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Wenjun Wang

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13 papers

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AAMAS Conference 2026 Conference Paper

Pareto-guided Pipeline for Distilling Featherweight AI Agents in Mobile MOBA Games

Xionghui Yang
Bozhou Chen
Yunlong Lu
Yongyi Wang
Lingfeng Li
Lanxiao Huang
Lin Liu
Wenjun Wang

Recent advances in game AI have demonstrated the feasibility of training agents that surpass top-tier human professionals in complex environments such as Honor of Kings (HoK), a leading mobile multiplayer online battle arena (MOBA) game. However, deploying such powerful agents on mobile devices remains a major challenge. On one hand, the intricate multi-modal state representation and hierarchical action space of HoK demand large, sophisticated policy networks that are inherently difficult to compress into lightweight forms. On the other hand, production deployment requires highfrequency inference under strict energy and latency constraints on mobile platform. To the best of our knowledge, bridging largescale game AI and practical on-device deployment has not been systematically studied. In this work, we propose a Pareto optimality guided pipeline and design a high-efficiency student architecture search space tailored for mobile execution, enabling systematic exploration of the trade-off between performance and efficiency. Experimental results demonstrate that the distilled model achieves remarkable efficiency, including an 12. 4× faster inference speed (under 0. 5ms per frame) and a 15. 6× improvement in energy efficiency (under 0. 5mAh per game), while retaining a 40. 32% win rate against the original teacher model. FullVersion: Thefull version ofthispaper, including theappendix, is available on arXiv. 1 1https: //arxiv. org/abs/2602. 07521 This work is licensed under a Creative Commons Attribution International 4. 0 License. Proc. of the 25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026), C. Amato, L. Dennis, V. Mascardi, J. Thangarajah (eds.), May 25 – 29, 2026, Paphos, Cyprus. © 2026 International Foundation for Autonomous Agents and Multiagent Systems (www. ifaamas. org). https: //doi. org/10. 65109/HUOT2523

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IJCAI Conference 2025 Conference Paper

Dual Encoder Contrastive Learning with Augmented Views for Graph Anomaly Detection

Nannan Wu
Hongdou Dong
Wenjun Wang
Yiming Zhao

Graph anomaly detection (GAD), which aims to identify patterns that deviate significantly from normal nodes in attributed networks, is widely used in financial fraud, cybersecurity, and bioinformatics. The paradigms of jointly optimizing contrastive learning and reconstruction learning have shown significant potential in this field. However, when using GNNs as an encoder, it still faces the problem of over-smoothing, and it is difficult to effectively capture the fine-grain topology information of the graph. In this paper, we introduce an innovative approach: Dual Encoder Contrastive Learning with Augmented Views for Graph Anomaly Detection, named DECLARE. Specifically, the dual encoder integrates the strengths of GNNs and Graph Transformers to learn graph representation from multiple perspectives comprehensively. Although contrastive learning enhances the model's ability to learn discriminative features, it cannot directly identify anomalous patterns. To address this, the reconstruction module independently reconstructs graph structures and attributes, helping the model focus on learning the normal patterns of both structure and attributes. Through extensive experimental analysis, we demonstrate the superiority of DECLARE over the state-of-the-art baselines on six benchmark datasets.

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AAAI Conference 2025 Conference Paper

Federated Graph Anomaly Detection Through Contrastive Learning with Global Negative Pairs

Nannan Wu
Yazheng Zhao
Hongdou Dong
Keao Xi
Wei Yu
Wenjun Wang

Anomaly detection on attributed graphs has applications in various domains such as finance and email spam detection, thus gaining substantial attention. Distributed scenarios can also involve issues related to anomaly detection in attribute graphs, such as in medical scenarios. However, most of the existing anomaly detection methods are designed for centralized scenarios, and directly applying them to distributed settings may lead to reduced performance. One possible reason for this issue is that, when graph data are distributed across multiple clients, federated graph learning may struggle to fully exploit the potential of the dispersed data, leading to suboptimal performance. Building on this insight, we propose FedCLGN, a federated graph anomaly detection framework that leverages contrastive self-supervised learning. First, we put forward an augmentation method to maintain global negative pairs on the server. This involves identifying anomalous nodes using pseudo-labels, extracting embedding representations of the negative pairs corresponding to these anomalous nodes from clients, and uploading them to the server. Then, we adopt graph diffusion to enhance the feature representation of nodes, capturing the global structure and local connection patterns. This strategy can strengthen the differentiation between positive and negative instance pairs. Finally, the effectiveness of our approach is verified by experimental results on four real graph datasets.

