11 papers
CryptoFlowBench: Benchmarking Multi-Hop Reasoning on Blockchains for Cryptocurrency Fund Tracing
Runang He, Tongya Zheng, Jing Cao +7
Pseudonymous accounts and complex smart-contract ecosystems substantially hinder the tracing of illicit cryptocurrency fund flows. Existing Graph Neural Networks (GNNs) have achiev…
GraphCert: Bootstrap Agentic Graph Reasoning with Certified Evidence Rubrics
Weiqi Jiang, Yuchen Ying, Rui Wang +5
Graph agents extend large language models (LLMs) with the ability to actively explore and reason over knowledge graphs through multi-step interactions with graph tools. However, tr…
LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation
Hongchen Li, Bohao Wang, Jingbang Chen +5
Large language models (LLMs) have recently emerged as powerful backbones for recommender systems by reformulating recommendation as a token-level generation task. Despite their pro…
Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection
Runang He, Tongya Zheng, Huiling Peng +6
Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous be…
Informative Graph Structure Learning
Shen Han, Zhiyao Zhou, Jiawei Chen +6
The quality of graph-structured data is fundamental to the success of modern graph analysis techniques such as Graph Neural Networks (GNNs). However, real-world graph data is often…
Adaptive Forensic Feature Refinement via Intrinsic Importance Perception
Jiazhen Yang, Junjun Zheng, Kejia Chen +5
With the rapid development of generative models and multimodal content editing technologies, the key challenge in synthetic image detection (SID) lies in cross-distribution general…
SpatCode: Rotary-based Unified Encoding Framework for Efficient Spatiotemporal Vector Retrieval
Bingde Hu, Enhao Pan, Wanjing Zhou +3
Spatiotemporal vector retrieval has emerged as a critical paradigm in modern information retrieval, enabling efficient access to massive, heterogeneous data that evolve over both t…
Dual-branch Spatial-Temporal Self-supervised Representation for Enhanced Road Network Learning
Qinghong Guo, Yu Wang, Ji Cao +5
Road network representation learning (RNRL) has attracted increasing attention from both researchers and practitioners as various spatiotemporal tasks are emerging. Recent advanced…
Towards Efficient LLM-aware Heterogeneous Graph Learning
Wenda Li, Tongya Zheng, Shunyu Liu +7
Heterogeneous graphs are widely present in real-world complex networks, where the diversity of node and relation types leads to complex and rich semantics. Efforts for modeling com…
Dataset Ownership Verification in Contrastive Pre-trained Models
Yuechen Xie, Jie Song, Mengqi Xue +5
High-quality open-source datasets, which necessitate substantial efforts for curation, has become the primary catalyst for the swift progress of deep learning. Concurrently, protec…
LG-CAV: Train Any Concept Activation Vector with Language Guidance
Qihan Huang, Jie Song, Mengqi Xue +6
Concept activation vector (CAV) has attracted broad research interest in explainable AI, by elegantly attributing model predictions to specific concepts. However, the training of C…