6 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…
Graph Neural Networks for Brain Graph Learning: A Survey
Xuexiong Luo, Jia Wu, Jian Yang +7
Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders. Thanks to advancements in neuroimaging technology,…