Whoops…Nothing found

Try other keywords in your search

Network Insights

 2 Minutes

 0 Likes

 327 Views

What Is It?

Network Insights is a tool that enables users to better interpret the significance of communities generated from their network.


Why Is This Important?

Network Insights enables users to distinguish trends in holistic network behavior. Consider this example involving the Airport Traffic sample dataset. Examining the Community Leaders, we can quickly identify some of the main flight hubs across different regions of the US:



We can also use Network Insights for epidemiology to understand which group of viral strains is most central, indicating potential targets for a vaccine due to their similarities to many other strains.


How?

Steps to run Insights on a Network Graph:

  1. Open the Insights panel by clicking the icon in the toolbar or selecting Insights from the Data Analytics menu.
  2. The Insight cards should automatically populate with information identifying specific communities of interest. If not, click Refresh at the bottom of the panel.
    • Some cards may not be expanded the first time you open the Insights panel in a new network. This is because these Insights rely on additional Network Graph metrics that are not automatically computed. You can generate these Insights by simply clicking on the respective cards.
  3. Selecting a card will highlight the community corresponding to that Insight.
  4. If you right-click a card, you have the option to add this text as an Annotation.


Additional Details

Community Leaders

Community leaders represent nodes with the highest level of connectivity (most coverage) within their community. Since the community leaders are so well connected within their community, analyzing those nodes may help you interpret what the overall community represents relative to the rest of the network.


Largest Community

We highlight the largest community and show the number of nodes in this community. Frequently in network analysis, there is 1 major community with several satellite communities that sit just outside the main community. This insight is useful for quickly contextualizing and orienting yourself around the network.


Most Influential Community

In order to determine the most influential community, a community graph is created where each node represents a community from the original network graph. The edges in the community graph are representative of the collective edge weights between each pair of communities in the original network graph. The most influential community is determined to be the community with the highest PageRank value in the community graph.


Most Tightly-Knit Community

Virtualitics determines the most tightly-knit community by computing the Clustering Coefficient for the network and identifying the community with the highest median clustering coefficient.


The nodes in the most tightly-knit community are highly connected to each other and will have a high clustering coefficient. However, this metric does not take into account any connections to other communities.


Most Central Community

The most central community is determined by computing the Graph Distance metrics. For each node we determine the average of the betweenness and closeness centrality metrics; we can refer to this new metric as the overall centrality measure. Then we determine which community has the highest median value for our overall centrality measure.


The most central community in the network plays an important role in that its nodes are close (in terms of graph distance) to other nodes and in that the most efficient paths through the network tend to pass through these nodes.


Most Isolated Community

The most isolated community is determined by computing the Graph Distance metrics. Specifically, the community with the lowest median Closeness Centrality is the most isolated community.


The most isolated community is weakly connected to the other communities in the network. This implies that the nodes in the community are niche or not alike the other nodes and communities of the network in some way.

Was this article helpful?