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Pengfei Jiao

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17 papers

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AIIM Journal 2026 Journal Article

Multi-annotation agreement and prediction consistency networks: Improving semi-supervised segmentation of medical images with ambiguous boundaries

Shuai Wang
Tengjin Weng
Jingyi Wang
Kai Zhao
Yang Shen
Zhidong Zhao
Yixiu Liu
Pengfei Jiao

Medical image segmentation annotations exhibit variations among experts due to the ambiguous boundaries of segmented objects and backgrounds in medical images. Although using multiple annotations for each image in the fully-supervised setting has been extensively studied for training deep models, obtaining a large amount of multi-annotated data is challenging due to the substantial time and manpower costs required for segmentation annotations, resulting in most images lacking any annotations. To address this, we propose Multi-annotated Semi-supervised Ensemble Networks (MSE-Nets) for learning segmentation from limited multi-annotated and abundant unannotated data. Specifically, we introduce the Network Pairwise Consistency Enhancement (NPCE) module and Multi-Network Pseudo Supervised (MNPS) module to enhance MSE-Nets for the segmentation task by considering two major factors: (1) to optimize the utilization of all accessible multi-annotated data, the NPCE separates (dis)agreement annotations of multi-annotated data at the pixel level and handles agreement and disagreement annotations in different ways; (2) to mitigate the introduction of imprecise pseudo-labels, the MNPS extends the training data by leveraging consistent pseudo-labels from unannotated data. Finally, we improve confidence calibration by averaging the predictions of base networks. Experiments on the ISIC dataset show that we reduced the demand for multi-annotated data by 97. 75% and narrowed the gap with the best fully-supervised baseline to just a Jaccard index of 3. 7%. Furthermore, compared to other semi-supervised methods that rely only on a single annotation or a combined fusion approach, the comprehensive experimental results on ISIC and RIGA datasets demonstrate the superior performance of our proposed method in medical image segmentation with ambiguous boundaries.

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AAAI Conference 2026 Conference Paper

Towards OOD Generalization in Dynamic Graphs via Causal Invariant Learning

Xinxun Zhang
Pengfei Jiao
Mengzhou Gao
Tianpeng Li
Xuan Guo

Although dynamic graph neural networks (DyGNNs) have demonstrated promising capabilities, most existing methods ignore out-of-distribution (OOD) shifts that commonly exist in dynamic graphs. Dynamic graph OOD generalization is non-trivial due to the following challenges: 1) Identifying invariant and variant patterns amid complex graph evolution, 2) Capturing the intrinsic evolution rationale from these patterns, and 3) Ensuring model generalization across diverse OOD shifts despite limited data distribution observations. Although several attempts have been made to tackle these challenges, none has successfully addressed all three simultaneously, and they face various limitations in complex OOD scenarios. To solve these issues, we propose a Dynamic graph Causal Invariant Learning (DyCIL) model for OOD generalization via exploiting invariant spatio-temporal patterns from a causal view. Specifically, we first develop a dynamic causal subgraph generator to identify causal dynamic subgraphs explicitly. Next, we design a causal-aware spatio-temporal attention module to extract the intrinsic evolution rationale behind invariant patterns. Finally, we further introduce an adaptive environment generator to capture the underlying dynamics of distributional shifts. Extensive experiments on both real-world and synthetic dynamic graph datasets demonstrate the superiority of our model over state-of-the-art baselines in handling OOD shifts.

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IJCAI Conference 2025 Conference Paper

A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities

Pengfei Jiao
Hongjiang Chen
Xuan Guo
Zhidong Zhao
Dongxiao He
Di Jin

Temporal interaction graphs (TIGs), defined by sequences of timestamped interaction events, have become ubiquitous in real-world applications due to their capability to model complex dynamic system behaviors. As a result, temporal interaction graph representation learning (TIGRL) has garnered significant attention in recent years. TIGRL aims to embed nodes in TIGs into low-dimensional representations that effectively preserve both structural and temporal information, thereby enhancing the performance of downstream tasks such as classification, prediction, and clustering within constantly evolving data environments. In this paper, we begin by introducing the foundational concepts of TIGs and emphasizing the critical role of temporal dependencies. We then propose a comprehensive taxonomy of state-of-the-art TIGRL methods, systematically categorizing them based on the types of information utilized during the learning process to address the unique challenges inherent to TIGs. To facilitate further research and practical applications, we curate the source of datasets and benchmarks, providing valuable resources for empirical investigations. Finally, we examine key open challenges and explore promising research directions in TIGRL, laying the groundwork for future advancements that have the potential to shape the evolution of this field.

