What is graph analytics?
The simple description to this is that Graph analytics is like playing a game of connect the dots, but instead of dots, you have nodes and instead of lines, you have edges. The goal of the game is to find patterns and relationships between the nodes.
By using graph algorithms and relationships in graph databases, graph analytics solutions are uncovering insights in fields like social network analysis, fraud detection, supply chain and search engine optimization.
To be able to have a deep understanding on how to work with graph databases and also graph analytics these are the following graph algorithms you should keep a keen eye on and they include:
- Between Centrality.
- Cycle Detection.
- Community Detection.
- Degree centrality.
- Graph coloring.
- Page Rank .
just to name a few .
The graph algorithms above have been widely used in graph analytics to be able to find the perfect solutions.
In this article we will cover the centrality analysis algorithm describing on how it works,and also demonstrate some of the use cases that this algorithm can be used to find the best solutions
Centrality Analysis Algorithm
Centrality analysis provides information about the node’s importance for an information flow or connectivity of the network. Betweenness centrality is one of the most used centrality metrics. Centrality comes in different flavors and each flavor or a metric defines importance of a node from a different perspective and further provides relevant analytical information about the graph and its nodes.
Betweeness Centrality
Betweenness centrality measures the extent to which a node lies on paths between other nodes in the graph.Thus, nodes with high betweenness may have considerable influence within a network under their control over information passing between others. The calculation of betweenness centrality is not standardized, and there are many ways to solve it.
The betweenness centrality algorithm works by calculating the number of shortest paths that pass through each node in the graph. For each pair of nodes, the algorithm finds all of the shortest paths between them. The betweenness centrality of a node is then the sum of the number of shortest paths that pass through that node, divided by the total number of shortest paths between all pairs of nodes.
The betweenness centrality algorithm can be implemented in both unweighted and weighted graphs. In an unweighted graph, all edges have the same weight. In a weighted graph, each edge has a weight that represents the cost of traversing that edge.
How can betweeness centrality be understood ?
The betweenness centrality algorithm can be used to identify important nodes in a network. For example, in a social network, a node with high betweenness centrality might be a person who is well-connected to many other people. This person could be an important source of information or could help to connect different groups of people together.
Also the betweenness centrality algorithm can also be used to identify bottlenecks in a network. A bottleneck is a node that is frequently used by other nodes to communicate with each other. If a bottleneck node fails, it can disrupt the flow of information or resources in the network.
Sample use case to illustrate understanding of the Centrality Analysis through betweness centrality
Transportation:
1.Rerouting Traffic
Betweenness centrality can be used to reroute traffic during disruptions. By identifying alternative routes that have low betweenness centrality, transportation handlers can help drivers avoid congested areas. For example, during a traffic accident, a traffic routing algorithm could use betweenness centrality to find an alternative route for drivers.
2.Design Smart cities
Betweenness centrality can be used to design smart cities by By identifying nodes with high betweenness centrality, an allow investors or planners focus on improving efficiency and reducing congestion .A good example implementation of betweeness centrality in Kenya is the building of digital smart cities such as Konza City which will improve efficiency and reduce congestion of Nairobi city.
3.Improving Public transportation
Betweenness centrality can be used to improve public transportation by identifying nodes with high betweenness centrality,Allow public users to be able to asses bus stops and train stations easily so as to reduce traffic congestion.
Conclusion
In conclusion graph analytics can be used to undertake/analyze real social use cases that affect our daily lives and also provide clear analysis on how to solve this issues using graph database technologies.
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