Author name cluster

Ziwei Chai

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4 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Cypher-RI: Reinforcement Learning for Integrating Schema Selection into Cypher Generation

Hanchen Su
Xuyuan Li
Yan Zhou
zhuoyi lu
Ziwei Chai
Haozheng Wang
Chen Zhang
Yang Yang

The increasing utilization of graph databases across various fields stems from their capacity to represent intricate interconnections. Nonetheless, exploiting the full capabilities of graph databases continues to be a significant hurdle, largely because of the inherent difficulty in translating natural language into Cypher. Recognizing the critical role of schema selection in database query generation and drawing inspiration from recent progress in reasoning-augmented approaches trained through reinforcement learning to enhance inference capabilities and generalization, we introduce Cypher-RI, a specialized framework for the Text-to-Cypher task. Distinct from conventional approaches, our methodology seamlessly integrates schema selection within the Cypher generation pipeline, conceptualizing it as a critical element in the reasoning process. The schema selection mechanism is guided by textual context, with its outcomes recursively shaping subsequent inference processes. Impressively, our 7B-parameter model, trained through this RL paradigm, demonstrates superior performance compared to baselines, exhibiting a 9. 41\% accuracy improvement over GPT-4o on CypherBench. These results underscore the effectiveness of our proposed reinforcement learning framework, which integrates schema selection to enhance both the accuracy and reasoning capabilities in Text-to-Cypher tasks.

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ICML Conference 2024 Conference Paper

InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks

Xueyu Hu
Ziyu Zhao 0001
Shuang Wei
Ziwei Chai
Qianli Ma
Guoyin Wang 0002
Xuwu Wang
Jing Su

In this paper, we introduce InfiAgent-DABench, the first benchmark specifically designed to evaluate LLM-based agents on data analysis tasks. Agents need to solve these tasks end-to-end by interacting with an execution environment. This benchmark contains DAEval, a dataset consisting of 603 data analysis questions derived from 124 CSV files, and an agent framework which incorporates LLMs to serve as data analysis agents for both serving and evaluating. Since data analysis questions are often open-ended and hard to evaluate without human supervision, we adopt a format-prompting technique to convert each question into a closed-form format so that they can be automatically evaluated. Our extensive benchmarking of 34 LLMs uncovers the current challenges encountered in data analysis tasks. In addition, building upon our agent framework, we develop a specialized agent, DAAgent, which surpasses GPT-3. 5 by 3. 9% on DABench. Evaluation datasets and toolkits for InfiAgent-DABench are released at https: //github. com/InfiAgent/InfiAgent.

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AAAI Conference 2023 Conference Paper

Towards Learning to Discover Money Laundering Sub-network in Massive Transaction Network

Ziwei Chai
Yang Yang
Jiawang Dan
Sheng Tian
Changhua Meng
Weiqiang Wang
Yifei Sun

Anti-money laundering (AML) systems play a critical role in safeguarding global economy. As money laundering is considered as one of the top group crimes, there is a crucial need to discover money laundering sub-network behind a particular money laundering transaction for a robust AML system. However, existing rule-based methods for money laundering sub-network discovery is heavily based on domain knowledge and may lag behind the modus operandi of launderers. Therefore, in this work, we first address the money laundering sub-network discovery problem with a neural network based approach, and propose an AML framework AMAP equipped with an adaptive sub-network proposer. In particular, we design an adaptive sub-network proposer guided by a supervised contrastive loss to discriminate money laundering transactions from massive benign transactions. We conduct extensive experiments on real-word datasets in AliPay of Ant Group. The result demonstrates the effectiveness of our AMAP in both money laundering transaction detection and money laundering sub-network discovering. The learned framework which yields money laundering sub-network from massive transaction network leads to a more comprehensive risk coverage and a deeper insight to money laundering strategies.

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IJCAI Conference 2022 Conference Paper

Can Abnormality be Detected by Graph Neural Networks?

Ziwei Chai
Siqi You
Yang Yang
Shiliang Pu
Jiarong Xu
Haoyang Cai
Weihao Jiang

Anomaly detection in graphs has attracted considerable interests in both academia and industry due to its wide applications in numerous domains ranging from finance to biology. Meanwhile, graph neural networks (GNNs) is emerging as a powerful tool for modeling graph data. A natural and fundamental question that arises here is: can abnormality be detected by graph neural networks? In this paper, we aim to answer this question, which is nontrivial. As many existing works have explored, graph neural networks can be seen as filters for graph signals, with the favor of low frequency in graphs. In other words, GNN will smooth the signals of adjacent nodes. However, abnormality in a graph intuitively has the characteristic that it tends to be dissimilar to its neighbors, which are mostly normal samples. It thereby conflicts with the general assumption with traditional GNNs. To solve this, we propose a novel Adaptive Multi-frequency Graph Neural Network (AMNet), aiming to capture both low-frequency and high-frequency signals, and adaptively combine signals of different frequencies. Experimental results on real-world datasets demonstrate that our model achieves a significant improvement comparing with several state-of-the-art baseline methods.

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