An analytical model for information gathering and propagation in social networks using random graphs

Saurabh, Samant; Madria, Sanjay; Mondal, Anirban; Sairam, Ashok Singh; Mishra, Saurabh

doi:10.1016/j.datak.2020.101852

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…At the learning stage, modules update their states and carry out information exchange until the modules reach a stable equilibrium state. The output of a graph neural network is calculated based on the state of the module at each node [11,12].…”

Section: Introductionmentioning

confidence: 99%

Building a Semantic Knowledge Graph Search Model for Finding a Causal Answer

Bektemyssova,

Sabdenov

2024

RIA

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This research focuses on developing effective algorithms for semantic knowledge graph searches in the context of finding causal answers, which is highly relevant due to the widespread use of semantic networks. The study's primary goal is to examine the construction principles of a semantic knowledge graph search model tailored for causal answers, with a focus on deep neural semantic search characteristics. The research methodology combines system analysis methods for building knowledge graphs with an analytical investigation of deep neural semantic search aspects. The results provide insights into the construction of a semantic knowledge graph search model for causal answers, addressing the challenges and methodologies involved in building such a model. It underscores the significance of knowledge graphs in modern information systems and their potential applications in various domains. However, further scientific research is essential to explore the practical applications of deep neural semantic search knowledge graphs in various information systems, which are used across different aspects of everyday life. The practical significance of this research extends to various applications in information retrieval, knowledge management, and problem-solving, making it a valuable contribution to the advancement of technology and understanding of natural language text.

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Section: Introductionmentioning

confidence: 99%

Building a Semantic Knowledge Graph Search Model for Finding a Causal Answer

Bektemyssova,

Sabdenov

2024

RIA

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“…The outbreak of the COVID-19 virus [1] in 2020 has had a huge impact on global economic development. Many countries have imposed restrictions on the transportation industry to slow the spread of the virus [2] . The urban Traffic Revitalization Index (TRI) [3] is an important indicator to measure the recovery status of urban traffic after being affected by the epidemic.…”

Section: Introductionmentioning

confidence: 99%

A new approach to COVID-19 data mining: A deep spatial–temporal prediction model based on tree structure for traffic revitalization index

Lv,

Wang,

Cheng

et al. 2023

Data & Knowledge Engineering

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“…The knowledge contribution of this research article are as follows: The proposal for the root cause analysis improved with the knowledge management approach by using the “Erdos-Renyi model” [ 8 , 9 ]; indeed, this analysis allows to incorporate the maintenance team interactions and several failure modes. It provides a visual description of the failures and the knowledge nodes.…”

Section: Introductionmentioning

confidence: 99%

“…As an example, in the last decade, some approaches have considered the influence of the spatial analysis and the time parameter [7], [8]; for example: In 2012, the bottomup graphic Gaussian model (GGM) with neighborhood similarity [22], it has process with spatial analysis and influence of several failures' modes but low precision with more than one failure mode at the same time. On the other hand, the limits of the knowledge management application were a linear contribution in the root cause analysis [5], with expert systems.…”

Section: Introductionmentioning

confidence: 99%