Efficient Information Flow Based on Graphical Network Characteristics

Abstract

In today’s environment of rapid information generation, getting precise information about an event is not only sufficient but getting that information in time (or as early as possible) is rather more important. Further, in certain situations, it may also be equally important to put a control to a sort of outbreak in the network. This calls for the processes and techniques which can be developed to make the information to flow from one source to another as per the desired necessities. Social networks, VANETs, electrical circuit networks, etc. are prime examples where channelized information flow is required to accomplish the desired task. For this, we need to map or mimic the real information flow scenario to some simulative environment or model. In this paper, basic graph theory concepts have been used to model various scenarios of these suggested networks to depict how network characteristics can play a vital role to make the information flow happen in a more systematic and optimized way so that the desired task can be accomplished. The paper takes into consideration degree distribution of the graph, PageRank scores, strongly connected components, weakly connected components, average path length, etc. and other desired characteristics that will guide node to node information flow so that various parameters remain optimized like path length, no. of nodes visited, etc. The graphical model will be mimicked using Stanford Network Analysis Platform (SNAP) to evaluate these basic measure’s characteristics and help us propose different algorithms depending upon the scenarios. Finally, the paper will be concluded with an insight that how the algorithms can be worked out to already existing framework using these network characteristics.