2 citations · 7 across the 13 of their papers we have counts for
13 papers
CSC: Calibrated Simplicity for Conflict-Aware Social Bot Detection in the LLM Era
Yipeng Qian, Pengjie Zhao, Chaoxi Niu
Social bot detection is essential for protecting online platforms from misinformation amplification, coordinated manipulation, and distorted public discourse. However, large langua…
PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs
Guoguo Ai, Chaoxi Niu, Hui Yan +3
Activities in numerous evolving systems can be represented as dynamic graphs in snapshot form at different time intervals, i.e., discrete-time dynamic graphs (DTDGs). Existing meth…
WaveTuner: Comprehensive Wavelet Subband Tuning for Time Series Forecasting
Yubo Wang, Hui He, Chaoxi Niu +1
Due to the inherent complexity, temporal patterns in real-world time series often evolve across multiple intertwined scales, including long-term periodicity, short-term fluctuation…
Reprogramming Vision Foundation Models for Spatio-Temporal Forecasting
Changlu Chen, Yanbin Liu, Chaoxi Niu +2
Foundation models have achieved remarkable success in natural language processing and computer vision, demonstrating strong capabilities in modeling complex patterns. While recent…
Hierarchical Consensus Network for Multiview Feature Learning
Chengwei Xia, Chaoxi Niu, Kun Zhan
Multiview feature learning aims to learn discriminative features by integrating the distinct information in each view. However, most existing methods still face significant challen…
AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection
Hezhe Qiao, Chaoxi Niu, Ling Chen +1
Graph anomaly detection (GAD) aims to identify abnormal nodes that differ from the majority of the nodes in a graph, which has been attracting significant attention in recent years…
Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach
Chaoxi Niu, Guansong Pang, Ling Chen +1
Class-incremental learning (CIL) aims to continually learn a sequence of tasks, with each task consisting of a set of unique classes. Graph CIL (GCIL) follows the same setting but…
Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts
Chaoxi Niu, Hezhe Qiao, Changlu Chen +2
Graph anomaly detection (GAD), which aims to identify nodes in a graph that significantly deviate from normal patterns, plays a crucial role in broad application domains. However,…
Graph Continual Learning with Debiased Lossless Memory Replay
Chaoxi Niu, Guansong Pang, Ling Chen
Real-life graph data often expands continually, rendering the learning of graph neural networks (GNNs) on static graph data impractical. Graph continual learning (GCL) tackles this…
Graph-level Anomaly Detection via Hierarchical Memory Networks
Chaoxi Niu, Guansong Pang, Ling Chen
Graph-level anomaly detection aims to identify abnormal graphs that exhibit deviant structures and node attributes compared to the majority in a graph set. One primary challenge is…
Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning
Chaoxi Niu, Guansong Pang, Ling Chen
Hard negative mining has shown effective in enhancing self-supervised contrastive learning (CL) on diverse data types, including graph CL (GCL). The existing hardness-aware CL meth…
Mutual Teaching for Graph Convolutional Networks
Kun Zhan, Chaoxi Niu
Graph convolutional networks produce good predictions of unlabeled samples due to its transductive label propagation. Since samples have different predicted confidences, we take hi…
Generative approach to unsupervised deep local learning
Changlu Chen, Chaoxi Niu, Xia Zhan +1
Most existing feature learning methods optimize inflexible handcrafted features and the affinity matrix is constructed by shallow linear embedding methods. Different from these con…