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Meng Jiang

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18 papers
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18

NeurIPS Conference 2025 Conference Paper

Learning Repetition-Invariant Representations for Polymer Informatics

  • Yihan Zhu
  • Gang Liu
  • Eric Inae
  • Tengfei Luo
  • Meng Jiang

Polymers are large macromolecules composed of repeating structural units known as monomers and are widely applied in fields such as energy storage, construction, medicine, and aerospace. However, existing graph neural network methods, though effective for small molecules, only model the single unit of polymers and fail to produce consistent vector representations for the true polymer structure with varying numbers of units. To address this challenge, we introduce Graph Repetition Invariance (GRIN), a novel method to learn polymer representations that are invariant to the number of repeating units in their graph representations. GRIN integrates a graph-based maximum spanning tree alignment with repeat-unit augmentation to ensure structural consistency. We provide theoretical guarantees for repetition‐invariance from both model and data perspectives, demonstrating that three repeating units are the minimal augmentation required for optimal invariant representation learning. GRIN outperforms state-of-the-art baselines on both homopolymer and copolymer benchmarks, learning stable, repetition-invariant representations that generalize effectively to polymer chains of unseen sizes.

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TMLR Journal 2025 Journal Article

Leopard: A Vision Language Model for Text-Rich Multi- Image Tasks

  • Mengzhao Jia
  • Wenhao Yu
  • Kaixin Ma
  • Tianqing Fang
  • Zhihan Zhang
  • Siru Ouyang
  • Hongming Zhang
  • Dong Yu

Text-rich images, where text serves as the central visual element guiding the overall understanding, are prevalent in real-world applications, such as presentation slides, scanned documents, and webpage snapshots. Tasks involving multiple text-rich images are especially challenging, as they require not only understanding the content of individual images but reasoning about inter-relationships and logical flows across multiple visual inputs. Despite the importance of these scenarios, current multimodal large language models (MLLMs) struggle to handle such tasks due to two key challenges: (1) the scarcity of high-quality instruction tuning datasets for text-rich multi-image scenarios, and (2) the difficulty in balancing image resolution with visual feature sequence length. To address these challenges, we propose Leopard, a MLLM designed specifically for handling vision-language tasks involving multiple text-rich images. First, we curated about one million high-quality multimodal instruction-tuning data, tailored to text-rich, multi-image scenarios. Second, we proposed an adaptive high-resolution multi-image encoding module to dynamically optimize the allocation of visual sequence length based on the original aspect ratios and resolutions of images. Experiments on a diverse set of benchmarks reveal that our model consistently outperforms state-of-the-art systems, such as Llama-3.2 and Qwen2-VL, in challenging text-rich, multi-image evaluations. Remarkably, our approach achieves outstanding performance using only 1.2M fully open-sourced training instances, outperforming models that rely on large-scale in-house data, highlighting its efficiency and effectiveness. Our code and data are available at https://anonymous.4open.science/r/Leopard-908F.

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ICLR Conference 2025 Conference Paper

Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning

  • Gang Liu
  • Michael Sun
  • Wojciech Matusik
  • Meng Jiang
  • Jie Chen 0007

While large language models (LLMs) have integrated images, adapting them to graphs remains challenging, limiting their applications in materials and drug design. This difficulty stems from the need for coherent autoregressive generation across texts and graphs. To address this, we introduce Llamole, the first multimodal LLM capable of interleaved text and graph generation, enabling molecular inverse design with retrosynthetic planning. Llamole integrates a base LLM with the Graph Diffusion Transformer and Graph Neural Networks for multi-conditional molecular generation and reaction inference within texts, while the LLM, with enhanced molecular understanding, flexibly controls activation among the different graph modules. Additionally, Llamole integrates A* search with LLM-based cost functions for efficient retrosynthetic planning. We create benchmarking datasets and conduct extensive experiments to evaluate Llamole against in-context learning and supervised fine-tuning. Llamole significantly outperforms 14 adapted LLMs across 12 metrics for controllable molecular design and retrosynthetic planning. Code and model at https://github.com/liugangcode/Llamole.

