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Visual Link Analysis and Pattern Analytics with Graph Databases

Visual Link Analysis and Pattern Analytics with Graph Databases

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Table of content

Pattern Analysis and Applications Overview

A pattern is composed of common characteristics forming a sequence occurring at regular intervals. Pattern Analysis involves an interpretation of the pattern to make predictions of data.

Steps in Pattern Analysis involves -

  • Pattern contains description
  • Intent and Motivation uses analysis patterns
  • External Factors affecting the pattern
  • Implementation of pattern in the analysis model
  • Consequences describe the after-effects of analysis pattern into the application
  • Design helps to achieve design patterns
  • Similar Patterns to discover related patterns

Link Analysis centers on relationships and connections between network nodes. Link Analysis performs matching for known patterns, detection of anomalies, discovers new patterns.

Steps in Link Analysis involves -

  • Count Total Links
  • Check Unique Domains
  • Anchor Text Variance
  • Incoming Links
  • Page performance
  • Link Quality

Challenge for Pattern Analysis

To view the relationship between the user who transacted and received the amount of transaction data.

Transaction data in the form of graph containing -

  • Type
  • Amount
  • Origin
  • Destination
  • Information about the user

Solution Offered for Pattern and Link Analysis

The graph represents the relationship between the origin(the user who transacted to another user) and destination (the user who received the amount paid by the origin).

Tinker graph creates the vertices and edges using the CSV file containing transaction data.

The relationship between the vertices represented in the form of edges. The relationship that exists between the vertices is stored in the form of XML in the .graphml file as an output of the Tinker graph.

This file is viewed by Gephi to visualize the output in the form of the graph.

Building Recommendation Engine with Graph Database

Graph Databases analyze and aggregate data.Recommendation Engines use Collaborative Filtering.

Collaborative Filtering follows these steps -

  • Generate Signals
  • Build Item Recommendation
  • Build a Personal Recommendation
  • Store Results in Database
  • Recommendation API integration
  • Perform Recommendation on User Application
  • Recommendation to Users
  • Users Action or Item Data stored in Database

Recommendation System using Graph Database executes following steps -

  • Import Data to insert source data within the tables
  • Construction of Graph
  • Perform Graph Query
  • Optimize Performance
  • Graph Visualization

Real-Time Recommendation System Applications using Graph Database -

  • Movie Recommendation System
  • E-Commerce Sites
  • Social Media Applications
  • Fraud Detection
  • Search Engines
  • Access Management

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