Duration: 1 day

Network analysis is not a new topic, but the digitisation of so many daily business, organisational, and personal activities as well as the migration of so much communication to online platforms makes analysis of data networks more important now than ever before. Insights about fraudsters, customers, influencers, products and competitors that cannot be uncovered using other analysis techniques can be illuminated through network analysis. Through real world examples, discussions, and live code demonstrations this one-day workshop designed for analytics professionals introduces the most important network analysis techniques that can be used in data analytics applications.

At Course Completion:

This workshop has been designed to equip delegates with the most important network analysis techniques, and an understanding of how they should be applied to build real-world-relevant solutions. After completing the workshop delegates will be able to:

  • Frame a wide range of problems as network analysis problems and solve them using appropriate techniques
  • Understand the structure and characteristics of networks
  • Perform basic network analysis using measures of degree, density, centrality, etc.
  • Visualise networks using appropriate tools and techniques
  • Perform anomaly detection to identify unusual patterns in networks
  • Apply community finding to discover group structures in networks
  • Perform classification tasks on networks using appropriate node featurisation.

Content:

  • Framing problems as network analysis problems
  • Creating and storing network data
  • Characterising networks using measures of degree, density, centrality, etc.
  • Demonstration: Characterising networks
  • Network analysis techniques for anomaly detection
  • Demonstration: Anomaly detection
  • Finding community structure in networks
  • Demonstration: Anomaly detection
  • Using network analysis to generate features for other machine learning tasks
  • Building motif-based features
  • Demonstration: Classifying network nodes
Social Network Analytics

Prerequisites:

To attend this course delegates should be familiar with fundamental concepts in data manipulation, descriptive statistics, and machine learning. Specifically, delegates should be comfortable building and evaluating classification models (using techniques such as logistic regression, decision trees, support vector machines or random forests).

Demonstrations:

The live code demonstrations during the workshop will use the Python programming language and relevant Python packages (e.g. pandas, scikit-learn, and nltk). While familiarity with these is not required it would be useful. A list of specific functionality with which delegates should be familiar, and suggested online revision materials, will be circulated to delegates before the workshop.

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About The Trainers

Dr. Derek Greene
Dr. Derek Greene
Dr. Derek Greene has over 13 years’ experience in the field of machine learning, with a PhD in Computer Science from Trinity College Dublin, and over 40 research papers presented at international conferences and published in journals. He currently leads a research group which focuses on algorithms and applications in areas such as social network analysis and text mining.