Accession Number:

AD1023792

Title:

Information Extraction from Large-Multi-Layer Social Networks

Personal Author(s):

• Oselio,Brandon
• Kulesza,Alex
• Hero,Alfred

Corporate Author:

University of Michigan - Ann Arbor Ann Arbor United States

Report Date:

2015-08-06

Abstract:

Social networks often encode community structure using multiple distinct types of links between nodes. In this paper we introduce a novel method to extract information from such multi-layer networks, where each type of link forms its own layer. Using the concept of Pareto optimality, community detection in this multi-layer setting is formulated as a multiple criterion optimization problem. We propose an algorithm for finding an approximate Pareto frontier containing a family of solutions. The power of this approach is demonstrated on a Twitter dataset, where the nodes are hashtags and the layers correspond to 1 behavioral edges connecting pairs of hashtags whose temporal profiles are similar and 2 relational edges connecting pairs of hashtags that appear in the same tweets.

Descriptive Note:

Journal Article

Supplementary Note:

2015 IEEE International Conference on Acoustics, Speech, and Signal Processing , 19 Apr 2015, 24 Apr 2015, Presented at the IEEE International Conference on Acoustics, Speech and Signal Processing (40th) (ICASSP 2015) held in Brisbane, Australia on 19-24 April 2015.

Pages:

0007

Descriptors:

• social networks
• ONLINE COMMUNITIES
• Information retrieval
• data mining
• multiobjective optimization
• algorithms
• Data sets
• Linear programming
• Correlation

Identifiers:

• 10005
• 10012

Subject Categories:

Communities Of Interest:

• Energy and Power Technologies

Modernization Areas:

• Fully Networked C3

Distribution Statement:

Approved For Public Release;

File Size:

0.21MB