Gephi Network Diagram Minimalism Course

Application of Network in Data Analysis of Single Cell Transcriptome

Matters needing attention in statistical analysis of network data || Why do you want to study the network?

Description of statistical analysis of network data || Operating network data

Notes on Statistical Analysis of Network Data || Visualization of Network Data

Description of Statistical Analysis of Network Data || Descriptive Analysis of Network Data

Notes on Statistical Analysis of Network Data || Mathematical Model of Network Diagram

Matters needing attention in statistical analysis of network data || Statistical model of network diagram

Notes on Statistical Analysis of Network Data || Inference of Network Topology Structure

Network flow is a problem-solving method of analogy flow, which is closely related to linear programming. With the continuous development of network flow theory and application, new topics such as decomposition and synthesis of gain flow, multi-terminal flow, multi-commodity flow and network flow have emerged. The application of network flow has been widely used in many fields, such as communication, transportation, electric power, engineering planning, task assignment, equipment update and computer aided design.

Related definitions of network traffic:

Source point: there are n points, m directed edges, and one point is very special, which is called source point.

Meeting point: another point is also very special, and it can only enter but not exit, which is called meeting point.

Capacity and flow: Each directed edge has two quantities, capacity and flow. The capacity from I to J is usually represented by c[i, j], and the flow is usually f[i, j].

Usually, these sides can be considered as roads, the traffic volume is the traffic volume of this road, and the capacity is the maximum traffic volume that the road can bear. Obviously, traffic

Maximum flow: if the source point is compared to a factory, the problem is to find out the maximum amount of goods that the factory can send, which does not exceed the capacity limit of the road, that is, the maximum flow. Understand the concepts of S (source) and T (sink) first. S is often said to be the source point, and T is the sink point (that is, the starting point and the key point, the same as the shortest path concept). We have a graph that requires the maximum flow from the source to the sink (there can be multiple roads to the sink), which is our maximum flow problem.

The above can be regarded as feature selection.

With the knowledge of traffic measurement on the network link and power distribution on the link between the starting point and the end point, we can accurately predict the traffic between the starting point and the end point. This is called traffic matrix estimation.

The flow matrix is a two-dimensional matrix and its adjacent matrix elements. Tij determines the traffic purchased from node I and node J. Tij value, also known as traffic demand, is the number of data transmissions between each pair of network nodes represented by each demand. In a network composed of four nodes, each node is a traffic source or a traffic set, and the traffic matrix contains 12 requirements (Figure 5a). When nodes 1 and 2 represent traffic sources and nodes 3 and 4 represent traffic destinations, there are only four requirements (fig. 5b).

Chromatographic gravity algorithm

The modeling and analysis of computer communication networks have produced various interesting statistical problems. In this paper, the problem of network chromatography is discussed. Tomography is used to describe two large-capacity inverse problems. The first one deals with passive tomography, that is, collecting aggregated data at the level of a single router/node, with the goal of recovering P-level information. The main problem here is to estimate the start-end transformation matrix. The second method is active tomography, which uses the method of reconstructing link-level information fr for data analysis.

blogs.com/FibonacciHeap/articles/969 1400.html

Research on Regional Logistics Demand Forecasting Based on Gravity Model

/Knowledge Base/Network Capacity Planning

Traffic matrix estimation: a neural network method with extended input and expectation maximization iteration

Working fault scanning/

https://arxiv.org/pdf/0708.0945.pdf