Tongya Zheng

Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection

Runang He, Tongya Zheng, Huiling Peng, Yuanyu Wan et autres

Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors. Recently, advanced Graph Anomaly Detection (GAD) approaches applied to blockchains have faced two critical challenges: adversarial pattern evolution by …

cn (code pays fourni par la source)

Learnable Data Augmentation and Contrastive Pre-training for Temporal Link Prediction

Canghong Jin, Jiafeng Zhao, Feng Xu, Tongya Zheng et autres

Link prediction is a foundational task in temporal graphs. While temporal graph neural networks exhibit commendable performance, they are often criticized for providing inadequate representations, especially under limited data. Contrastive learning has been introduced as a solution for graph pre-training to mitigate …

bd, cn (code pays fourni par la source)

GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning

Yuchen Ying, Weiqi Jiang, Tongya Zheng, Yi-Qiong Wang et autres

Knowledge graphs provide structured and reliable information for many real-world applications, motivating increasing interest in combining large language models (LLMs) with graph-based retrieval to improve factual grounding. Recent Graph-based Retrieval-Augmented Generation (GraphRAG) methods therefore introduce iterative interaction between LLMs and knowledge graphs …

cn, mo, sg (code pays fourni par la source)

Capturing Context-Aware Route Choice Semantics for Trajectory Representation Learning

Ji Cao, Yu Wang, Tongya Zheng, Jie Song et autres

Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation, mobility prediction, and trajectory similarity analysis. From a behavioral perspective, a trajectory reflects a sequence of route choices within an urban …

cn, bd (code pays fourni par la source)

Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection

Runang He, Tongya Zheng, Huiling Peng, Yuanyu Wan et autres

Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors. Recently, advanced Graph Anomaly Detection (GAD) approaches applied to blockchains have faced two critical challenges: \textit{adversarial pattern evolution by …

cn (code pays fourni par la source)

Parallelized Planning-Acting for Multi-Agent LLM Systems in Minecraft

Y. Li, Shunyu Liu, Tongya Zheng, Li Sun et autres

Recent advancements in Large Language Model (LLM)-based Multi-Agent Systems (MAS) have demonstrated remarkable potential for tackling complex decision-making tasks. However, existing frameworks inevitably rely on serialized execution paradigms, where agents must complete sequential LLM planning before taking action. This fundamental constraint severely …

cn, sg, mo (code pays fourni par la source)

Physics-Informed Diffusion Generation for Geomagnetic Map Interpolation

Wenda Li, Tongya Zheng, K Chen, Shunyu Liu et autres

Geomagnetic map interpolation aims to infer unobserved geomagnetic data at spatial points, yielding critical applications in navigation and resource exploration. However, existing methods for scattered data interpolation are not specifically designed for geomagnetic maps, which inevitably leads to suboptimal performance due to …

cn, ru (code pays fourni par la source)

Adaptive Location Hierarchy Learning for Long-Tailed Mobility Prediction

Tongya Zheng, Mingli Song

Human mobility prediction is crucial for applications ranging from location-based recommendations to urban planning, which aims to forecast users' next location visits based on historical trajectories. While existing mobility prediction models excel at capturing sequential patterns through diverse architectures for different scenarios, …

cn (code pays fourni par la source)

Dual-branch Spatial-Temporal Self-supervised Representation for Enhanced Road Network Learning

Qinghong Guo, Yu Wang, Ji Cao, Tongya Zheng et autres

Road network representation learning (RNRL) has attracted increasing attention from both researchers and practitioners as various spatiotemporal tasks are emerging. Recent advanced methods leverage Graph Neural Networks (GNNs) and contrastive learning to characterize the spatial structure of road segments in a self-supervised …

cn, sg (code pays fourni par la source)

GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning

Yuchen Ying, Weiqi Jiang, Tongya Zheng, Yu Wang et autres

Knowledge graphs provide structured and reliable information for many real-world applications, motivating increasing interest in combining large language models (LLMs) with graph-based retrieval to improve factual grounding. Recent Graph-based Retrieval-Augmented Generation (GraphRAG) methods therefore introduce iterative interaction between LLMs and knowledge graphs …

GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning

Yuchen Ying, Weiqi Jiang, Tongya Zheng, Yu Wang et autres

Knowledge graphs provide structured and reliable information for many real-world applications, motivating increasing interest in combining large language models (LLMs) with graph-based retrieval to improve factual grounding. Recent Graph-based Retrieval-Augmented Generation (GraphRAG) methods therefore introduce iterative interaction between LLMs and knowledge graphs …

cn, sg (code pays fourni par la source)

Dual-branch Spatial-Temporal Self-supervised Representation for Enhanced Road Network Learning

Association for Artificial Intelligence 2026, Ji Cao, Junshu Dai, Qinghong Guo et autres

Road network representation learning (RNRL) has attracted increasing attention from both researchers and practitioners as various spatiotemporal tasks are emerging. Recent advanced methods leverage Graph Neural Networks (GNNs) and contrastive learning to characterize the spatial structure of road segments in a self-supervised …

sg (code pays fourni par la source)