A local random walk model for complex networks based on discriminative feature combinations

Song, Aibo; Liu, Yangyang; Wu, Zhang; Zhai, Mingyu; Luo, Junzhou

doi:10.1016/j.eswa.2018.10.018

Cited by 20 publications

(11 citation statements)

References 22 publications

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“…For instance, we have a number of nodes and some of them are labeled while some are unlabeled. The combination of structural features and content of the labeled nodes to classify the unlabeled node 31,32 . The collective classification achieves higher classification accuracy compared with the individual classification methods shown in the previous techniques 33,34 .…”

Section: Related Workmentioning

confidence: 99%

Author classification using transfer learning and predicting stars in co‐author networks

et al. 2020

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Summary The vast amount of data is key challenge to mine a new scholar that is plausible to be star in the upcoming period. The enormous amount of unstructured data raise every year is infeasible for traditional learning; consequently, we need a high quality of preprocessing technique to expand the performance of traditional learning. We have persuaded a novel approach, Authors classification algorithm using Transfer Learning (ACTL) to learn new task on target area to mine the external knowledge from the source domain. Comprehensive experimental outcomes on real‐world networks showed that ACTL, Node‐based Influence Predicting Stars, Corresponding Authors Mutual Influence based on Predicting Stars, and Specific Topic Domain‐based Predicting Stars enhanced the node classification accuracy as well as predicting rising stars to compared with contemporary baseline methods.

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Section: Related Workmentioning

confidence: 99%

Author classification using transfer learning and predicting stars in co‐author networks

et al. 2020

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“…In recent years, network analysis on PPI networks has raised the interest of researchers, such as the node centrality method for identifying key proteins [9] and the MCODE method for mining functional modules in the PPI network [10]. Many novel network model and analysis methods are also proposed [11]- [15]. These methods have the potentials to be applied to PPI network analysis.…”

Section: Introductionmentioning

confidence: 99%

Toward Identifying Key Gene Group in the Occurrence and Development of Lung Adenocarcinoma

Zhao

Liu

et al. 2021

IEEE Access

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Lung adenocarcinoma (LUAD) has become the most common pathological type of lung cancer in recent years. However, the molecular mechanism of LUAD remains unclear. To reveal the laws in the occurrence and development of LUAD, this paper first collects 676 differentially expressed genes related to LUAD and the corresponding proteins encoded by the genes, resulting in a complex proteinprotein interaction (PPI) network. The traditional analysis methods focus on the gene interaction relationships or the important genes separately, ignoring the fact that some important target genes may take effect jointly. In contrast, this paper adopts a new analysis method named the overlapping gene analysis method to screen out the closely interacted and important genes. Thereinto, the hub genes are first discovered according to the node importance index, and then the PPI network is divided into multiple communities. The overlapping genes are some genes belonging to both the hub genes and the genes in the same community, regarded as gene groups. Through experiments, 10 genes are identified as a gene group. Survival analyses to the gene group show that only PLK1, CCNB1, and CDK1 participate in the prognosis of LUAD, and relate to the pathological stage of LUAD. These demonstrate that the three genes are extremely important in the cell cycle, which is confirmed by the following enrichment analyses. Besides, some significant correlations are observed among the three genes, which provide a clue that the three genes may cooperate in the occurrence and development of LUAD. This finding provides possible clinical targets for the diagnosis and treatment of LUAD. INDEX TERMS complex protein network, hub gene analysis, community analysis, cell cycle, tumor and cancer.

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“…Therefore, attribute-based methods are preferable in some aspects. Song et al [29] present a combined approach based on discriminative feature combinations that direct link prediction by the attribute information of nodes and topological structure. However, it is difficult to predetermine the weights of structure and attributes [30].…”

Section: Introductionmentioning

confidence: 99%

The Deep Fusion of Topological Structure and Attribute Information for Link Prediction

et al. 2020

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The link prediction can be used to seek missing or future links in the network, so it has become a hot research topic. The network generally contains two types of information: the topological structure of network formed by the connection between nodes, and the attribute information of nodes. However, the existing topology-based link prediction algorithms consider little attribute information. In this paper, a novel algorithm called Network Embedding with Attribute Deep Fusion for Link Prediction (NEADF-LP) is proposed. We get the embedded vectors with topological structure and attribute information by structure encoder and attribute encoder respectively, and fuse two vectors deeply. Compared with mainstream baselines on CiteSeer and Cora datasets, the results show that the deep fusion of topological structure and attribute information improve the accuracy of link prediction effectively. INDEX TERMS Complex network, deep fusion, link prediction, network embedding.

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A local random walk model for complex networks based on discriminative feature combinations

Cited by 20 publications

References 22 publications

Author classification using transfer learning and predicting stars in co‐author networks

Author classification using transfer learning and predicting stars in co‐author networks

Toward Identifying Key Gene Group in the Occurrence and Development of Lung Adenocarcinoma

The Deep Fusion of Topological Structure and Attribute Information for Link Prediction

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