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AAAI Conference 2025 Conference Paper

Pre-trained Behavioral Model for Malicious User Prediction on Social Platform

  • Meng Jiang
  • Wenjie Wang
  • Shaofeng Hu
  • Kaishen Ou
  • Zhenjing Zheng
  • Fuli Feng

The proliferation of malicious users on social platforms poses significant financial and psychological threats, with activities ranging from scams to the dissemination of illicit content. Existing malicious user prediction comprises supervised and self-supervised learning methods. However, the former relies on extensive labeled malicious users for training, while the latter typically focuses on one form of malicious activity and depends heavily on manually crafted rules and features during pre-training. Moreover, existing pre-training methods fail to effectively capture the crucial repetitive and sporadic behavior patterns of malicious users. To address these limitations, we propose a Malicious User Behavior Pre-training framework (MaP) to build pre-trained behavior models. MaP integrates malicious pattern recognition with behavior consistency augmentation and local disruption augmentation strategies for contrastive learning to capture repetitive and sporadic malicious patterns, respectively. We instantiate MaP on a billion-level behavior pre-training scenario within an industry context. Both online and offline evaluations validate the superior performance of MaP in malicious user detection and classification.

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AAAI Conference 2024 Conference Paper

Get an A in Math: Progressive Rectification Prompting

  • Zhenyu Wu
  • Meng Jiang
  • Chao Shen

Chain-of-Thought (CoT) prompting methods have enabled large language models (LLMs) to generate reasoning paths and solve math word problems (MWPs). However, they are sensitive to mistakes in the paths, as any mistake can result in an incorrect answer. We propose a novel method named Progressive Rectification Prompting (PRP) to improve average accuracy on eight MWP datasets from 77.3 to 90.5. Given an initial answer from CoT, PRP iterates a verify-then-rectify process to progressively identify incorrect answers and rectify the reasoning paths. With the most likely correct answer, the LLM predicts a masked numerical value in the question; if the prediction does not match the masked value, the answer is likely incorrect. Then the LLM is prompted to re-generate the reasoning path hinted with a set of incorrect answers to prevent itself from repeating previous mistakes. PRP achieves the best performance compared against the CoT methods. Our implementation is made publicly available at https://wzy6642.github.io/prp.github.io/.

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NeurIPS Conference 2024 Conference Paper

Graph Diffusion Transformers for Multi-Conditional Molecular Generation

  • Gang Liu
  • Jiaxin Xu
  • Tengfei Luo
  • Meng Jiang

Inverse molecular design with diffusion models holds great potential for advancements in material and drug discovery. Despite success in unconditional molecule generation, integrating multiple properties such as synthetic score and gas permeability as condition constraints into diffusion models remains unexplored. We present the Graph Diffusion Transformer (Graph DiT) for multi-conditional molecular generation. Graph DiT has a condition encoder to learn the representation of numerical and categorical properties and utilizes a Transformer-based graph denoiser to achieve molecular graph denoising under conditions. Unlike previous graph diffusion models that add noise separately on the atoms and bonds in the forward diffusion process, we propose a graph-dependent noise model for training Graph DiT, designed to accurately estimate graph-related noise in molecules. We extensively validate the Graph DiT for multi-conditional polymer and small molecule generation. Results demonstrate our superiority across metrics from distribution learning to condition control for molecular properties. A polymer inverse design task for gas separation with feedback from domain experts further demonstrates its practical utility. The code is available at https: //github. com/liugangcode/Graph-DiT.

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NeurIPS Conference 2024 Conference Paper

Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models

  • Yilun Jin
  • Zheng Li
  • Chenwei Zhang
  • Tianyu Cao
  • Yifan Gao
  • Pratik Jayarao
  • Mao Li
  • Xin Liu

