Skip to Main Content
 

Global Search Box

 
 
 
 

ETD Abstract Container

Abstract Header

Mining Dynamic Structures in Complex Networks

McCallen, Scott J.

Abstract Details

2007, MS, Kent State University, College of Arts and Sciences / Department of Computer Science.

Complex networks have attracted much attention across many scientific disciplines ranging from sociology to microbiology to describe systems of interacting units. Most of the studies have mainly focused on the topology of the network. However, in many real world applications, the edges and vertices have associated attributes that are frequently represented as edge or vertex weights. Furthermore, these weights are often not static, instead changing with time and forming a time series. Hence, to fully understand the dynamics of a complex network, we must consider both topology and related time series data. Despite the rapid accumulation of such data, understanding the dynamic nature of complex networks remains a complicated and mostly unexplored task.

In this work, we propose two novel mining approaches to identify dynamic structures that account for both temporal and topological characteristics in complex networks. The first approach is the definition and identification of time series trends and trend motifs. A trend motif describes a recurring subgraph where all of its vertices or edges display similar temporal trends. Given this, each occurrence can help reveal significant events in a complex system and frequent motifs may aid in uncovering dynamic rules of change for the system. In our second approach, we define the dynamic module, which expands and improves upon our first model. Essentially, a dynamic module is a set of connected vertices where the time series associated with each vertex forms certain structures in the temporal domain.

We have developed efficient mining algorithms to extract these interesting dynamic structures and our experimental validation using datasets ranging from the stock market, world trade, to a protein interaction network, has demonstrated the efficiency and effectiveness of our approach.

Ruoming Jin, PhD (Advisor)
72 p.

Recommended Citations

Citations

  • McCallen, S. J. (2007). Mining Dynamic Structures in Complex Networks [Master's thesis, Kent State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=kent1204154279

    APA Style (7th edition)

  • McCallen, Scott. Mining Dynamic Structures in Complex Networks. 2007. Kent State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=kent1204154279.

    MLA Style (8th edition)

  • McCallen, Scott. "Mining Dynamic Structures in Complex Networks." Master's thesis, Kent State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=kent1204154279

    Chicago Manual of Style (17th edition)