Social Networks: The Semantic Metadata of Connections
David Loshin
President
Knowledge Integrity, Inc.
March 8, 2007
8:30 AM - 9:30 AM
Level: Introductory/All Levels
Social network analysis (SNA) is the process of analyzing
the relationships, interactions, and flows between discrete objects
such as people, locations, or things). Much flows move through social
networks – ideas, fashions, market tips, viruses, drugs, money, and
many applications rely on social networks, ranging from the mundane
(movie fans), to the business-oriented (creating a word-of-mouth marketing
campaign), to the criminal (assessing illegal drug activity), to extremely
critical (tracking terrorist activity).
Although social networks are represented in a straightforward manner, (objects are represented as nodes, and a connection between any two nodes is represented as a link between those two nodes), the simplicity of the connection between two objects hides potentially complex associations. The interesting part lies in understanding what those links really mean, and how to measure and assess affinity between different objects. Consequently, the result of the analysis depends on the metadata, taxonomies, and semantics associated with the defined nodes and relationships.
Attendees will learn:
Although social networks are represented in a straightforward manner, (objects are represented as nodes, and a connection between any two nodes is represented as a link between those two nodes), the simplicity of the connection between two objects hides potentially complex associations. The interesting part lies in understanding what those links really mean, and how to measure and assess affinity between different objects. Consequently, the result of the analysis depends on the metadata, taxonomies, and semantics associated with the defined nodes and relationships.
Attendees will learn:
- Nodes and Edges: The Model for Social Networks
- Basic analysis: degrees, betweenness, closeness, and other measures
- SNA metadata: how data about your connections drives the analysis
David Loshin is the president of Knowledge Integrity, Inc,
(www.knowledge-integrity.com), a consulting company focusing on customized
information management solutions including information quality consulting,
information quality training, business intelligence, metadata management,
and data standards management. David is an internationally recognized
expert in Information Quality, contributing to Intelligent Enterprise,
writing a monthly column for DM Review between October 2002 and December
2005, is a quarterly featured columnist for the Data Administration
Newsletter (www.tdan.com), and is the channel expert of the B-EYE-Network’s
Information Quality and Data Integration channel.
David’s book, “Business Intelligence: The Savvy Manager’s Guide” (June 2003) has been hailed as a resource allowing readers to “gain an understanding of business intelligence, business management disciplines, data warehousing, and how all of the pieces work together.” David also wrote “Enterprise Knowledge Management – The Data Quality Approach,” which describes a revolutionary strategy for defining, managing, and implementing business rules affecting Enterprise-wide information quality management.
David’s book, “Business Intelligence: The Savvy Manager’s Guide” (June 2003) has been hailed as a resource allowing readers to “gain an understanding of business intelligence, business management disciplines, data warehousing, and how all of the pieces work together.” David also wrote “Enterprise Knowledge Management – The Data Quality Approach,” which describes a revolutionary strategy for defining, managing, and implementing business rules affecting Enterprise-wide information quality management.