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IJCAI Conference 2025 Conference Paper

Leveraging Personalized PageRank and Higher-Order Topological Structures for Heterophily Mitigation in Graph Neural Networks

Yumeng Wang
Zengyi Wo
Wenjun Wang
Xingcheng Fu
Minglai Shao

Graph Neural Networks (GNNs) excel in node classification tasks but often assume homophily, where connected nodes share similar labels. This assumption does not hold in many real-world heterophilic graphs. Existing models for heterophilic graphs primarily rely on pairwise relationships, overlooking multi-scale information from higher-order structures. This leads to suboptimal performance, particularly under noise from conflicting class information across nodes. To address these challenges, we propose HPGNN, a novel model integrating Higher-order Personalized PageRank with Graph Neural Networks. HPGNN introduces an efficient high-order approximation of Personalized PageRank (PPR) to capture long-range and multiscale node interactions. This approach reduces computational complexity and mitigates noise from surrounding information. By embedding higher-order structural information into convolutional networks, HPGNN effectively models key interactions across diverse graph dimensions. Extensive experiments on benchmark datasets demonstrate HPGNN’s effectiveness. The model achieves better performance than five out of seven state-of-the-art methods on heterophilic graphs in downstream tasks while maintaining competitive performance on homophilic graphs. HPGNN’s ability to balance multi-scale information and robustness to noise makes it a versatile solution for real-world graph learning challenges. Codes are available at https: //github. com/streetcorner/HPGNN.

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AAAI Conference 2025 Conference Paper

SECodec: Structural Entropy-based Compressive Speech Representation Codec for Speech Language Models

Linqin Wang
Yaping Liu
Zhengtao Yu
Shengxiang Gao
Cunli Mao
Yuxin Huang
Wenjun Wang
Ling Dong

With the rapid advancement of large language models (LLMs), discrete speech representations have become crucial for integrating speech into LLMs. Existing methods for speech representation discretization rely on a predefined codebook size and Euclidean distance-based quantization. However, 1) the size of codebook is a critical parameter that affects both codec performance and downstream task training efficiency. 2) The Euclidean distance-based quantization may lead to audio distortion when the size of the codebook is controlled within a reasonable range. In fact, in the field of information compression, structural information and entropy guidance are crucial, but previous methods have largely overlooked these factors. Therefore, we address the above issues from an information-theoretic perspective, we present SECodec, a novel speech representation codec based on structural entropy (SE) for building speech language models. Specifically, we first model speech as a graph, clustering the speech features nodes within the graph and extracting the corresponding codebook by hierarchically and disentangledly minimizing 2D SE. Then, to address the issue of audio distortion, we propose a new quantization method. This method still adheres to the 2D SE minimization principle, adaptively selecting the most suitable token corresponding to the cluster for each incoming original speech node. Furthermore, we develop a Structural Entropy-based Speech Language Model (SESLM) that leverages SECodec. Experimental results demonstrate that SECodec performs comparably to EnCodec in speech reconstruction, and SESLM surpasses VALL-E in zero-shot text-to-speech tasks.

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AIIM Journal 2024 Journal Article

A clinical consensus-compliant deep learning approach to quantitatively evaluate human in vitro fertilization early embryonic development with optical microscope images

Zaowen Liao
Chaoyu Yan
Jianbo Wang
Ningfeng Zhang
Huan Yang
Chenghao Lin
Haiyue Zhang
Wenjun Wang

