As the variety and volume of our data increases, more sophisticated methods are required to unveil the valuable information hidden in it. As the number of data-sources increase, it is vital that we define the underlying the relationship between them. Social Media, sensors, surveillance intelligence, industrial control systems and connected devices have become visibly important in the data-procurement scenario. Although they might appear to be discrete and isolated on the surface, they are all nodes of a bigger network that binds them with complex relationships. Graph Analytics defines and strengthens these relationships by using mathematical concepts of graph theory.
What is Graph Analytics?
Graph analytics is an analytical tool that leverages graphs to analyze, codify, and visualize links that exist between databases or devices in a network. A graph can be interpreted as a mathematical structure comprising of nodes or vertices, connected by edges. While the nodes represent the different entities of the system, the edge are illustrative of the relationship between them. However, if graphs are extrapolated to the context of data sciences, they are rather powerful and organized data structures. These, in turn, represent complex dependencies in the data. Graph analytics is used to model pairwise relationships between people and/or objects in any system. This would help one in generating insights about the strength and direction of the relationship.
Having defined graphs and graph analytics, it is necessary to explain the components of the two. The strength of the relationship between nodes is defined by how frequently the nodes communicate with each other, which other nodes participate in the communication and what the importance of the node is in the communication, based on the context of analysis. The direction, however indicates if it is a 2-way communication or not, who initiates it and if the conversation gets forwarded to any other node. The nodes have characteristic properties which can provide information about these entities. The edges, being the more important component, might connect nodes to other nodes or to its properties. These links might reveal definitive patterns when studied in detail. Studying the components of the graphs can thus reveal a bevy of metadata about the complete system.
Different kinds of graph analysis
Graph analytics offers sophisticated abilities for analyzing relationships unlike conventional analytics algorithms that focus on summarizing, aggregating and reporting on data. There are four different types of analyses done using graphs. They include:
1 – Path analysis: This is done to analyze traits of the connections between a pair of entities, for example, the distance between them. This in turn, helps one to understand the risks and exposure faced by the connection.
2 – Connectivity analysis: It can be used to assess the strength of links between nodes. The application of connectivity analysis can be found in identifying weak links in a power grid.
3 – Centrality Analysis: It helps one in identifying the relevance of the different entities in your network and analyzing the central entities. One can use this to find the most highly accessed website or webpages for further analysis.
4 – Community Detection: This method is a distance and density–based analysis is used to identify communities of people or devices in a huge network and therefore, analyse if they are transient. Detecting target audience by identifying people on a social network can be an example of the same.
5 – Sub-graph isomorphism: This is done to identify pattern of relationships. It can be used to validate hypotheses and isolate abnormal situations. Applications of the same are fraud detection and identifying hacker attacks.
Using Graph Analytics…
Graphs can be modelled to replicate any scenario in the biological, social, physical or information systems. This can hence, be used to simulate the actual test-cases and dive deeper into them before scaling them up at a larger level. Some useful applications have been found in the popular analytical method, clustering. Clustering is used to identify a group of nodes (could be KPIs, macro-economic factors or traits of an IT system) based on their characteristics and similarity in behavior. This information can in turn, be used to optimize and adjust the factor to improve the performance of the network. Example would be to identify drivers of sales growth of a company.
Graph analytics can also be used in assigning page ranks to web-pages for analyzing the performance of the same. It is widely done in social media analytics, recommendation engines and various other biological applications. Some other use-cases are:
1 – Graph theory has found wide application in sociology. It could be used to measure an actors’ reputation or to explore rumor spreading. That however uses multiple other facets of a social network analysis software.
2 – Graph Algorithms (also called network analysis) is used to address relationship-based problems in industries like manufacturing, energy, gas exploration, travel, biology, conservation, computer chip design, chemistry, physics, higher education research, government, security, defense and many other fields. Moreover, pairing graph analytics with conventional analytical tools like shortest path analysis, cutting or partitioning can help one solve complex business and scientific problems.
3 – Theoretical problems of graph analytics has helped algorithms like NLP to build stronger application in text analytics or behavioral analysis. Traditionally, semantics and syntax are based on tree-based structures, also modeled on hierarchical graphs.
Beyond these, graph-theory has found extensive application in social-media analysis. The link structure of any website/page can be represented in a directed graph, with the nodes be different pages or sections and the edges being the links or routes defined in the sites. Since the growth of the social-media network is rapid and huge, the applications are endless too. It is needless to say that graph theory will prove to be useful in almost every sphere of business-decision taking and regular-daily lives.
However, it will be necessary to couple it with more conventional analytical and statistical methods. Although there will be problems that might look unrelated to graphs at first, a little deeper understanding of the same will help one represent almost every situation as a graph. Having said that, it is safe to conclude that graphs are versatile mathematical equations that can help you visualize any business problem in a clearer way, so as to reveal hidden traits and trends.