network flow model social network analysis definition

Let us first start with what do we mean by Social Networks. This tutorial assumes that the reader is familiar with the basic syntax of Python, no previous knowledge of SNA is expected. The focus of this tutorial is to teach social network analysis (SNA) using Python and NetworkX, a Python library for the study of the structure, dynamics, and functions of complex networks.

The ability to analyze these networks and make informed decisions based on them is a skill that is important for any data analyst. They are playing a significant role in our day to day lives from spreading useful information to influencing national elections. Networks are everywhere, networks of roads, a network of friends and followers on social media, and a network of office colleagues. 1939).If you want to learn about Network Analysis, take DataCamp's Network Analysis in Python (Part 1) course. Colebrook, 'Turbulent flow in pipes, with particular reference to the transition region between smooth and rough pipe laws,' Jour. Moody, 'Friction factors for pipe flow,' Trans. Houghtalen, 'Fundamentals of hydraulic Engineering Systems' Prentice Hall, Upper Saddle River, NJ.

Q instead (with | Q| the absolute value of Q) for the formulation so that any sign changes will reflect appropriately in the resulting head-loss calculation.

Create a separate equation for each loop where the head losses are added up, but instead of squaring Q, use | Q|

The literal friction loss equations will use a term called Q 2, but we want to preserve any changes in direction. Then, use a Root-finding algorithm to find Q values that satisfy all the equations. The more modern method is simply to create a set of conditions from your junctions and head-loss criteria. However, we should use the results from the first loop if we progress to any other loops. It is ok to do this adjustment, however, because our flow changes won't alter condition 1, and therefore, our other loops will still satisfy condition 1. This adjustment won't solve the problem, since with most networks we will have several loops. The clockwise specifier (c) means only the flows that are moving clockwise in our loop, while the counter-clockwise specifier (cc) is only the flows that are moving counter-clockwise. If the actual sum of our head loss is not equal to 0, then we will adjust all the flows in the loop by an amount given by the following formula, where a positive adjustment is in the clockwise direction. In order to satisfy the second condition, we should end up with 0 about the loop if the network is completely solved. We add up the head losses according to the Darcy–Weisbach equation for each pipe if Q is in the same direction as our loop like Q1, and subtract the head loss if the flow is in the reverse direction, like Q4. Given a starting node, we work our way around the loop in a clockwise fashion, as illustrated by Loop 1. After the initial guess is made, then, a loop is considered so that we can evaluate our second condition. That is, if Q7 enters a junction and Q6 and Q4 leave the same junction, then the initial guess must satisfy Q7 = Q6 + Q4. In this formulation, first you go through and create guess values for the flows in the network. The classical approach for solving these networks is to use the Hardy Cross algorithm.

Network flow model social network analysis definition