Online shopping is a complex multi-task, few-shot learning problem with a wide and evolving range of entities, relations, and tasks. However, existing models and benchmarks are commonly tailored to specific tasks, falling short of capturing the full complexity of online shopping. Large Language Models (LLMs), with their multi-task and few-shot learning abilities, have the potential to profoundly transform online shopping by alleviating task-specific engineering efforts and by providing users with interactive conversations. Despite the potential, LLMs face unique challenges in online shopping, such as domain-specific concepts, implicit knowledge, and heterogeneous user behaviors. Motivated by the potential and challenges, we propose Shopping MMLU, a diverse multi-task online shopping benchmark derived from real-world Amazon data. Shopping MMLU consists of 57 tasks covering 4 major shopping skills: concept understanding, knowledge reasoning, user behavior alignment, and multi-linguality, and can thus comprehensively evaluate the abilities of LLMs as general shop assistants. With Shoppping MMLU, we benchmark over 20 existing LLMs and uncover valuable insights about practices and prospects of building versatile LLM-based shop assistants. Shopping MMLU can be publicly accessed at https: //github. com/KL4805/ShoppingMMLU. In addition, with Shopping MMLU, we are hosting a competition in KDD Cup 2024 with over 500 participating teams. The winning solutions and the associated workshop can be accessed at our website https: //amazon-kddcup24. github. io/.

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NeurIPS Conference 2023 Conference Paper

Data-Centric Learning from Unlabeled Graphs with Diffusion Model

  • Gang Liu
  • Eric Inae
  • Tong Zhao
  • Jiaxin Xu
  • Tengfei Luo
  • Meng Jiang

Graph property prediction tasks are important and numerous. While each task offers a small size of labeled examples, unlabeled graphs have been collected from various sources and at a large scale. A conventional approach is training a model with the unlabeled graphs on self-supervised tasks and then fine-tuning the model on the prediction tasks. However, the self-supervised task knowledge could not be aligned or sometimes conflicted with what the predictions needed. In this paper, we propose to extract the knowledge underlying the large set of unlabeled graphs as a specific set of useful data points to augment each property prediction model. We use a diffusion model to fully utilize the unlabeled graphs and design two new objectives to guide the model's denoising process with each task's labeled data to generate task-specific graph examples and their labels. Experiments demonstrate that our data-centric approach performs significantly better than fifteen existing various methods on fifteen tasks. The performance improvement brought by unlabeled data is visible as the generated labeled examples unlike the self-supervised learning.

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TIST Journal 2022 Journal Article

Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance

  • Meng Jiang
  • Taeho Jung
  • Ryan Karl
  • Tong Zhao

Distributed surveillance systems have the ability to detect, track, and snapshot objects moving around in a certain space. The systems generate video data from multiple personal devices or street cameras. Intelligent video-analysis models are needed to learn dynamic representation of the objects for detection and tracking. Can we exploit the structural and dynamic information without storing the spatiotemporal video data at a central server that leads to a violation of user privacy? In this work, we introduce Federated Dynamic Graph Neural Network (Feddy), a distributed and secured framework to learn the object representations from graph sequences: (1) It aggregates structural information from nearby objects in the current graph as well as dynamic information from those in the previous graph. It uses a self-supervised loss of predicting the trajectories of objects. (2) It is trained in a federated learning manner. The centrally located server sends the model to user devices. Local models on the respective user devices learn and periodically send their learning to the central server without ever exposing the user’s data to server. (3) Studies showed that the aggregated parameters could be inspected though decrypted when broadcast to clients for model synchronizing, after the server performed a weighted average. We design an appropriate aggregation mechanism of secure aggregation primitives that can protect the security and privacy in federated learning with scalability. Experiments on four video camera datasets as well as simulation demonstrate that Feddy achieves great effectiveness and security.

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AAAI Conference 2021 Conference Paper

Data Augmentation for Graph Neural Networks

  • Tong Zhao
  • Yozen Liu
  • Leonardo Neves
  • Oliver Woodford
  • Meng Jiang
  • Neil Shah

Data augmentation has been widely used to improve generalizability of machine learning models. However, comparatively little work studies data augmentation for graphs. This is largely due to the complex, non-Euclidean structure of graphs, which limits possible manipulation operations. Augmentation operations commonly used in vision and language have no analogs for graphs. Our work studies graph data augmentation for graph neural networks (GNNs) in the context of improving semi-supervised node-classification. We discuss practical and theoretical motivations, considerations and strategies for graph data augmentation. Our work shows that neural edge predictors can effectively encode classhomophilic structure to promote intra-class edges and demote inter-class edges in given graph structure, and our main contribution introduces the GAUG graph data augmentation framework, which leverages these insights to improve performance in GNN-based node classification via edge prediction. Extensive experiments on multiple benchmarks show that augmentation via GAUG improves performance across GNN architectures and datasets.

