Network & Graph Theory
To navigate globalized world where everything is interlinked and multi-dimensional - politics, people, companies, finance, social trends, and economies we need tools that can structure this complexity. Often strategies fail because organizations attempt to place complex systems into a black-and-white categories or linear models. This removes context in favor of synthetic classification - a massive risk to any strategy or investment decision.
Network analysis also called graph analytics, excels at exploring both structured and unstructured complex data sets - surfacing how people, regions, events, and trends are connected. This allows organizations to find risks, blind spots, and opportunities not possible with traditional analytics or dashboards.
The Insights extracted can be used to shape business decisions or summarize influential and emerging narratives. Business leaders are often surprised by the complexity within their initial area of focus. While graph Combining network analytics with advanced natural language processing allows firms to quickly find connections between unstructured data to see how narratives and thematics intersect at a level of granularity and context not possible with traditional research.
Network Metrics
Centrality: Nodes with high betweenness centrality have many thematics which extend across the network. Nodes with low betweenness centrality do not connect to other nodes/clusters beyond their immediate vicinity.
Degree: Degree measures the number of shared connections for a node or cluster (clusters are made of many similar nodes).
Flow: Flow represents the combined strength of a node’s connections. Nodes with a high flow have a more shared language and more connections with other nodes in the network. Nodes with a low flow contain thematics that are peripheral to the network.
Inter-Cluster Connectivity: Nodes with high inter-cluster connectivity contain language shared by nodes in multiple different clusters. A node with low Inter-Cluster Connectivity is not connected to many other clusters. The metric helps us identify thematics and entities that bridge multiple topics so that one strategy can encapsulate multiple issues or opportunities.
Entity Extraction
In addition to understanding the overall trends, algorithms can extract the entities associated with the clusters and nodes (circles). The graph below is the SAME data from the network above, only the visual shows how people and institutions are connected.