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  • Open Access


    Colouring of COVID-19 Affected Region Based on Fuzzy Directed Graphs

    Rupkumar Mahapatra1, Sovan Samanta2, Madhumangal Pal1, Jeong-Gon Lee3,*, Shah Khalid Khan4, Usman Naseem5, Robin Singh Bhadoria6

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1219-1233, 2021, DOI:10.32604/cmc.2021.015590

    Abstract Graph colouring is the system of assigning a colour to each vertex of a graph. It is done in such a way that adjacent vertices do not have equal colour. It is fundamental in graph theory. It is often used to solve real-world problems like traffic light signalling, map colouring, scheduling, etc. Nowadays, social networks are prevalent systems in our life. Here, the users are considered as vertices, and their connections/interactions are taken as edges. Some users follow other popular users’ profiles in these networks, and some don’t, but those non-followers are connected directly to… More >

  • Open Access


    Average Convergence for Directed & Undirected Graphs in Distributed Systems

    Ali Mustafa1,2, M Najam ul Islam1, Salman Ahmed1,3,*

    Computer Systems Science and Engineering, Vol.37, No.3, pp. 399-413, 2021, DOI:10.32604/csse.2021.015575

    Abstract Consensus control of multi-agent systems is an innovative paradigm for the development of intelligent distributed systems. This has fascinated numerous scientific groups for their promising applications as they have the freedom to achieve their local and global goals and make their own decisions. Network communication topologies based on graph and matrix theory are widely used in a various real-time applications ranging from software agents to robotics. Therefore, while sustaining the significance of both directed and undirected graphs, this research emphases on the demonstration of a distributed average consensus algorithm. It uses the harmonic mean in… More >

  • Open Access


    Causality Learning from Time Series Data for the Industrial Finance Analysis via the Multi-Dimensional Point Process

    Liangliang Shi1,2, Peili Lu3, Junchi Yan4,5,*

    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 873-885, 2020, DOI:10.32604/iasc.2020.010121

    Abstract Causality learning has been an important tool for decision making, especially for financial analytics. Given the time series data, most existing works construct the causality network with the traditional regression models and estimate the causality by pairs. To fulfil a holistic one-shot inference procedure over the whole network, we propose a new causal inference method for the multidimensional time series data, specifically related to some case studies for the industrial finance analytics. Specifically, the time series are first converted to the event sequences with timestamps by fluctuation the detection, and then a multidimensional point process More >

  • Open Access


    The Optimization Reachability Query of Large Scale Multi-Attribute Constraints Directed Graph

    Kehong Zhang, Keqiu Li

    Computer Systems Science and Engineering, Vol.33, No.2, pp. 71-85, 2018, DOI:10.32604/csse.2018.33.071

    Abstract Today, many applications such as social network and biological network develop rapidly,the graph data will be expanded constantly on a large scale. Some classic methods can not effectively solve this scale of the graph data. In the reachability query, many technologies such as N-Hop, tree, interval labels, uncertain graph processing are emerging, they also solve a lot of questions about reachability query of graph. But, these methods have not put forward the effective solution for the new issues of the multiattribute constraints reachability on directed graph. In this paper, TCRQDG algorithm effectively solves this new More >

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