A framework for dynamic link prediction in heterogeneous networks1. (12th August 2013)

• Record Type: Journal Article
• Authors: Aggarwal, Charu C.; Xie, Yan; Yu, Philip S.
• Is Part Of: Statistical analysis and data mining. Volume 7:Number 1(2014)
• Page Start: 14
• Page End: 33
• Publication Date: 2013-08-12
• Subjects: Data mining -- Statistical methods -- Periodicals; 006.312
• DOI: 10.1002/sam.11198
• Languages: English
• ISSNs: 1932-1864

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <p>Network and linked data have become quite prevalent in recent years because of the ubiquity of the web and social media applications, which are inherently network oriented. Such networks are massive, dynamic, contain a lot of content, and may evolve over time. In this paper, we will study the problem of efficient dynamic link inference in temporal and heterogeneous information networks. The problem of efficiently performing dynamic link inference is extremely challenging in massive and heterogeneous information network because of the challenges associated with the dynamic nature of the network, and the different types of nodes and attributes in it. Both the topology and type information need to be used effectively for the link inference process. We propose an effective two‐level scheme which makes efficient macro‐ and micro‐decisions for combining structure and content in a dynamic and time‐sensitive way. The time‐sensitive nature of the links is leveraged in order to perform effective link prediction. We will also study how to apply the method to the problem of community prediction. We illustrate the effectiveness of our technique over a number of real data sets. <italic>Statistical Analysis and Data Mining</italic> 2013 DOI: 10.1002/sam.11198</p> </abstract>