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Xixun Lin

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

Breaking One-Size-Fits-All: Revisiting Out-of-Distribution Detection on Graphs Under Diverse Distribution Shifts

Chuancheng Song
Hanyang Shen
Yan Dong
Xixun Lin
Yanmin Shang
Yanan Cao

Graph OOD detection is crucial in open-world scenarios, where OOD samples may manifest in diverse forms such as open-set deviations, feature-similar shifts, and structural anomalies, each exhibiting distinct geometric characteristics. However, most existing methods adopt a one-size-fits-all geometric assumption (typically Euclidean space), which inadequately captures the diverse nature of real-world distribution shifts. Therefore, adaptively selecting geometric spaces according to the properties of OOD samples is critical for their effective representation and reliable identification. Motivated by this, we revisit the graph OOD detection task under diverse distribution shifts and propose UniGOD, a unified framework serving as a graph foundation model for this task. UniGOD comprises two core modules: GeoUP and DynEVO. GeoUP module adaptively perceives the geometric space (such as Euclidean, hyperbolic, and hyperspherical space) by learning the curvature k of Riemannian manifolds. DynEVO module leverages the dynamic nature of neural SDEs to reveal pronounced uncertainty differences between ID/OOD samples, which are reflected in the divergent evolutionary trajectories of node embeddings induced by k-GNN iterations. With the geometry-dynamics coupling mechanism of the above two modules, UniGOD effectively captures the diverse distribution shifts. Extensive experiments demonstrate its superior performance over existing SOTA methods.

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AAAI Conference 2026 Conference Paper

PathMind: A Retrieve-Prioritize-Reason Framework for Knowledge Graph Reasoning with Large Language Models

Yu Liu
Xixun Lin
Yanmin Shang
Yangxi Li
Shi Wang
Yanan Cao

Knowledge graph reasoning (KGR) is the task of inferring new knowledge by performing logical deductions on knowledge graphs. Recently, large language models (LLMs) have demonstrated remarkable performance in complex reasoning tasks. Despite promising success, current LLM-based KGR methods still face two critical limitations. First, existing methods often extract reasoning paths indiscriminately, without assessing their different importance, which may introduce irrelevant noise that misleads LLMs. Second, while some methods leverage LLMs to dynamically explore potential reasoning paths, they require high retrieval demands and frequent LLM calls. To address these limitations, we propose PathMind, a novel framework designed to enhance faithful and interpretable reasoning by selectively guiding LLMs with important reasoning paths. Specifically, PathMind follows a "Retrieve-Prioritize-Reason" paradigm. First, it retrieves a query subgraph from KG through the retrieval module. Next, it introduces a path prioritization mechanism that identifies important reasoning paths using a semantic-aware path priority function, which simultaneously considers the accumulative cost and the estimated future cost for reaching the target. Finally, PathMind generates accurate and logically consistent responses via a dual-phase training strategy, including task-specific instruction tuning and path-wise preference alignment. Extensive experiments on benchmark datasets demonstrate that PathMind consistently outperforms competitive baselines, particularly on complex reasoning tasks with fewer input tokens, by identifying essential reasoning paths.

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ICML Conference 2025 Conference Paper

Conformal Anomaly Detection in Event Sequences

Shuai Zhang 0007
Chuan Zhou 0001
Yang Liu 0320
Peng Zhang 0001
Xixun Lin
Shirui Pan

Anomaly detection in continuous-time event sequences is a crucial task in safety-critical applications. While existing methods primarily focus on developing a superior test statistic, they fail to provide guarantees regarding the false positive rate (FPR), which undermines their reliability in practical deployments. In this paper, we propose CADES (Conformal Anomaly Detection in Event Sequences), a novel test procedure based on conformal inference for the studied task with finite-sample FPR control. Specifically, by using the time-rescaling theorem, we design two powerful non-conformity scores tailored to event sequences, which exhibit complementary sensitivities to different abnormal patterns. CADES combines these scores with Bonferroni correction to leverage their respective strengths and addresses non-identifiability issues of existing methods. Theoretically, we prove the validity of CADES and further provide strong guarantees on calibration-conditional FPR control. Experimental results on synthetic and real-world datasets, covering various types of anomalies, demonstrate that CADES outperforms state-of-the-art methods while maintaining FPR control.