The selection of embryos is a key for the success of in vitro fertilization (IVF). However, automatic quality assessment on human IVF embryos with optical microscope images is still challenging. In this study, we developed a clinical consensus-compliant deep learning approach, named Esava (Embryo Segmentation and Viability Assessment), to quantitatively evaluate the development of IVF embryos using optical microscope images. In total 551 optical microscope images of human IVF embryos of day-2 to day-3 were collected, preprocessed, and annotated. Using the Faster R-CNN model as baseline, our Esava model was constructed, refined, trained, and validated for precise and robust blastomere detection. A novel algorithm Crowd-NMS was proposed and employed in Esava to enhance the object detection and to precisely quantify the embryonic cells and their size uniformity. Additionally, an innovative GrabCut-based unsupervised module was integrated for the segmentation of blastomeres and embryos. Independently tested on 94 embryo images for blastomere detection, Esava obtained the high rates of 0. 9940, 0. 9121, and 0. 9531 for precision, recall, and mAP respectively, and gained significant advances compared with previous computational methods. Intraclass correlation coefficients indicated the consistency between Esava and three experienced embryologists. Another test on 51 extra images demonstrated that Esava surpassed other tools significantly, achieving the highest average precision 0. 9025. Moreover, it also accurately identified the borders of blastomeres with mIoU over 0. 88 on the independent testing dataset. Esava is compliant with the Istanbul clinical consensus and compatible to senior embryologists. Taken together, Esava improves the accuracy and efficiency of embryonic development assessment with optical microscope images.

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IJCAI Conference 2024 Conference Paper

Anomaly Subgraph Detection through High-Order Sampling Contrastive Learning

Ying Sun
Wenjun Wang
Nannan Wu
Chunlong Bao

Anomaly subgraph detection is a crucial task in various real-world applications, including identifying high-risk areas, detecting river pollution, and monitoring disease outbreaks. Early traditional graph-based methods can obtain high-precision detection results in scenes with small-scale graphs and obvious anomaly features. Most existing anomaly detection methods based on deep learning primarily concentrate on identifying anomalies at the node level, while neglecting to detect anomaly groups in the internal structure. In this paper, we propose a novel end-to-end Graph Neural Network (GNN) based anomaly subgraph detection approach(ASD-HC) in graph-structured data. 1)We propose a high-order neighborhood sampling strategy to construct our node and k-order neighbor-subgraph instance pairs. 2)Anomaly features of nodes are captured through a self-supervised contrastive learning model. 3) Detecting the maximum connected anomaly subgraph is performed by integrating the Non-parameter Graph Scan statistics and a Random Walk module. We evaluate ASD-HC against five state-of-the-art baselines using five benchmark datasets. ASD-HC outperforms the baselines by over 13. 01% in AUC score. Various experiments demonstrate that our approach effectively detects anomaly subgraphs within large-scale graphs.

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IJCAI Conference 2024 Conference Paper

Graph Collaborative Expert Finding with Contrastive Learning

Qiyao Peng
Wenjun Wang
Hongtao Liu
Cuiying Huo
Minglai Shao

In Community Question Answering (CQA) websites, most current expert finding methods often model expert embeddings from textual features and optimize them with expert-question first-order interactions, i. e. , this expert has answered this question. In this paper, we try to address the limitation of current models that typically neglect the intrinsic high-order connectivity within expert-question interactions, which is pivotal for collaborative effects. We introduce an innovative and simple approach: by conceptualizing expert-question interactions as a bipartite graph, and then we propose a novel graph-based expert finding method based on contrastive learning to effectively capture both first-order and intricate high-order connectivity, named CGEF. Specifically, we employ a question encoder to model questions from titles and employ the graph attention network to recursively propagate embeddings. Besides, to alleviate the problem of sparse interactions, we devise two auxiliary tasks to enhance expert modeling. First, we generate multiple views of one expert, including: 1) behavior-level augmentation drops interaction edges randomly in the graph; 2) interest-level augmentation randomly replaces question titles with tags in the graph. Then we maximize the agreement between one expert and the corresponding augmented expert on a specific view. In this way, the model can effectively inject collaborative signals into expert modeling. Extensive experiments on six CQA datasets demonstrate significant improvements compared with recent methods.