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EAAI Journal 2025 Journal Article

A two-stage model for unified sentence- and document-level biomedical event extraction

Fangfang Su
Yue Zhang
Pengfei Jiao
Zhidong Zhao
Bobo Li
Fei Li
Donghong Ji

Biomedical event extraction, a cornerstone of information extraction, has increasingly attracted attention within the biomedical research community. Moreover, it is a highly complex task, which not only deals with many sub-tasks but also involves nested events. Currently, the research on biomedical event extraction, whether pipelined model or joint method, needs to be processed for each sub-task. The process of processing each sub-task one by one lead to the degradation of event extraction performance. In addition, most studies focus on extracting sentence-level events and ignore cross-sentence event information. To solve these problems, we simplify the process of event extraction, reduce the processing steps, and combine the two sub-tasks of relation extraction and argument combination as one sub-task. In addition, we consider document-level event extraction, which not only extracts cross-sentence events but also considers broader context information. Experimental results indicate that our novel approach outperforms prior studies. Additionally, the document-level event extraction model attains the top performance on the BioNLP’11 test data and achieves near-leading performance on the BioNLP’13 test data.

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IJCAI Conference 2025 Conference Paper

FedCCH: Automatic Personalized Graph Federated Learning for Inter-Client and Intra-Client Heterogeneity

Pengfei Jiao
Zian Zhou
Meiting Xue
Huijun Tang
Zhidong Zhao
Huaming Wu

Graph federated learning (GFL) is increasingly utilized in domains such as social network analysis and recommendation systems, where non-IID data exist extensively and necessitate a strong emphasis on personalized learning. However, existing methods focus only on the personality among different clients instead of the personality within a client which widely exists in the real social networks, where intra-client personality addresses the heterogeneity of known data, while inter-client personality always tackle client heterogeneity under privacy constraint. In this paper, we propose a novel automatic personalized graph federated learning (PGFL) scheme named FedCCH to capture both inter-client and intra-client heterogeneity. For intra-client heterogeneity, we innovatively propose the learnable Personalized Factor (PF) to automatically normalize each graph representation within clients by learnable parameters, which weakens the impact of non-IID data distribution. For inter-client heterogeneity, we propose a novel hash-based similarity clustering method to generate the hash signature for each client, and then group similar clients for joint training among different clients. Ultimately, we collaboratively train intra-client and inter-client modules to improve the effectiveness of capturing the heterogeneity of the graph data of clients. Experiment results demonstrate that FedCCH outperforms other state-of-the-art baseline methods.

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AAAI Conference 2025 Conference Paper

GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causality

Zehao Liu
Mengzhou Gao
Pengfei Jiao

Multivariate time series anomaly detection has numerous real-world applications and is being extensively studied. Modeling pairwise correlations between variables is crucial. Existing methods employ learnable graph structures and graph neural networks to explicitly model the spatial dependencies between variables. However, these methods are primarily based on prediction or reconstruction tasks, which can only learn similarity relationships between sequence embeddings and lack interpretability in how graph structures affect time series evolution. In this paper, we designed a framework that models spatial dependencies using interpretable causal relationships and detects anomalies through changes in causal patterns. Specifically, we propose a method to dynamically discover Granger causality using gradients in nonlinear deep predictors and employ a simple sparsification strategy to obtain a Granger causality graph, detecting anomalies from a causal perspective. Experiments on real-world datasets demonstrate that the proposed model achieves more accurate anomaly detection compared to baseline methods.

