7 citations · 8 across the 9 of their papers we have counts for
11 papers
MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning
Junlin Chen, Wenhao Wang, Junchao Xu +5
Graph Neural Networks (GNNs) excel on graph-structured data but suffer catastrophic forgetting when classes arrive sequentially. In class-incremental graph learning, predictions ar…
Riemannian Graph Tokenizer for Structural Knowledge Transfer
Qimin Zhou, Haibo Liu, Yujie Wang +2
Foundation models are at the forefront of artificial intelligence. A tokenizer, converting the raw input into discrete representations that the model can understand, plays an impor…
Trust-But-Verify: Poisoning-Resilient Locally Private Graph Learning Protocols
Longzhu He, Li Sun, Hao Peng +3
Built upon local differential privacy (LDP), locally private graph learning protocols have emerged as an important paradigm for decentralized graph learning, balancing privacy prot…
Paying for Honesty Without Knowing the Truth: Reputation-Penalty Design for LLM Marketplace Agents
Mingdai Yang, Shicheng Fan, Kejing Yu +5
LLM agents increasingly act as autonomous merchants that write their own product listings, and under competitive pressure, they fabricate attributes to win sales. Even under instru…
Towards Personalized Differentially Private Learning for Decentralized Local Graphs
Longzhu He, Peng Tang, Chaozhuo Li +5
Graph-structured data is increasingly generated and stored in decentralized environments, such as social platforms, mobile applications, and edge networks, where users maintain con…
Effective and Unsupervised Social Event Detection and Evolution via RAG and Structural Entropy
Qitong Liu, Hao Peng, Zuchen Li +5
With the growing scale of social media, social event detection and evolution modeling have attracted increasing attention. Graph neural networks (GNNs) and transformer-based pre-tr…
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols
Longzhu He, Chaozhuo Li, Peng Tang +3
Graph neural networks (GNNs) have achieved significant success in graph representation learning and have been applied to various domains. However, many real-world graphs contain se…
Towards Effective, Efficient and Unsupervised Social Event Detection in the Hyperbolic Space
Xiaoyan Yu, Yifan Wei, Shuaishuai Zhou +5
The vast, complex, and dynamic nature of social message data has posed challenges to social event detection (SED). Despite considerable effort, these challenges persist, often resu…
DAMe: Personalized Federated Social Event Detection with Dual Aggregation Mechanism
Xiaoyan Yu, Yifan Wei, Pu Li +5
Training social event detection models through federated learning (FedSED) aims to improve participants' performance on the task. However, existing federated learning paradigms are…
Table-Filling via Mean Teacher for Cross-domain Aspect Sentiment Triplet Extraction
Kun Peng, Lei Jiang, Qian Li +6
Cross-domain Aspect Sentiment Triplet Extraction (ASTE) aims to extract fine-grained sentiment elements from target domain sentences by leveraging the knowledge acquired from the s…
Adaptive Differentially Private Structural Entropy Minimization for Unsupervised Social Event Detection
Zhiwei Yang, Yuecen Wei, Haoran Li +6
Social event detection refers to extracting relevant message clusters from social media data streams to represent specific events in the real world. Social event detection is impor…