5 papers
FraudBench: Protocol-Sensitive Benchmarking of Adversarial Robustness for Financial Risk Assessment
Xitong Zeng, Zhaoge Bi, Yitian Yang +2
Machine learning models are widely used in financial fraud and credit-risk detection, yet their adversarial robustness remains difficult to evaluate because financial tabular data…
Adversarial Attacks Against Automated Fact-Checking: A Survey
Fanzhen Liu, Alsharif Abuadbba, Kristen Moore +5
In an era where misinformation spreads freely, fact-checking (FC) plays a crucial role in verifying claims and promoting reliable information. While automated fact-checking (AFC) h…
Towards Faithful Class-level Self-explainability in Graph Neural Networks by Subgraph Dependencies
Fanzhen Liu, Xiaoxiao Ma, Jian Yang +6
Enhancing the interpretability of graph neural networks (GNNs) is crucial to ensure their safe and fair deployment. Recent work has introduced self-explainable GNNs that generate e…
ProgRoCC: A Progressive Approach to Rough Crowd Counting
Shengqin Jiang, Linfei Li, Haokui Zhang +6
As the number of individuals in a crowd grows, enumeration-based techniques become increasingly infeasible and their estimates increasingly unreliable. We propose instead an estima…
Learning To Sample the Meta-Paths for Social Event Detection
Congbo Ma, Hu Wang, Zitai Qiu +5
Social media data is inherently rich, as it includes not only text content, but also users, geolocation, entities, temporal information, and their relationships. This data richness…