Networked Knowledge and Complex Networks: An Engineering View

Lü, Jinhu; Wen, Guanghui; Lu, Ruqian; Wang, Yong; Zhang, Songmao

doi:10.1109/jas.2022.105737

Cited by 27 publications

(2 citation statements)

References 146 publications

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“…Reference [28] conducted a detailed survey of KG representation, highlighting hotspots, contributing countries, and top literature. In [29], a KG modeling framework was presented, utilizing information fusion for system modeling. Reference [30] introduced a unique methodology for information representation in KG, emphasizing a semi-autonomous approach.…”

Section: ) Knowledge Graph and Static Graph Explainabilitymentioning

confidence: 99%

A Network Analysis-Driven Framework for Factual Explainability of Knowledge Graphs

Munir,

Malick,

Ferretti

2024

IEEE Access

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Knowledge Graphs are widely used to represent knowledge structures in complex domains. In most real-world scenarios, these knowledge structures are dynamic. As a result, measures must be developed to assess the robustness and usability of Knowledge Graphs in temporal settings. Additionally, the explainability of inherent knowledge constituents is crucial for the desired attention of Knowledge Graphs, particularly in temporal settings. In this paper, we developed a framework to understand the robustness of factual explainability of Knowledge Graphs. The method is further verified by using meso-level attributes of the knowledge graph. The complex network analysis along with the community structures are co-evaluated through homophilic and heterophilic properties within the graph to validate the robustness of the factual interpretations. The analysis reveals that symbolic representation could be used as a reasonable metric for extracting link-based communities.INDEX TERMS Knowledge graph analysis, complex network analysis, homophily and heterophily analysis, factual reasoning and explainability, interpretability.

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Section: ) Knowledge Graph and Static Graph Explainabilitymentioning

confidence: 99%

A Network Analysis-Driven Framework for Factual Explainability of Knowledge Graphs

Munir,

Malick,

Ferretti

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In the SBCN model, the expression of genes is quantified as states "1" (ON) and "0" (OFF), and the state update obeys logical functions, randomly selected from a candidate set at each time step. Beyond biological systems, the SBCN model has attracted considerable attention in potential games [2], multi-agent systems [3], networked knowledge [4], and smart home [5]. Stabilization, as an essential issue in SBCNs, reveals the ability of systems to reach and stay at a desired attractor (fixed state or state set) [6]- [8].…”

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confidence: 99%