Double Starlike Graphs with Largest and Second Largest Degree Difference 2 Determined by Their Laplacian Spectrum. - Pengli Lu; Zhanting Yuan (CSIE (3), 2009)

One special double starlike graph is determined by its Laplacian spectrum. - Xiaogang Liu; Yuanping Zhang; Pengli Lu (Appl. Math. Lett., 2009)

Spectral characterizations of sandglass graphs. - Pengli Lu; Xiaogang Liu; Zhanting Yuan; Xuerong Yong (Appl. Math. Lett., 2009)

Finding Relational Community through Two Nodes. - Pengli Lu; ChunXu Jia (ACIS-ICIS, 2011)

The generalized chaaracteristic polynomial of the subdivision-vertex and subdivision-edge coronae. - Pengli Lu; Yufang Miao (Ars Comb., 2016)

Resistance Distance of Three Classes of Join Graphs. - Pengli Lu; Teng Zhang (Ars Comb., 2016)

Laplacian spectral characterization of dumbbell graphs and theta graphs. - Xiaogang Liu; Pengli Lu (Discret. Math. Algorithms Appl., 2016)

Laplacian and signless Laplacian characteristic polynomial of generalized subdivision corona vertex graph. - Pengli Lu; Yumo Wu (Ars Comb., 2017)

Generalized characteristic polynomial of generalized R-vertex corona. - Pengli Lu; Yang Yang (Ars Comb., 2017)

Adjacency and Laplacian spectrum of a class of generalized corona. - Pengli Lu; Ke Gao (Ars Comb., 2018)

Resistance distance and Kirchhoff index of two corona graphs. - Pengli Lu; Liangyuan Xue (Ars Comb., 2018)

Signless Laplacian spectrum of a class of generalized corona and its application. - Pengli Lu; Ke Gao; Yumo Wu (Discret. Math. Algorithms Appl., 2018)

Two New Methods for Identifying Essential Proteins Based on the Protein Complexes and Topological Properties. - Pengli Lu; Jingjuan Yu (IEEE Access, 2020)

Ranking the spreading influence of nodes in complex networks based on mixing degree centrality and local structure. - Pengli Lu; Chen Dong 006 (CoRR, 2020)

EMH: Extended Mixing H-index centrality for identification important users in social networks based on neighborhood diversity. - Pengli Lu; Chen Dong 006 (CoRR, 2020)

A novel method based on node correlation to evaluate the important nodes in complex networks. - Pengli Lu; Chen Dong 006; Yuhong Guo (CoRR, 2021)

Probing the influence maximization-cost minimization problem in social networks by using a multi-objective discrete differential evolution optimization. - Jianxin Tang; Li Zhang; Pengli Lu; Jimao Lan (CNIOT, 2023)

The generalized path matrix and energy. - Pengli Lu; Rui Luan (Discret. Math. Algorithms Appl., 2023)

HCGCCDA: Prediction of circRNA-disease associations based on the combination of hypergraph convolution and graph convolution. - Pengli Lu; Jinkai Wu; Wenqi Zhang (J. Comput. Sci., 2023)

Combining transformer-based model and GCN to predict ICD codes from clinical records. - Pengli Lu; Jingjin Xue (Knowl. Based Syst., 2023)

DCSGMDA: A dual-channel convolutional model based on stacked deep learning collaborative gradient decomposition for predicting miRNA-disease associations. - Xu Cao; Pengli Lu (Comput. Biol. Chem., 2024)

AE-RW: Predicting miRNA-disease associations by using autoencoder and random walk on miRNA-gene-disease heterogeneous network. - Pengli Lu; Jicheng Jiang (Comput. Biol. Chem., 2024)

MGDHGS: Gene-bridged metabolite-disease relationships prediction via GraphSAGE and self-attention mechanism. - Pengli Lu; Ling Li (Comput. Biol. Chem., 2024)

ACDMBI: A deep learning model based on community division and multi-source biological information fusion predicts essential proteins. - Pengli Lu; Jialong Tian (Comput. Biol. Chem., 2024)

GEHGAN: CircRNA-disease association prediction via graph embedding and heterogeneous graph attention network. - Yuehao Wang; Pengli Lu (Comput. Biol. Chem., 2024)

Identification of key nodes in complex networks by using a joint technique of nonnegative matrix factorization and regularization. - Pengli Lu; Junxia Yang; Wenzhi Liu (Phys. Commun., 2024)

RDGAN: Prediction of circRNA-Disease Associations via Resistance Distance and Graph Attention Network. - Pengli Lu; Yuehao Wang (IEEE ACM Trans. Comput. Biol. Bioinform., 2024)

AMPCDA: Prediction of circRNA-disease associations by utilizing attention mechanisms on metapaths. - Pengli Lu; Wenqi Zhang; Jinkai Wu (Comput. Biol. Chem., 2024)

Identifying Influential Nodes in Complex Networks From Semi-Local and Global Perspective. - Wenzhi Liu; Pengli Lu; Teng Zhang (IEEE Trans. Comput. Soc. Syst., 2024)

ICD code mapping model based on clinical text tree structure. - Jingjin Xue; Pengli Lu (Artif. Intell. Medicine, 2025)

DMNAG: Prediction of disease-metabolite associations based on Neighborhood Aggregation Graph Transformer. - Pengli Lu; Jiajie Gao; Wenzhi Liu (Comput. Biol. Chem., 2025)

Gene-related multi-network collaborative deep feature learning for predicting miRNA-disease associations. - Pengli Lu; Xu Cao (Comput. Electr. Eng., 2025)

RGCGT: A high-order feature learning framework for predicting disease-metabolite interaction using residual graph convolution and graph transformer. - Wenzhi Liu; Pengli Lu; Jiajie Gao (Expert Syst. Appl., 2025)

MGDRGCN: A novel framework for predicting metabolite-disease connections using tripartite network and relational graph convolutional network. - Pengli Lu; Ling Li (J. Comput. Sci., 2025)

Enhanced metabolite-disease associations prediction via Neighborhood Aggregation Graph Transformer with Kolmogorov-Arnold Networks. - Pengli Lu; Jian Zhang; Wenzhi Liu; Fentang Gao (J. Comput. Sci., 2025)

TECNN: Identification of key nodes in complex networks based on transformer encoder and Convolutional Neural Network. - Lihui Sun; Pengli Lu (J. Comput. Sci., 2025)