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ECAI Conference 2025 Conference Paper

RANA: Robust Active Learning for Noisy Network Alignment

Yixuan Nan
Xixun Lin
Yanmin Shang
Zhuofan Li
Can Zhao
Yanan Cao 0001

Network alignment has attracted widespread attention in various fields. However, most existing works mainly focus on the problem of label sparsity, while overlooking the issue of noise in network alignment, which can substantially undermine model performance. Such noise mainly includes structural noise from noisy edges and labeling noise caused by human-induced and process-driven errors. To address these problems, we propose RANA, a Robust Active learning framework for noisy Network Alignment. RANA effectively tackles both structure noise and label noise while addressing the sparsity of anchor link annotations, which can improve the robustness of network alignment models. Specifically, RANA introduces the proposed Noise-aware Selection Module and the Label Denoising Module to address structural noise and labeling noise, respectively. In the first module, we design a noise-aware maximization objective to select node pairs, incorporating a cleanliness score to address structural noise. In the second module, we propose a novel multi-source fusion denoising strategy that leverages model and twin node pairs labeling to provide more accurate labels for node pairs. Empirical results on three real-world datasets demonstrate that RANA outperforms state-of-the-art active learning-based methods in alignment accuracy. Our code is available at https: //github. com/YXNan0110/RANA.

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AAAI Conference 2025 Conference Paper

UniFORM: Towards Unified Framework for Anomaly Detection on Graphs

Chuancheng Song
Xixun Lin
Hanyang Shen
Yanmin Shang
Yanan Cao

Graph anomaly detection has attracted significant attention due to its critical applications, such as identifying money laundering in financial systems and detecting fake reviews on social networks. However, two major challenges persist: (1) anomaly detection at the node, edge, and graph levels is often addressed in isolation, hindering the integration of complementary information to identify anomalies arising from collective behaviors; and (2) the inherent label sparsity in graph data, coupled with the difficulty of obtaining high-quality annotations, exacerbates bias in detection. To address these challenges, we propose UniFORM, a unified self-supervised anomaly detection framework comprising two modules: UIO and UMC. UIO unifies node-, edge-, and graph-level tasks from a subgraph perspective, leveraging an energy-based GNN for iterative multi-granular anomaly detection. UMC enhances meta-learning through contrastive learning and employs Langevin dynamics to generate phantom samples as substitutes for anomalous data, reducing reliance on labeled data. Extensive experiments on real-world datasets demonstrate that UniFORM significantly outperforms state-of-the-art methods across multiple granularities.

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ICML Conference 2024 Conference Paper

Graph Neural Stochastic Diffusion for Estimating Uncertainty in Node Classification

Xixun Lin
Wenxiao Zhang
Fengzhao Shi
Chuan Zhou 0001
Lixin Zou
Xiangyu Zhao 0001
Dawei Yin 0001
Shirui Pan

Graph neural networks (GNNs) have advanced the state of the art in various domains. Despite their remarkable success, the uncertainty estimation of GNN predictions remains under-explored, which limits their practical applications especially in risk-sensitive areas. Current works suffer from either intractable posteriors or inflexible prior specifications, leading to sub-optimal empirical results. In this paper, we present graph neural stochastic diffusion (GNSD), a novel framework for estimating predictive uncertainty on graphs by establishing theoretical connections between GNNs and stochastic partial differential equation. GNSD represents a GNN-based parameterization of the proposed graph stochastic diffusion equation which includes a $Q$-Wiener process to model the stochastic evolution of node representations. GNSD introduces a drift network to guarantee accurate prediction and a stochastic forcing network to model the propagation of epistemic uncertainty among nodes. Extensive experiments are conducted on multiple detection tasks, demonstrating that GNSD yields the superior performance over existing strong approaches.

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ICML Conference 2024 Conference Paper

Neural Jump-Diffusion Temporal Point Processes

Shuai Zhang 0007
Chuan Zhou 0001
Yang Liu 0320
Peng Zhang 0001
Xixun Lin
Zhiming Ma

We present a novel perspective on temporal point processes (TPPs) by reformulating their intensity processes as solutions to stochastic differential equations (SDEs). In particular, we first prove the equivalent SDE formulations of several classical TPPs, including Poisson processes, Hawkes processes, and self-correcting processes. Based on these proofs, we introduce a unified TPP framework called Neural Jump-Diffusion Temporal Point Process (NJDTPP), whose intensity process is governed by a neural jump-diffusion SDE (NJDSDE) where the drift, diffusion, and jump coefficient functions are parameterized by neural networks. Compared to previous works, NJDTPP exhibits model flexibility in capturing intensity dynamics without relying on any specific functional form, and provides theoretical guarantees regarding the existence and uniqueness of the solution to the proposed NJDSDE. Experiments on both synthetic and real-world datasets demonstrate that NJDTPP is capable of capturing the dynamics of intensity processes in different scenarios and significantly outperforms the state-of-the-art TPP models in prediction tasks.

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