural networks (SGNNs) emerging as th…
Towards Robust Graph Structural Learning Beyond Homophily via Preserving Neighbor Similarity
Yulin Zhu, Yuni Lai, Xing Ai +7
Despite the tremendous success of graph-based learning systems in handling structural data, it has been widely investigated that they are fragile to adversarial attacks on homophil…
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach
Xing Ai, Jialong Zhou, Yulin Zhu +4
Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry. Howev…
Robust Graph Contrastive Learning with Information Restoration
Yulin Zhu, Xing Ai, Yevgeniy Vorobeychik +1
The graph contrastive learning (GCL) framework has gained remarkable achievements in graph representation learning. However, similar to graph neural networks (GNNs), GCL models are…
Labeled Subgraph Entropy Kernel
Chengyu Sun, Xing Ai, Zhihong Zhang +1
In recent years, kernel methods are widespread in tasks of similarity measuring. Specifically, graph kernels are widely used in fields of bioinformatics, chemistry and financial da…
Simple yet Effective Gradient-Free Graph Convolutional Networks
Yulin Zhu, Xing Ai, Qimai Li +2
Linearized Graph Neural Networks (GNNs) have attracted great attention in recent years for graph representation learning. Compared with nonlinear Graph Neural Network (GNN) models,…
Two-level Graph Neural Network
Xing Ai, Chengyu Sun, Zhihong Zhang +1
Graph Neural Networks (GNNs) are recently proposed neural network structures for the processing of graph-structured data. Due to their employed neighbor aggregation strategy, exist…
Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach
Xing Ai, Zhihong Zhang, Luzhe Sun +2
Quantum machine learning is a fast-emerging field that aims to tackle machine learning using quantum algorithms and quantum computing. Due to the lack of physical qubits and an eff…