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IJCAI Conference 2025 Conference Paper

HeTa: Relation-wise Heterogeneous Graph Foundation Attack Model

Yuling Wang
Zihui Chen
Pengfei Jiao
Xiao Wang

Heterogeneous Graph Neural Networks (HGNNs) are vulnerable, highlighting the need for tailored attacks to assess their robustness and ensure security. However, existing HGNN attacks often require complex retraining of parameters to generate specific perturbations for new scenarios. Recently, foundation models have opened new horizons for the generalization of graph neural networks by capturing shared semantics across various graph distributions. This leads us to ask: Can we design a foundation attack model for HGNNs that enables generalizable perturbations across different HGNNs, and quickly adapts to new heterogeneous graphs (HGs)? Empirical findings reveal that, despite significant differences in model design and parameter space, different HGNNs surprisingly share common vulnerability patterns from a relation-aware perspective. Therefore, we explore how to design foundation HGNN attack criteria by mining shared attack units. In this paper, we propose a novel relation-wise heterogeneous graph foundation attack model, HeTa. We introduce a foundation surrogate model to align heterogeneity and identify the importance of shared relation-aware attack units. Building on this, we implement a serialized relation-by-relation attack based on the identified relational weights. In this way, the perturbation can be transferred to various target HGNNs and easily fine-tuned for new HGs. Extensive experiments exhibit powerful attack performances and generalizability of our method.

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IJCAI Conference 2025 Conference Paper

Heterogeneous Temporal Hypergraph Neural Network

Huan Liu
Pengfei Jiao
Mengzhou Gao
Chaochao Chen
Di Jin

Graph representation learning (GRL) has emerged as an effective technique for modeling graph-structured data. When modeling heterogeneity and dynamics in real-world complex networks, GRL methods designed for complex heterogeneous temporal graphs (HTGs) have been proposed and have achieved successful applications in various fields. However, most existing GRL methods mainly focus on preserving the low-order topology information while ignoring higher-order group interaction relationships, which are more consistent with real-world networks. In addition, most existing hypergraph methods can only model static homogeneous graphs, limiting their ability to model high-order interactions in HTGs. Therefore, to simultaneously enable the GRL model to capture high-order interaction relationships in HTGs, we first propose a formal definition of heterogeneous temporal hypergraphs and P-uniform heterogeneous hyperedge construction algorithm that does not rely on additional information. Then, a novel Heterogeneous Temporal HyperGraph Neural network (HTHGN), is proposed to fully capture higher-order interactions in HTGs. HTHGN contains a hierarchical attention mechanism module that simultaneously performs temporal message-passing between heterogeneous nodes and hyperedges to capture rich semantics in a wider receptive field brought by hyperedges. Furthermore, HTHGN performs contrastive learning by maximizing the consistency between low-order correlated heterogeneous node pairs on HTG to avoid the low-order structural ambiguity issue. Detailed experimental results on three real-world HTG datasets verify the effectiveness of the proposed HTHGN for modeling high-order interactions in HTGs and demonstrate significant performance improvements.

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IJCAI Conference 2025 Conference Paper

HGMP: Heterogeneous Graph Multi-Task Prompt Learning

Pengfei Jiao
Jialong Ni
Di Jin
Xuan Guo
Huan Liu
Hongjiang Chen
Yanxian Bi

The pre-training and fine-tuning methods have gained widespread attention in the field of heterogeneous graph neural networks due to their ability to leverage large amounts of unlabeled data during the pre-training phase, allowing the model to learn rich structural features. However, these methods face the issue of a mismatch between the pre-trained model and downstream tasks, leading to suboptimal performance in certain application scenarios. Prompt learning methods have emerged as a new direction in heterogeneous graph tasks, as they allow flexible adaptation of task representations to address target inconsistency. Building on this idea, this paper proposes a novel multi-task prompt framework for the heterogeneous graph domain, named HGMP. First, to bridge the gap between the pre-trained model and downstream tasks, we reformulate all downstream tasks into a unified graph-level task format. Next, we address the limitations of existing graph prompt learning methods, which struggle to integrate contrastive pre-training strategies in the heterogeneous graph domain. We design a graph-level contrastive pre-training strategy to better leverage heterogeneous information and enhance performance in multi-task scenarios. Finally, we introduce heterogeneous feature prompts, which enhance model performance by refining the representation of input graph features. Experimental results on public datasets show that our proposed method adapts well to various tasks and significantly outperforms baseline methods.