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AAAI Conference 2022 Conference Paper

Self-Supervised Object Localization with Joint Graph Partition

Yukun Su
Guosheng Lin
Yun Hao
Yiwen Cao
Wenjun Wang
Qingyao Wu

Object localization aims to generate a tight bounding box for the target object, which is a challenging problem that has been deeply studied in recent years. Since collecting bounding-box labels is time-consuming and laborious, many researchers focus on weakly supervised object localization (WSOL). As the recent appealing self-supervised learning technique shows its powerful function in visual tasks, in this paper, we take the early attempt to explore unsupervised object localization by self-supervision. Specifically, we adopt different geometric transformations to image and utilize their parameters as pseudo labels for self-supervised learning. Then, the classagnostic activation map is used to highlight the target object potential regions. However, such attention maps merely focus on the most discriminative part of the objects, which will affect the quality of the predicted bounding box. Based on the motivation that the activation maps of different transformations of the same image should be equivariant, we further design a siamese network that encodes the paired images and propose a joint graph partition mechanism in an unsupervised manner to enhance the object co-occurrent regions. To validate the effectiveness of the proposed method, extensive experiments are conducted on CUB-200-2011, Stanford Cars and FGVC-Aircraft datasets. Experimental results show that our method outperforms state-of-the-art methods using the same level of supervision, even outperforms some weaklysupervised methods.

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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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IS Journal 2020 Journal Article

A Deep Coupled LSTM Approach for USD/CNY Exchange Rate Forecasting

Wei Cao
Weidong Zhu
Wenjun Wang
Yves Demazeau
Chen Zhang

Forecasting CNY exchange rate accurately is a challenging task due to its complex coupling nature, which includes market-level coupling from interactions with multiple financial markets, macrolevel coupling from interactions with economic fundamentals, and deep coupling from interactions of the two aforementioned kinds of couplings. This study develops a new deep coupled long short-term memory (LSTM) approach, namely, DC-LSTM, to capture the complex couplings for USD/CNY exchange rate forecasting. In this approach, a deep structure consisting of stacked LSTMs is built to model the complex couplings. The experimental results with 10 years data indicate that the proposed approach significantly outperforms seven other benchmarks. The DC-LSTM is verified to be a useful tool to make wise investment decisions through a profitability discussion. The purpose in this article is to clarify the importance of coupling learning for exchange rate forecasting, and the usefulness of deep coupled model to capture the couplings.

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AAAI Conference 2019 Conference Paper

Uncovering Specific-Shape Graph Anomalies in Attributed Graphs

Nannan Wu
Wenjun Wang
Feng Chen
Jianxin Li
Bo Li
Jinpeng Huai

As networks are ubiquitous in the modern era, point anomalies have been changed to graph anomalies in terms of anomaly shapes. However, the specific-shape priors about anomalous subgraphs of interest are seldom considered by the traditional approaches when detecting the subgraphs in attributed graphs (e. g. , computer networks, Bitcoin networks, and etc.). This paper proposes a nonlinear approach to specific-shape graph anomaly detection. The nonlinear approach focuses on optimizing a broad class of nonlinear cost functions via specific-shape constraints in attributed graphs. Our approach can be used to many different graph anomaly settings. The traditional approaches can only support linear cost functions (e. g. , an aggregation function for the summation of node weights). However, our approach can employ more powerful nonlinear cost functions, and enjoys a rigorous theoretical guarantee on the near-optimal solution with the geometrical convergence rate.

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EAAI Journal 2015 Journal Article

A framework with reasoning capabilities for crisis response decision–support systems

Nady Slam
Wenjun Wang
Guixiang Xue
Pei Wang

This paper reviews the methods in decision–support systems for crisis management. While much research has been conducted in this field, little emphasis has been placed on the uncertainty representation, reasoning, learning and real time decision-making capabilities of system. The purpose of this paper is to explore the basic assumptions of constructing an intelligent decision–support system for crisis response management. A novel framework for crisis response decision-making system under the assumption of openness to various kinds of uncertainties, reasoning and learning with real-time response is proposed. We applied the Non-Axiomatic Logic in representing and reasoning the uncertainty knowledge in the framework and demonstrated the reasoning and learning mechanisms of the framework through an application in a case study in the field of urban firefighting. The results show that the framework provides a suitable model for intelligent crisis response decision support systems.

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