13 citations · 26 across the 19 of their papers we have counts for
22 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…
GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning
Yuchen Ying, Weiqi Jiang, Tongya Zheng +4
Knowledge graphs provide structured and reliable information for many real-world applications, motivating increasing interest in combining large language models (LLMs) with graph-b…
Physics-informed Diffusion Generation for Geomagnetic Map Interpolation
Wenda Li, Tongya Zheng, Kaixuan Chen +8
Geomagnetic map interpolation aims to infer unobserved geomagnetic data at spatial points, yielding critical applications in navigation and resource exploration. However, existing…
Learning Multi-Modal Mobility Dynamics for Generalized Next Location Recommendation
Junshu Dai, Yu Wang, Tongya Zheng +6
The precise prediction of human mobility has produced significant socioeconomic impacts, such as location recommendations and evacuation suggestions. However, existing methods suff…
Neural Graph Navigation for Intelligent Subgraph Matching
Yuchen Ying, Yiyang Dai, Wenda Li +6
Subgraph matching, a cornerstone of relational pattern detection in domains ranging from biochemical systems to social network analysis, faces significant computational challenges…
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…
Capturing Context-Aware Route Choice Semantics for Trajectory Representation Learning
Ji Cao, Yu Wang, Tongya Zheng +6
Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation, mobility prediction…
VeriWeb: Verifiable Long-Chain Web Benchmark for Agentic Information-Seeking
Shunyu Liu, Minghao Liu, Huichi Zhou +31
Recent advances have showcased the extraordinary capabilities of Large Language Model (LLM) agents in tackling web-based information-seeking tasks. However, existing efforts mainly…
Bi-level Mean Field: Dynamic Grouping for Large-Scale MARL
Yuxuan Zheng, Yihe Zhou, Feiyang Xu +2
Large-scale Multi-Agent Reinforcement Learning (MARL) often suffers from the curse of dimensionality, as the exponential growth in agent interactions significantly increases comput…
SeRL: Self-Play Reinforcement Learning for Large Language Models with Limited Data
Wenkai Fang, Shunyu Liu, Yang Zhou +5
Recent advances have demonstrated the effectiveness of Reinforcement Learning (RL) in improving the reasoning capabilities of Large Language Models (LLMs). However, existing works…
Adaptive Location Hierarchy Learning for Long-Tailed Mobility Prediction
Yu Wang, Junshu Dai, Yuchen Ying +4
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 his…
From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networks
Jie Yang, Yuwen Wang, Kaixuan Chen +6
Interpretable Graph Neural Networks (GNNs) aim to reveal the underlying reasoning behind model predictions, attributing their decisions to specific subgraphs that are informative.…
Parallelized Planning-Acting for Efficient LLM-based Multi-Agent Systems in Minecraft
Yaoru Li, Shunyu Liu, Tongya Zheng +2
Recent advancements in Large Language Model~(LLM)-based Multi-Agent Systems (MAS) have demonstrated remarkable potential for tackling complex decision-making tasks. However, existi…
Learning a Mini-batch Graph Transformer via Two-stage Interaction Augmentation
Wenda Li, Kaixuan Chen, Shunyu Liu +3
Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has demonstrated significant advantages in semi-supervised node prediction tasks with improved computationa…
Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks
Yuwen Wang, Shunyu Liu, Tongya Zheng +2
Graph Neural Networks (GNNs) have emerged as a prominent framework for graph mining, leading to significant advances across various domains. Stemmed from the node-wise representati…
Curriculum Negative Mining For Temporal Networks
Ziyue Chen, Tongya Zheng, Mingli Song
Temporal networks are effective in capturing the evolving interactions of networks over time, such as social networks and e-commerce networks. In recent years, researchers have pri…
Odyssey: Empowering Minecraft Agents with Open-World Skills
Shunyu Liu, Yaoru Li, Kongcheng Zhang +5
Recent studies have delved into constructing generalist agents for open-world environments like Minecraft. Despite the encouraging results, existing efforts mainly focus on solving…
Temporal Prototype-Aware Learning for Active Voltage Control on Power Distribution Networks
Feiyang Xu, Shunyu Liu, Yunpeng Qing +3
Active Voltage Control (AVC) on the Power Distribution Networks (PDNs) aims to stabilize the voltage levels to ensure efficient and reliable operation of power systems. With the in…
Improving Adversarial Robustness via Feature Pattern Consistency Constraint
Jiacong Hu, Jingwen Ye, Zunlei Feng +5
Convolutional Neural Networks (CNNs) are well-known for their vulnerability to adversarial attacks, posing significant security concerns. In response to these threats, various defe…
COLA: Cross-city Mobility Transformer for Human Trajectory Simulation
Yu Wang, Tongya Zheng, Yuxuan Liang +2
Human trajectory data produced by daily mobile devices has proven its usefulness in various substantial fields such as urban planning and epidemic prevention. In terms of the indiv…
Simple Graph Condensation
Zhenbang Xiao, Yu Wang, Shunyu Liu +3
The burdensome training costs on large-scale graphs have aroused significant interest in graph condensation, which involves tuning Graph Neural Networks (GNNs) on a small condensed…
A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective
Yunpeng Qing, Shunyu liu, Jingyuan Cong +3
Offline reinforcement learning endeavors to leverage offline datasets to craft effective agent policy without online interaction, which imposes proper conservative constraints with…