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AAAI Conference 2024 Conference Paper

Enhancing Multi-Scale Diffusion Prediction via Sequential Hypergraphs and Adversarial Learning

Pengfei Jiao
Hongqian Chen
Qing Bao
Wang Zhang
Huaming Wu

Information diffusion prediction plays a crucial role in understanding the propagation of information in social networks, encompassing both macroscopic and microscopic prediction tasks. Macroscopic prediction estimates the overall impact of information diffusion, while microscopic prediction focuses on identifying the next user to be influenced. While prior research often concentrates on one of these aspects, a few tackle both concurrently. These two tasks provide complementary insights into the diffusion process at different levels, revealing common traits and unique attributes. The exploration of leveraging common features across these tasks to enhance information prediction remains an underexplored avenue. In this paper, we propose an intuitive and effective model that addresses both macroscopic and microscopic prediction tasks. Our approach considers the interactions and dynamics among cascades at the macro level and incorporates the social homophily of users in social networks at the micro level. Additionally, we introduce adversarial training and orthogonality constraints to ensure the integrity of shared features. Experimental results on four datasets demonstrate that our model significantly outperforms state-of-the-art methods.

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AAAI Conference 2022 Conference Paper

Block Modeling-Guided Graph Convolutional Neural Networks

Dongxiao He
Chundong Liang
Huixin Liu
Mingxiang Wen
Pengfei Jiao
Zhiyong Feng

Graph Convolutional Network (GCN) has shown remarkable potential of exploring graph representation. However, the GCN aggregating mechanism fails to generalize to networks with heterophily where most nodes have neighbors from different classes, which commonly exists in real-world networks. In order to make the propagation and aggregation mechanism of GCN suitable for both homophily and heterophily (or even their mixture), we introduce block modeling into the framework of GCN so that it can realize “blockguided classified aggregation”, and automatically learn the corresponding aggregation rules for neighbors of different classes. By incorporating block modeling into the aggregation process, GCN is able to aggregate information from homophilic and heterophilic neighbors discriminately according to their homophily degree. We compared our algorithm with state-of-art methods which deal with the heterophily problem. Empirical results demonstrate the superiority of our new approach over existing methods in heterophilic datasets while maintaining a competitive performance in homophilic datasets.

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AAAI Conference 2021 Conference Paper

Hybrid-order Stochastic Block Model

Xunxun Wu
Chang-Dong Wang
Pengfei Jiao

Community detection is a research hotspot in machine learning and data mining. However, most of the existing community detection methods only rely on the lower-order connectivity patterns, while ignoring the higher-order connectivity patterns, and unable to capture the building blocks of the complex network. In recent years, some community detection methods based on higher-order structures have been developed, but they mainly focus on the motif network composed of higher-order structures, which violate the original lowerorder topological structure and are affected by the fragmentation issue, resulting in the deviation of community detection results. Therefore, there is still a lack of community detection methods that can effectively utilize higher-order connectivity patterns and lower-order connectivity patterns. To overcome the above limitations, this paper proposes the Hybridorder Stochastic Block Model (HSBM) from the perspective of the generative model. Based on the classical stochastic block model, the generation of lower-order structure and higher-order structure of the network is modeled uniformly, and the original topological properties of the network are maintained while using higher-order connectivity patterns. At the same time, a heuristic algorithm for community detection is proposed to optimize the objective function. Extensive experiments on six real-world datasets show that the proposed method outperforms the existing approaches.

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IJCAI Conference 2021 Conference Paper

Learning Stochastic Equivalence based on Discrete Ricci Curvature

Xuan Guo
Qiang Tian
Wang Zhang
Wenjun Wang
Pengfei Jiao

Role-based network embedding methods aim to preserve node-centric connectivity patterns, which are expressions of node roles, into low-dimensional vectors. However, almost all the existing methods are designed for capturing a relaxation of automorphic equivalence or regular equivalence. They may be good at structure identification but could show poorer performance on role identification. Because automorphic equivalence and regular equivalence strictly tie the role of a node to the identities of all its neighbors. To mitigate this problem, we construct a framework called Curvature-based Network Embedding with Stochastic Equivalence (CNESE) to embed stochastic equivalence. More specifically, we estimate the role distribution of nodes based on discrete Ricci curvature for its excellent ability to concisely representing local topology. We use a Variational Auto-Encoder to generate embeddings while a degree-guided regularizer and a contrastive learning regularizer are leveraged to improving both its robustness and discrimination ability. The effectiveness of our proposed CNESE is demonstrated by extensive experiments on real-world networks.

