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Hua Mao

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4 papers

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AAAI Conference 2026 Conference Paper

Conditional Distribution Learning for Graph Classification

Jie Chen
Hua Mao
Chuanbin Liu
Zhu Wang
Xi Peng

Leveraging the diversity and quantity of data provided by various graph-structured data augmentations while preserving intrinsic semantic information is challenging. Additionally, successive layers in graph neural network (GNN) tend to produce more similar node embeddings, while graph contrastive learning aims to increase the dissimilarity between negative pairs of node embeddings. This inevitably results in a conflict between the message-passing mechanism (MPM) of GNNs and the contrastive learning (CL) of negative pairs via intraviews. In this paper, we propose a conditional distribution learning (CDL) method that learns graph representations from graph-structured data for semisupervised graph classification. Specifically, we present an end-to-end graph representation learning model to align the conditional distributions of weakly and strongly augmented features over the original features. This alignment enables the CDL model to effectively preserve intrinsic semantic information when both weak and strong augmentations are applied to graph-structured data. To avoid the conflict between the MPM and the CL of negative pairs, positive pairs of node representations are retained for measuring the similarity between the original features and the corresponding weakly augmented features. Extensive experiments with several benchmark graph datasets demonstrate the effectiveness of the proposed CDL method.

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AAAI Conference 2025 Conference Paper

Cross-View Graph Consistency Learning for Invariant Graph Representations

Jie Chen
Hua Mao
Wai Lok Woo
Chuanbin Liu
Xi Peng

Graph representation learning is fundamental for analyzing graph-structured data. Exploring invariant graph representations remains a challenge for most existing graph representation learning methods. In this paper, we propose a cross-view graph consistency learning (CGCL) method that learns invariant graph representations for link prediction. First, two complementary augmented views are derived from an incomplete graph structure through a coupled graph structure augmentation scheme. This augmentation scheme mitigates the potential information loss that is commonly associated with various data augmentation techniques involving raw graph data, such as edge perturbation, node removal, and attribute masking. Second, we propose a CGCL model that can learn invariant graph representations. A cross-view training scheme is proposed to train the proposed CGCL model. This scheme attempts to maximize the consistency information between one augmented view and the graph structure reconstructed from the other augmented view. Furthermore, we offer a comprehensive theoretical CGCL analysis. This paper empirically and experimentally demonstrates the effectiveness of the proposed CGCL method, achieving competitive results on graph datasets in comparisons with several state-of-the-art algorithms.

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AAMAS Conference 2012 Conference Paper

Improved Use of Partial Policies for Identifying Behavioral Equivalence

Yifeng Zeng
Yinghui Pan
Hua Mao
Jian Luo

Interactive multiagent decision making often requires to predict actions of other agents by solving their behavioral models from the perspective of the modeling agent. Unfortunately, the general space of models in the absence of constraining assumptions tends to be very large thereby making multiagent decision making intractable. One approach that can reduce the model space is to cluster \emph{behaviorally equivalent models} that exhibit identical policies for the whole planning horizon. Currently, the state-of-the art on identifying equivalence of behavioral models compares partial policy trees instead of entire trees. In this paper, we further improve the use of partial trees for the identification purpose and develop an incremental comparison strategy in order to efficiently ascertain the model equivalence. We investigate the improved approach in a well-defined probabilistic graphical model for sequential multiagent decision making - interactive dynamic influence diagrams, and evaluate its performance over multiple problem domains.

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AAAI Conference 2011 Conference Paper

Utilizing Partial Policies for Identifying Equivalence of Behavioral Models

Yifeng Zeng
Prashant Doshi
Yinghui Pan
Hua Mao
Muthukumaran Chandrasekaran
Jian Luo

We present a novel approach for identifying exact and approximate behavioral equivalence between models of agents. This is signiﬁcant because both decision making and game play in multiagent settings must contend with behavioral models of other agents in order to predict their actions. One approach that reduces the complexity of the model space is to group models that are behaviorally equivalent. Identifying equivalence between models requires solving them and comparing entire policy trees. Because the trees grow exponentially with the horizon, our approach is to focus on partial policy trees for comparison and determining the distance between updated beliefs at the leaves of the trees. We propose a principled way to determine how much of the policy trees to consider, which trades off solution quality for efﬁciency. We investigate this approach in the context of the interactive dynamic inﬂuence diagram and evaluate its performance.

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