Dynamic Representation and Visualization in Networking for Big Data Using Ai

Abstract

It's hard to figure out what's going on with a lot of network data because both the amount of data and the way it changes over time and space are getting bigger. When nodes are added to or taken out of a network, both the topology of the network as a whole and the properties of its links are dynamic and can change. Using the usual methods for data mining, it is either impossible or too expensive to process large, dynamic networks. Neither of these is the best choice. To do this, we came up with a technology called DNAV, which stands for "dynamic network analysis and visualisation." The AI based algorithm is mostly based on a dynamic graph, which is made up of links that are separated by time. Each edge segment in the network shows how the graph elements in the network, like communication hubs, have changed over time. The proposed static view of dynamic networks can make the process of visually analysing these networks much easier because. To study and analysis malware data classification and Thevisualisation has data filtering modules like time selection, hops settings, entity selection, and edge weight thresholds to make it easier to see large networks. to suggest using AI to make dynamic representations and visualizations of big data that is networked.