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Yuan Zuo

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4 papers

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NeurIPS Conference 2025 Conference Paper

UniGTE: Unified Graph–Text Encoding for Zero-Shot Generalization across Graph Tasks and Domains

Duo Wang
Yuan Zuo
Guangyue Lu
Junjie Wu

Generalizing to unseen graph tasks without task-specific supervision is challenging: conventional graph neural networks are typically tied to a fixed label space, while large language models (LLMs) struggle to capture graph structure. We introduce UniGTE, an instruction-tuned encoder–decoder framework that unifies structural and semantic reasoning. The encoder augments a pretrained autoregressive LLM with learnable alignment tokens and a structure-aware graph–text attention mechanism, enabling it to attend jointly to a tokenized graph and a natural-language task prompt while remaining permutation-invariant to node order. This yields compact, task-aware graph representations. Conditioned solely on these representations, a frozen LLM decoder predicts and reconstructs: it outputs the task answer and simultaneously paraphrases the input graph in natural language. The reconstruction objective regularizes the encoder to preserve structural cues. UniGTE is instruction-tuned on five datasets spanning node-, edge-, and graph-level tasks across diverse domains, yet requires no fine-tuning at inference. It achieves new state-of-the-art zero-shot results on node classification, link prediction, graph classification and graph regression under cross-task and cross-domain settings, demonstrating that tight integration of graph structure with LLM semantics enables robust, transferable graph reasoning.

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NeurIPS Conference 2024 Conference Paper

LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings

Duo Wang
Yuan Zuo
Fengzhi Li
Junjie Wu

Zero-shot graph machine learning, especially with graph neural networks (GNNs), has garnered significant interest due to the challenge of scarce labeled data. While methods like self-supervised learning and graph prompt learning have been extensively explored, they often rely on fine-tuning with task-specific labels, limiting their effectiveness in zero-shot scenarios. Inspired by the zero-shot capabilities of instruction-fine-tuned large language models (LLMs), we introduce a novel framework named Token Embedding-Aligned Graph Language Model (TEA-GLM) that leverages LLMs as cross-dataset and cross-task zero-shot learners for graph machine learning. Concretely, we pretrain a GNN, aligning its representations with token embeddings of an LLM. We then train a linear projector that transforms the GNN's representations into a fixed number of graph token embeddings without tuning the LLM. A unified instruction is designed for various graph tasks at different levels, such as node classification (node-level) and link prediction (edge-level). These design choices collectively enhance our method's effectiveness in zero-shot learning, setting it apart from existing methods. Experiments show that our graph token embeddings help the LLM predictor achieve state-of-the-art performance on unseen datasets and tasks compared to other methods using LLMs as predictors. Our code is available at https: //github. com/W-rudder/TEA-GLM.

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AAAI Conference 2018 Short Paper

Towards Experienced Anomaly Detector Through Reinforcement Learning

Chengqiang Huang
Yulei Wu
Yuan Zuo
Ke Pei
Geyong Min

This abstract proposes a time series anomaly detector which 1) makes no assumption about the underlying mechanism of anomaly patterns, 2) refrains from the cumbersome work of threshold setting for good anomaly detection performance under speciﬁc scenarios, and 3) keeps evolving with the growth of anomaly detection experience. Essentially, the anomaly detector is powered by the Recurrent Neural Network (RNN) and adopts the Reinforcement Learning (RL) method to achieve the self-learning process. Our initial experiments demonstrate promising results of using the detector in network time series anomaly detection problems.

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AAAI Conference 2017 Conference Paper

Collaborative Company Profiling: Insights from an Employee’s Perspective

Hao Lin
Hengshu Zhu
Yuan Zuo
Chen Zhu
Junjie Wu
Hui Xiong

Company proﬁling is an analytical process to build an indepth understanding of company’s fundamental characteristics. It serves as an effective way to gain vital information of the target company and acquire business intelligence. Traditional approaches for company proﬁling rely heavily on the availability of rich ﬁnance information about the company, such as ﬁnance reports and SEC ﬁlings, which may not be readily available for many private companies. However, the rapid prevalence of online employment services enables a new paradigm — to obtain the variety of company’s information from their employees’ online ratings and comments. This, in turn, raises the challenge to develop company pro- ﬁles from an employee’s perspective. To this end, in this paper, we propose a method named Company Proﬁling based Collaborative Topic Regression (CPCTR), for learning the latent structural patterns of companies. By formulating a joint optimization framework, CPCTR has the ability in collaboratively modeling both textual (e. g. , reviews) and numerical information (e. g. , salaries and ratings). Indeed, with the identi- ﬁed patterns, including the positive/negative opinions and the latent variable that inﬂuences salary, we can effectively carry out opinion analysis and salary prediction. Extensive experiments were conducted on a real-world data set to validate the effectiveness of CPCTR. The results show that our method provides a comprehensive understanding of company characteristics and delivers a more effective prediction of salaries than other baselines.

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