Redundancy reduction based node classification with attribute augmentation

Liu, Songhua; Ding, Caiying; Ma, Yao; Li, Zepeng; Xiang, Jie

doi:10.1016/j.knosys.2019.105080

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…En ambos casos, se propone un método compuesto de fases para representar el conocimiento mediante un GC de un AD; cada nodo es etiquetado y ponderado de acuerdo con su influencia entre los temas relevantes. Ninguno de ellos clasifica los nodos para evitar su evaluación exhaustiva como en el caso de Liu et al (2020) y tampoco examina los nodos con una precisión adecuada .…”

Section: Revisión De La Literaturaunclassified

“…Como ya se mencionó, el dominio educativo necesita representar el conocimiento de los estudiantes mediante procesos y métodos innovadores (Guan et al, 2019;Long et al, 2020) que eviten la evaluación exhaustiva (Liu et al, 2020) y procuren la precisión . El presente estudio de caso describe cómo se puede estudiar este problema de manera simplificada.…”

Section: Estudio De Casounclassified

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Knowledge Graph to determine the domain of learning in Higher Education

Almazán

Vega

Amaya

et al. 2021

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The representing of a student’s knowledge in an academic discipline plays an important role in boosting the student’s skills. To support stakeholders in the educational domain, it is necessary to provide them with robust assessment strategies that facilitate the teaching-learning process. Student´s mastery is determined by the degree of knowledge, which demonstrates objectively, on the topics included in the different areas that make up an academic discipline. Although there is a wide variety of techniques to represent knowledge, particularly Knowledge Graph technique is becoming relevant due to the structured approach and benefits it offers. This paper proposes a method that classifies and weights the nodes (topics) of a Knowledge Graph of a disciplinary area, which is analyzed through a case study. The method has two approaches: avoid exhaustive evaluation of the nodes and weight the nodes with adequate precision. Method´s application is illustrated by a case study. As results, a Knowledge Graph is obtained with its classified and weighted nodes through the application of the proposed method, in which 100% of the topics have been impacted through the objective evaluation of 20.8% representing 10 nodes. It is concluded that the proposed method has potential to be used in the representation and management of knowledge, being necessary to improve phases’ iteration to condition number of objective nodes.

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Section: Revisión De La Literaturaunclassified

Section: Estudio De Casounclassified

Knowledge Graph to determine the domain of learning in Higher Education

Almazán

Vega

Amaya

et al. 2021

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“…This subsection verified the node classification effect of the DPP model in the dynamic network by computer simulation. In the simulation, LPA algorithm [16], ALPA algorithm [21], DyPerm algorithm [22], and DynaMo algorithm [23] have been used to compare with the DPP model proposed in this paper.…”

Section: Node Classification Experiments On Dynamic Networkmentioning

confidence: 99%

“…In addition, node classification for specific types of networks has also been developed in recent years. NCAA [23] framework uses some methods such as paying attention to similar attributes and reducing redundancy attribute to achieve effective dynamic node classification in multi-attribute networks. NCTA [24] algorithm constructs a node classification mechanism to complete the reasonable allocation of computing power.…”

Section: Introductionmentioning

confidence: 99%

Particle Propagation Model for Dynamic Node Classification

Yin

Yuan

et al. 2020

IEEE Access

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With the popularity of online social networks, researches on dynamic node classification have received further attention. Dynamic node classification also helps the rapid popularization of online social networks. This paper proposes a particle competition model named DPP to complete the dynamic node classification. Existing node classification models based on particle competition do not perform well in terms of accuracy. Hence, we formulate a unique particle competition framework to make the node classification more effective. In addition, for applying the model in the dynamic network, based on the dynamic characteristics of the model, we have added an automatic update strategy of the source node to the model. The particles in the new model perform the steps of walking, splitting, and jumping according to the method introduced in this paper. Then, the domination matrix of the network has been changed with particle movements continuously. Although the particles randomly walk at the micro-level, the model can converge to obtain the node classification results. Finally, simulation results show both the effectiveness and superiority of our proposed node classification model with the comparison of other major particle competition models and dynamic node classification methods. Based on the above contributions, the proposed model may have compelling applications in the context of community detection and network embedding, etc. INDEX TERMS Complex network, particle competition, dynamic node classification.

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Graph pooling via Dual-view Multi-level Infomax

Zhao

Zhang

et al. 2023

Knowledge-Based Systems

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Redundancy reduction based node classification with attribute augmentation

Cited by 6 publications

References 19 publications

Knowledge Graph to determine the domain of learning in Higher Education

Knowledge Graph to determine the domain of learning in Higher Education

Particle Propagation Model for Dynamic Node Classification

Graph pooling via Dual-view Multi-level Infomax

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