1 citations · 3 across the 13 of their papers we have counts for
15 papers
DynGraphAgentBench: A Benchmark for Agentic Lifecycle Control in Dynamic Graph Anomaly Detection
Yuwei Han, Lingwei Wei, Wooseong Yang +4
Dynamic graph anomaly detection requires repeated decisions as graph structure and class prevalence drift, yet detector benchmarks usually score a fixed pipeline after current labe…
Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability
Lingwei Wei, Dou Hu, Wei Zhou +2
Large language models (LLMs) have transformed misinformation from a primarily content-centric problem into a broader ecosystem-level security challenge. When misused, LLMs create r…
ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models
Chengze Li, Lingwei Wei, Li Sun +7
Partial differential equation (PDE) foundation models are pretrained networks that forecast how physical fields like velocity and pressure evolve from a single reusable solver. On…
An Information-theoretic Propagation Denoising and Fusion Framework for Fake News Detection
Mengyang Chen, Lingwei Wei, Wei Zhou +1
Incomplete propagation data significantly hinders robust fake news detection. Recent approaches leverage large language models to simulate missing user interactions via role-playin…
Propagation Structure-Semantic Transfer Learning for Robust Fake News Detection
Mengyang Chen, Lingwei Wei, Han Cao +3
Fake news generally refers to false information that is spread deliberately to deceive people, which has detrimental social effects. Existing fake news detection methods primarily…
Structure-aware Propagation Generation with Large Language Models for Fake News Detection
Mengyang Chen, Lingwei Wei, Wei Zhou +1
The spread of fake news on social media poses a serious threat to public trust and societal stability. While propagation-based methods improve fake news detection by modeling how i…
MPPFND: A Dataset and Analysis of Detecting Fake News with Multi-Platform Propagation
Congyuan Zhao, Lingwei Wei, Ziming Qin +3
Fake news spreads widely on social media, leading to numerous negative effects. Most existing detection algorithms focus on analyzing news content and social context to detect fake…
Enhancing Multi-Hop Fact Verification with Structured Knowledge-Augmented Large Language Models
Han Cao, Lingwei Wei, Wei Zhou +1
The rapid development of social platforms exacerbates the dissemination of misinformation, which stimulates the research in fact verification. Recent studies tend to leverage seman…
An Information-theoretic Multi-task Representation Learning Framework for Natural Language Understanding
Dou Hu, Lingwei Wei, Wei Zhou +1
This paper proposes a new principled multi-task representation learning framework (InfoMTL) to extract noise-invariant sufficient representations for all tasks. It ensures sufficie…
Multi-source Knowledge Enhanced Graph Attention Networks for Multimodal Fact Verification
Han Cao, Lingwei Wei, Wei Zhou +1
Multimodal fact verification is an under-explored and emerging field that has gained increasing attention in recent years. The goal is to assess the veracity of claims that involve…
Transferring Structure Knowledge: A New Task to Fake news Detection Towards Cold-Start Propagation
Lingwei Wei, Dou Hu, Wei Zhou +1
Many fake news detection studies have achieved promising performance by extracting effective semantic and structure features from both content and propagation trees. However, it is…
Representation Learning with Conditional Information Flow Maximization
Dou Hu, Lingwei Wei, Wei Zhou +1
This paper proposes an information-theoretic representation learning framework, named conditional information flow maximization, to extract noise-invariant sufficient representatio…
Structured Probabilistic Coding
Dou Hu, Lingwei Wei, Yaxin Liu +2
This paper presents a new supervised representation learning framework, namely structured probabilistic coding (SPC), to learn compact and informative representations from input re…
Are Large Language Models Good Fact Checkers: A Preliminary Study
Han Cao, Lingwei Wei, Mengyang Chen +2
Recently, Large Language Models (LLMs) have drawn significant attention due to their outstanding reasoning capabilities and extensive knowledge repository, positioning them as supe…
Explore the Potential of LLMs in Misinformation Detection: An Empirical Study
Mengyang Chen, Lingwei Wei, Han Cao +2
Large Language Models (LLMs) have garnered significant attention for their powerful ability in natural language understanding and reasoning. In this paper, we present a comprehensi…