Ardi Perception

Understanding and solving complex problems.

Graph Analytics Tools

The human brain is constantly analyzing the situation based on one’s past experience and makes decisions accordingly. It is extremely good at cross referencing data from different senses and connecting the dots. In other words, graphs are the natural way to store and process unstructured data in the real world.

Ardi Analytics contains two main parts: Graph Analytics and Processing. It supports graphical analysis without any coding!

Its efficient analytics include Minimum Spanning Tree (MST), Topological Analysis, Traverse, Shortest Paths and K-core.

The metrics support Centrality and Metrics, Compute PageRank, Link Prediction Indices, Clustering Coefficients and Similarity Ranking.

Ardi's Component Analysis and Retrieval tools help understand Cycles, Egonets, Strongly/Weakly Connected Components, Cliques and Graph Spectral Clustering.

The analytics tools also help make predictions including Missing Links Prediction, Entity Resolution and Risk Propagation.


Predicting Hidden Relationships

With Graph Analytics, Ardi can detect potential missing links by predicting the probability of link existence between two nodes using supervised machine learning methods.

Training Phase: Model the relationship between X and Y using supervised machine learning algorithms.

Prediction Phase: Calculate the link existence based on the new graph and the relationship learned in the training phase.


Anomaly Detection Tools

Ardi detects unsupervised abnormal nodes/links by estimating discrepancies compared to its self or peer group behavior. Various technologies include:

  • Statistical: Estimate a parametric model describing the distribution of the data.
  • Proximity-based: Identify data points far away from the majority.
  • Density-based: Identify data points in regions of low density.
  • Clustering-based: Identify data points that do not belong strongly to any cluster.
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    Use Case

    Amy is a researcher at a gene service company, working with doctors, researchers, professors and analysts. Amy’s primary objective is to explore the genomic data. Such information can help her company to perform ancestry analysis, personalized exercise and diet recommendations, nutritional genomics, genomic medicine, etc. Amy uses Ardi’s Analytics to compare the genomic data and find similarity between two genes.

    With the help of Ardi, she can learn about clients’ genomic composition, trace the migration routes of clients’ ancestors, and even learn the genetic traces of extinct races hidden in clients’ body.

    Also, Ardi helps her to model and predict one’s unborn child’s genes based on the parents’ genes to understand the genetic risks.

    Another use case of the Analytics function is Graphen's work on Monitoring Worldwide COVID-19 Mutations of Millions of Sequenced Viruses since March 2020.


    Learn more about Graphen Ardi

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