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Li WM, Li Z, Luvembe AMO, Yang C. "Influence maximization algorithm based on Gaussian propagation model." Information Sciences. 2021;568:Pages 386-402. AbstractScience Direct

The influence of each entity in a network is a crucial index of the network information dissemination. Greedy influence maximization algorithms suffer from time efficiency and scalability issues. In contrast, heuristic influence maximization algorithms improve efficiency, but they cannot guarantee accurate results. Considering this, this paper proposes a Gaussian propagation model based on the social networks. Multi-dimensional space modeling is constructed by offset, motif, and degree dimensions for propagation simulation. This space’s circumstances are controlled by some influence diffusion parameters. An influence maximization algorithm is proposed under this model, and this paper uses an improved CELF algorithm to accelerate the influence maximization algorithm. Further, the paper evaluates the effectiveness of the influence maximization algorithm based on the Gaussian propagation model supported by theoretical proofs. Extensive experiments are conducted to compare the effectiveness and efficiency of a series of influence maximization algorithms. The results of the experiments demonstrate that the proposed algorithm shows significant improvement in both effectiveness and efficiency.

Luvembe AM, Mutai H. "Big Data Framework for Kenya’s County Governments." Journal of Computer and Communications. 2019;07(01):1-9. AbstractFull Text Link

Digitalization is transforming governments across the globe. At the national level, down to regional and multiple departments in the public institutions, unprecedented change is occurring exponentially as a result of massive digitalization. Digitalization is compelling governments at all levels to embrace voluminous data and institute appropriate multi-channel platforms to support digital transformation. While this is the case, most governments have been caught unprepared thwarting maximum benefits spurred by digitalization. Inherently, the social media and e-participation tools for generating huge amount of data have convoluted most governments’ appetite in Big Data management. This situation is further compounded with the slow pace of adoption of these technological tools by citizens and the public sectors. For enhanced e-citizen satisfaction and engagement, as well as e-participation processes, public institutions need to promote engagement and collaboration. In view of advancing benefits to their citizens, public institutions need to institute appropriate measures to collect citizen’s data. The information collected is vital for public institutions in actualizing what services the citizens want. Using literature reviews and cases, the authors examine Big Data benefits in counties and propose a Big Data model to improve efficiency of e-governance services and productivity in county governments. The authors demonstrate Big Data framework has the aptitude of molding citizen’s opinion in county decision making process. Better use of e-technologies is shown in the proposed model which illustrates sharing resources among various data analytics sources. Our proposed framework based on Big Data analytics is a viable initiative to progress effectiveness and productivity, strengthen citizen engagement and participation and encourage decision-making in e-governance services delivery in the counties.

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