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Chanjuan Liu

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5 papers

1 author row

AAAI Conference 2026 Conference Paper

Exact Optimization for Minimum Dominating Sets

Enqiang Zhu
Qiqi Bao
Yu Zhang
Chanjuan Liu
Pu Wu

The Minimum Dominating Set (MDS) problem is a well-established combinatorial optimization problem with numerous real-world applications. Its NP-hard nature makes it increasingly difficult to obtain exact solutions as the graph size grows. This paper introduces ParDS, an exact algorithm developed to address the MDS problem within the branch-and-bound framework. ParDS features two key innovations: an advanced linear programming technique that yields tighter lower bounds and a set of novel reduction rules that dynamically simplify instances throughout the solving process. Compared to the leading exact algorithms presented at IJCAI 2023 and 2024, ParDS demonstrates theoretically superior lower-bound quality. Experimental results on standard benchmark datasets highlight several significant advantages of ParDS: it achieves fastest solving times in 70% of graph categories, especially on large, sparse graphs, delivers a speed-up of up to 3,411 times on the fastest individual instance, and successfully solves 16 out of 43 instances that other algorithms were unable to resolve within the 5-hour time limit. These findings establish ParDS as a state-of-the-art solution for exactly solving the MDS problem

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EAAI Journal 2025 Journal Article

Critical nodes detection for complex networks via knowledge-guided evolutionary framework

Chanjuan Liu
Shike Ge
Zhihan Chen
Wenbin Pei
Enqiang Zhu
Hisao Ishibuchi

The Critical Node Problem (CNP) focuses on identifying critical nodes within complex networks. These nodes play a crucial role in maintaining connectivity, and their removal impacts network performance. Among CNP variants, CNP-1a — which minimizes pairwise connectivity after removing a limited number of nodes — has attracted significant research attention due to its NP-hard nature and applications in diverse fields like epidemic control and infrastructure resilience. While state-of-the-art methods leverage memetic algorithms and variable populations, they fundamentally rely on random initialization that often converges to local optima. This limitation arises because traditional methods fail to capture higher-order topological dependencies. To address this gap, we propose K2GA, a knowledge-guided genetic algorithm initialized by a graph attention network (GAT). The GAT embeds networks into low-dimensional spaces, assigning topology-aware attention weights to nodes that guide population initialization. K2GA then employs a hybrid genetic algorithm with a local search process to identify an optimal set of critical nodes. The local search process utilizes a cut node-based greedy strategy. Experiments on 26 real-world networks demonstrate that K2GA outperforms state-of-the-art methods in terms of the best, median, and average objective values, establishing new upper bounds for minimization in eight cases. This work pioneers a GAT-guided evolutionary search framework, offering a novel paradigm for solving CNP.

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EAAI Journal 2025 Journal Article

Multimodality based deep learning method for cancer-related T-cell receptor sequence prediction

Junjiang Liu
Shusen Zhou
Mujun Zang
Chanjuan Liu
Tong Liu
Qingjun Wang

T-cell receptor sequences (TCR-seq) are closely related to cancers, and in particular, cancer-related TCR-seq are crucial in cancer diagnosis and treatment. Current prediction methods for cancer-related TCR-seq often focus solely on the sequence structure, neglecting its spatial structure. Therefore, we propose a multimodal deep learning method based on parallel and residual structures (MDPR) for the detection of cancer-related TCR-seq. MDPR can effectively integrate the spatial and sequence structure of TCR-seq for accurately identifying cancer-related sequences. First, we introduce a TCR-seq encoding method based on atomic three-dimensional spatial coordinates, allowing for more effective extraction of the spatial structural features of TCR-seq. Second, we use high-dimensional word vectors instead of the amino acid feature vectors traditionally used by other researchers. Third, we pretrain the spatial feature extraction module and then conduct joint training with the sequence feature extraction module. This approach allows the model to better consider the relationship between the two modalities, thereby improving prediction accuracy. Finally, MDPR achieved an area under the curve (AUC) of 0. 971 after ten rounds of three-fold cross-validation on the dataset. The AUC of MDPR is 5% higher than that of the previous best method. In short, we propose an artificial intelligence method called MDPR, and apply it to the biomedical field. MDPR can be obtained from https: //github. com/biomg/MDPR.

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EAAI Journal 2024 Journal Article

Graph attention network with convolutional layer for predicting gene regulations from single-cell ribonucleic acid sequence data

Junjiang Liu
Shusen Zhou
Jing Ma
Mujun Zang
Chanjuan Liu
Tong Liu
Qingjun Wang

Reconstructing gene regulatory networks (GRNs) is an important task to reveal the regulatory relationship between genes and understand the mechanism of intracellular gene expression regulation. With the development of single-cell ribonucleic acid sequencing (scRNA-seq) technology, researchers begin to attempt to infer GRN within cells. In this paper, we propose graph attention network with convolutional layer (GATCL) to infer the latent interactions between transcription factors (TFs) and target genes in GRN. Firstly, GATCL uses graph attention network (GAT), which can effectively extract information about genes and TFs. Secondly, we combine multi-head attention layer with one-head attention layer and propose a new method of using convolution instead of weight matrix. Thirdly, we use the exponential linear unit (ELU) activation function to replace the leaky rectified linear unit (LReLU) commonly used in GAT, which further improves the accuracy of GATCL. The AUROC of our method on seven scRNA-seq datasets with four types of ground-truth networks reached an average of 0. 827, which is higher than other state-of-the-art models. GATCL applies a supervised deep learning algorithm to solve the problems existing in the inference of GRN from scRNA-seq in the engineering field, and the effectiveness of this method is verified by a large number of experiments.

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TCS Journal 2016 Journal Article

A logical characterization of extensive games with short sight

Chanjuan Liu
Fenrong Liu
Kaile Su
Enqiang Zhu

The notion of short sight, introduced by Grossi and Turrini, weakens the unrealistic assumption in traditional extensive games that every player is able to perceive the entire game structure. In this paper, we propose a more general model for extensive games with short sight. For reasoning about extensive games with short sight, we propose a new logic language and then present an axiomatization for this logic. We prove the soundness and completeness of the axiomatization. In addition, we show that the logic can formally characterize the solution concepts and Pearce's lemma in games with short sight.

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