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IJCAI Conference 2021 Conference Paper

Self-Guided Community Detection on Networks with Missing Edges

Dongxiao He
Shuai Li
Di Jin
Pengfei Jiao
Yuxiao Huang

The vast majority of community detection algorithms assume that the networks are totally observed. However, in reality many networks cannot be fully observed. On such network is edges-missing network, where some relationships (edges) between two entities are missing. Recently, several works have been proposed to solve this problem by combining link prediction and community detection in a two-stage method or in a unified framework. However, the goal of link prediction, which is to predict as many correct edges as possible, is not consistent with the requirement for predicting the important edges for discovering community structure on edges-missing networks. Thus, combining link prediction and community detection cannot work very well in terms of detecting community structure for edges-missing network. In this paper, we propose a community self-guided generative model which jointly completes the edges-missing network and identifies communities. In our new model, completing missing edges and identifying communities are not isolated but closely intertwined. Furthermore, we developed an effective model inference method that combines a nested Expectation-Maximization (EM) algorithm and Metropolis-Hastings Sampling. Extensive experiments on real-world edges-missing networks show that our model can effectively detect community structures while completing missing edges.

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TIST Journal 2020 Journal Article

Modeling with Node Popularities for Autonomous Overlapping Community Detection

Di Jin
Bingyi Li
Pengfei Jiao
Dongxiao He
Hongyu Shan
Weixiong Zhang

Overlapping community detection has triggered recent research in network analysis. One of the promising techniques for finding overlapping communities is the popular stochastic models, which, unfortunately, have some common drawbacks. They do not support an important observation that highly connected nodes are more likely to reside in the overlapping regions of communities in the network. These methods are in essence not truly unsupervised, since they require a threshold on probabilistic memberships to derive overlapping structures and need the number of communities to be specified a priori. We develop a new method to address these issues for overlapping community detection. We first present a stochastic model to accommodate the relative importance and the expected degree of every node in each community. We then infer every overlapping community by ranking the nodes according to their importance. Second, we determine the number of communities under the Bayesian framework. We evaluate our method and compare it with five state-of-the-art methods. The results demonstrate the superior performance of our method. We also apply this new method to two applications, showing its superb performance on practical problems.

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IJCAI Conference 2019 Conference Paper

Network-Specific Variational Auto-Encoder for Embedding in Attribute Networks

Di Jin
Bingyi Li
Pengfei Jiao
Dongxiao He
Weixiong Zhang

Network embedding (NE) maps a network into a low-dimensional space while preserving intrinsic features of the network. Variational Auto-Encoder (VAE) has been actively studied for NE. These VAE-based methods typically utilize both network topologies and node semantics and treat these two types of data in the same way. However, the information of network topology and information of node semantics are orthogonal and are often from different sources; the former quantifies coupling relationships among nodes, whereas the latter represents node specific properties. Ignoring this difference affects NE. To address this issue, we develop a network-specific VAE for NE, named as NetVAE. In the encoding phase of our new approach, compression of network structures and compression of node attributes share the same encoder in order to perform co-training to achieve transfer learning and information integration. In the decoding phase, a dual decoder is introduced to reconstruct network topologies and node attributes separately. Specifically, as a part of the dual decoder, we develop a novel method based on a Gaussian mixture model and the block model to reconstruct network structures. Extensive experiments on large real-world networks demonstrate a superior performance of the new approach over the state-of-the-art methods.

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IJCAI Conference 2018 Conference Paper

Finding Communities with Hierarchical Semantics by Distinguishing General and Specialized topics

Ge Zhang
Di Jin
Jian Gao
Pengfei Jiao
Françoise Fogelman-Soulié
Xin Huang

Using network topology and semantic contents to find topic-related communities is a new trend in the field of community detection. By analyzing texts in social networks, we find that topics in networked contents are often hierarchical. In most cases, they have a two-level semantic structure with general and specialized topics, to respectively denote common and specific interests of communities. However, the existing community detection methods ignore such a hierarchy and take all words used to describe node semantics from an identical perspective. This indiscriminate use of words leads to natural defects in depicting networked content in which the deep semantics is not fully utilized. To address this problem, we propose a novel probabilistic generative model. By distinguishing the general and specialized topics of words, our model not only can find community structures more accurately, but also provide two-level semantic interpretation for each community. We train the model by deriving an efficient inference method under the framework of variational expectation-maximization. We provide a case study to show the ability of our algorithm in deep semantic interpretability of communities. The superiority of our algorithm for community detection is further demonstrated in comparison with eight state-of-the-art algorithms on eight real-world networks.

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