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TIST Journal 2021 Journal Article

Modeling Complementarity in Behavior Data with Multi-Type Itemset Embedding

  • Daheng Wang
  • Qingkai Zeng
  • Nitesh V. Chawla
  • Meng Jiang

People are looking for complementary contexts, such as team members of complementary skills for project team building and/or reading materials of complementary knowledge for effective student learning, to make their behaviors more likely to be successful. Complementarity has been revealed by behavioral sciences as one of the most important factors in decision making. Existing computational models that learn low-dimensional context representations from behavior data have poor scalability and recent network embedding methods only focus on preserving the similarity between the contexts. In this work, we formulate a behavior entry as a set of context items and propose a novel representation learning method, Multi-type Itemset Embedding, to learn the context representations preserving the itemset structures. We propose a measurement of complementarity between context items in the embedding space. Experiments demonstrate both effectiveness and efficiency of the proposed method over the state-of-the-art methods on behavior prediction and context recommendation. We discover that the complementary contexts and similar contexts are significantly different in human behaviors.

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AAAI Conference 2020 Conference Paper

Few-Shot Knowledge Graph Completion

  • Chuxu Zhang
  • Huaxiu Yao
  • Chao Huang
  • Meng Jiang
  • Zhenhui Li
  • Nitesh V. Chawla

Knowledge graphs (KGs) serve as useful resources for various natural language processing applications. Previous KG completion approaches require a large number of training instances (i. e. , head-tail entity pairs) for every relation. The real case is that for most of the relations, very few entity pairs are available. Existing work of one-shot learning limits method generalizability for few-shot scenarios and does not fully use the supervisory information; however, few-shot KG completion has not been well studied yet. In this work, we propose a novel few-shot relation learning model (FSRL) that aims at discovering facts of new relations with few-shot references. FSRL can effectively capture knowledge from heterogeneous graph structure, aggregate representations of few-shot references, and match similar entity pairs of reference set for every relation. Extensive experiments on two public datasets demonstrate that FSRL outperforms the state-of-the-art.

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AAAI Conference 2020 Conference Paper

Graph Few-Shot Learning via Knowledge Transfer

  • Huaxiu Yao
  • Chuxu Zhang
  • Ying Wei
  • Meng Jiang
  • Suhang Wang
  • Junzhou Huang
  • Nitesh Chawla
  • Zhenhui Li

Towards the challenging problem of semi-supervised node classification, there have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest recently, which update the representation of each node by aggregating information of its neighbors. However, most GNNs have shallow layers with a limited receptive field and may not achieve satisfactory performance especially when the number of labeled nodes is quite small. To address this challenge, we innovatively propose a graph few-shot learning (GFL) algorithm that incorporates prior knowledge learned from auxiliary graphs to improve classification accuracy on the target graph. Specifically, a transferable metric space characterized by a node embedding and a graph-specific prototype embedding function is shared between auxiliary graphs and the target, facilitating the transfer of structural knowledge. Extensive experiments and ablation studies on four real-world graph datasets demonstrate the effectiveness of our proposed model and the contribution of each component.

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AAAI Conference 2020 Conference Paper

Multi-Label Patent Categorization with Non-Local Attention-Based Graph Convolutional Network

  • Pingjie Tang
  • Meng Jiang
  • Bryan (Ning) Xia
  • Jed W. Pitera
  • Jeffrey Welser
  • Nitesh V. Chawla

Patent categorization, which is to assign multiple International Patent Classification (IPC) codes to a patent document, relies heavily on expert efforts, as it requires substantial domain knowledge. When formulated as a multi-label text classification (MTC) problem, it draws two challenges to existing models: one is to learn effective document representations from text content; the other is to model the cross-section behavior of label set. In this work, we propose a label attention model based on graph convolutional network. It jointly learns the document-word associations and word-word cooccurrences to generate rich semantic embeddings of documents. It employs a non-local attention mechanism to learn label representations in the same space of document representations for multi-label classification. On a large CIRCA patent database, we evaluate the performance of our model and as many as seven competitive baselines. We find that our model outperforms all those prior state of the art by a large margin and achieves high performance on P@k and nDCG@k.

