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Gang Kou

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5 papers

2 author rows

AAAI Conference 2026 Conference Paper

CastX: Cohort-Level Causal Inference Meets Statistical Testing for Faithful and Reliable GNN Explanations

Guanyuan Yu
Yijun Chen
Liang Xu
Gang Kou

Explainability plays a critical role in understanding the workings of Graph Neural Networks (GNNs). While recent methods have introduced causal inference into GNN explanation, they predominantly rely on individual-level interventions and lack rigorous statistical causality testing, resulting in unfaithful and unreliable explanations. To address these challenges, we propose CastX that integrates cohort-level causal analysis with statistical causality testing for GNN explanations. Specifically, CastX formulates the discovery of explanatory subgraphs as a dynamic edge pruning task guided by Conditional Average Treatment Effect (CATE) estimation. A reinforcement learning agent is employed to iteratively eliminate spurious edges and identify causally informative substructures. To further enhance reliability, we introduce an i.i.d.-agnostic non-parametric permutation test that assesses the statistical significance of each target edge. Extensive experiments on real-world datasets demonstrate that our CastX outperforms existing methods in yielding explanatory subgraphs that are concise, faithful, reliable, and statistically supported.

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AAAI Conference 2026 Conference Paper

Transferable Graph Condensation from the Causal Perspective

Huaming Du
Yijie Huang
Su Yao
Yiying Wang
Yueyang Zhou
Jingwen Yang
Jinshi Zhang
Han Ji

The increasing scale of graph datasets has significantly improved the performance of graph representation learning methods, but it has also introduced substantial training challenges. Graph dataset condensation techniques have emerged to compress large datasets into smaller yet information-rich datasets, while maintaining similar test performance. However, these methods strictly require downstream applications to match the original dataset and task, which often fails in cross-task and cross-domain scenarios. To address these challenges, we propose a novel causal-invariance-based and transferable graph dataset condensation method, named TGCC, providing effective and transferable condensed datasets. Specifically, to preserve domain-invariant knowledge, we first extract domain causal-invariant features from the spatial domain of the graph using causal interventions. Then, to fully capture the structural and feature information of the original graph, we perform enhanced condensation operations. Finally, through spectral-domain Enhanced contrastive learning, we inject the causal-invariant features into the condensed graph, ensuring that the compressed graph retains the causal information of the original graph. Experimental results on five public datasets and our novel FinReport dataset demonstrate that TGCC achieves up to a 13.41% improvement in cross-task and cross-domain complex scenarios compared to existing methods, and achieves state-of-the-art performance on 5 out of 6 datasets in the single dataset and task scenario.

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EAAI Journal 2023 Journal Article

Adoption of energy consumption in urban mobility considering digital carbon footprint: A two-phase interval-valued Fermatean fuzzy dominance methodology

Jeevaraj S.
Ilgin Gokasar
Muhammet Deveci
Dursun Delen
Bilal Bahaa Zaidan
Xin Wen
Wen-Long Shang
Gang Kou

Interval-valued Fermatean fuzzy sets play a significant role in modelling decision-making problems with incomplete information more accurately than intuitionistic fuzzy sets. Various decision-making methods have been introduced for the different classes IFSs. In this study, we aim to introduce a novel two-phase interval-valued Fermatean fuzzy dominance method which suits the decision-making problems modelled under the IVFFS environment well and study its applications in the adoption of energy consumption in Urban mobility considering digital carbon footprint. The proposed method considers the importance and performance of one alternative with respect to all others, which is not the case with many available decision-making algorithms introduced in the literature. Transportation is one of the most significant sources of global greenhouse gas (GHG) emissions. Numerous potential remedies are proposed to reduce the quantity of GHG generated by transportation activities, including regulatory measures and public transit digitalization initiatives. Decision-makers, however, should consider the digital carbon footprint of such projects. This study proposes three alternatives for reducing GHG emissions from transportation activities: incremental adoption of digital technologies to reduce energy consumption and greenhouse gases, disruptive digitalization technologies in urban mobility, and redesign of urban mobility using regulatory approaches and economic instruments. The proposed novel two-phase interval-valued Fermatean fuzzy dominance method will be utilized to rank these alternative projects in order of advantage. First, the problem is converted into a multi-criterion group decision-making problem. Then a novel two-phase interval-valued Fermatean fuzzy dominance method is designed and developed to rank the alternatives. The importance and advantage of the proposed two-phase method over other existing methods are discussed by using sensitivity and comparative analysis. The results indicate that rethinking urban mobility through governmental policies and economic tools is the least advantageous choice, while incremental adoption of digital technologies is the most advantageous.

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AAAI Conference 2023 Conference Paper

Attribute and Structure Preserving Graph Contrastive Learning

Jialu Chen
Gang Kou

Graph Contrastive Learning (GCL) has drawn much research interest due to its strong ability to capture both graph structure and node attribute information in a self-supervised manner. Current GCL methods usually adopt Graph Neural Networks (GNNs) as the base encoder, which typically relies on the homophily assumption of networks and overlooks node similarity in the attribute space. There are many scenarios where such assumption cannot be satisfied, or node similarity plays a crucial role. In order to design a more robust mechanism, we develop a novel attribute and structure preserving graph contrastive learning framework, named ASP, which comprehensively and efficiently preserves node attributes while exploiting graph structure. Specifically, we consider three different graph views in our framework, i.e., original view, attribute view, and global structure view. Then, we perform contrastive learning across three views in a joint fashion, mining comprehensive graph information. We validate the effectiveness of the proposed framework on various real-world networks with different levels of homophily. The results demonstrate the superior performance of our model over the representative baselines.

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ICML Conference 2022 Conference Paper

Role-based Multiplex Network Embedding

Hegui Zhang
Gang Kou

In recent years, multiplex network embedding has received great attention from researchers. However, existing multiplex network embedding methods neglect structural role information, which can be used to determine the structural similarity between nodes. To overcome this shortcoming, this work proposes a simple, effective, role-based embedding method for multiplex networks, called RMNE. The RMNE uses the structural role information of nodes to preserve the structural similarity between nodes in the entire multiplex network. Specifically, a role-modified random walk is designed to generate node sequences of each node, which can capture both the within-layer neighbors, structural role members, and cross-layer structural role members of a node. Additionally, the variant of RMNE extends the existing collaborative embedding method by unifying the structural role information into our method to obtain the role-based node representations. Finally, the proposed methods were evaluated on the network reconstruction, node classification, link prediction, and multi-class edge classification tasks. The experimental results on eight public, real-world multiplex networks demonstrate that the proposed methods outperform state-of-the-art baseline methods.

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