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IJCAI Conference 2017 Conference Paper

ContextCare: Incorporating Contextual Information Networks to Representation Learning on Medical Forum Data

  • Stan Zhao
  • Meng Jiang
  • Quan Yuan
  • Bing Qin
  • Ting Liu
  • ChengXiang Zhai

Online users have generated a large amount of health-related data on medical forums and search engines. However, exploiting these rich data for orienting patient online and assisting medical checkup offline is nontrivial due to the sparseness of existing symptom-disease links, which caused by the natural and chatty expressions of symptoms. In this paper, we propose a novel and general representation learning method ContextCare for human generated health-related data, which learns the latent relationship between symptoms and diseases from the symptom-disease diagnosis network for disease prediction, disease category prediction and disease clustering. To alleviate the network sparseness, ContextCare adopts regularizations from rich contextual information networks including a symptom co-occurrence network and a disease evolution network. Therefore, our representations of symptoms and diseases incorporate knowledge from these three networks. Extensive experiments on medical forum data demonstrate that ContextCare outperforms the state-of-the-art methods in disease category prediction, disease prediction and disease clustering.

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AAAI Conference 2017 Conference Paper

Treatment Effect Estimation with Data-Driven Variable Decomposition

  • Kun Kuang
  • Peng Cui
  • Bo Li
  • Meng Jiang
  • Shiqiang Yang
  • Fei Wang

One fundamental problem in causal inference is the treatment effect estimation in observational studies when variables are confounded. Control for confounding effect is generally handled by propensity score. But it treats all observed variables as confounders and ignores the adjustment variables, which have no influence on treatment but are predictive of the outcome. Recently, it has been demonstrated that the adjustment variables are effective in reducing the variance of estimated treatment effect. However, how to automatically separate the confounders and adjustment variables in observational studies is still an open problem, especially in the scenarios of high dimensional variables, which are common in big data era. In this paper, we propose a Data-Driven Variable Decomposition (D2 VD) algorithm, which can 1) automatically separate confounders and adjustment variables with a data driven approach, and 2) simultaneously estimate treatment effect in observational studies with high dimensional variables. Under standard assumptions, we show experimentally that our D2 VD algorithm can automatically separate the variables precisely, and estimate treatment effect more accurately and with tighter confidence intervals than the state-of-the-art methods on both synthetic data and real online advertising dataset.

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AAAI Conference 2016 Conference Paper

Little Is Much: Bridging Cross-Platform Behaviors through Overlapped Crowds

  • Meng Jiang
  • Peng Cui
  • Nicholas Jing Yuan
  • Xing Xie
  • Shiqiang Yang

People often use multiple platforms to fulfill their different information needs. With the ultimate goal of serving people intelligently, a fundamental way is to get comprehensive understanding about user needs. How to organically integrate and bridge cross-platform information in a human-centric way is important. Existing transfer learning assumes either fullyoverlapped or non-overlapped among the users. However, the real case is the users of different platforms are partially overlapped. The number of overlapped users is often small and the explicitly known overlapped users is even less due to the lacking of unified ID for a user across different platforms. In this paper, we propose a novel semi-supervised transfer learning method to address the problem of cross-platform behavior prediction, called XPTRANS. To alleviate the sparsity issue, it fully exploits the small number of overlapped crowds to optimally bridge a user’s behaviors in different platforms. Extensive experiments across two real social networks show that XPTRANS significantly outperforms the state-of-the-art. We demonstrate that by fully exploiting 26% overlapped users, XPTRANS can predict the behaviors of non-overlapped users with the same accuracy as overlapped users, which means the small overlapped crowds can successfully bridge the information across different platforms.

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IS Journal 2016 Journal Article

Suspicious Behavior Detection: Current Trends and Future Directions

  • Meng Jiang
  • Peng Cui
  • Christos Faloutsos

Different real-world applications have varying definitions of suspicious behaviors. Detection methods often look for the most suspicious parts of the data by optimizing scores, but quantifying the suspiciousness of a behavioral pattern is still an open issue.

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v2026.09.13