Network topology generators: degree-based vs. structural
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
The CoralReef Software Suite as a Tool for System and Network Administrators
LISA '01 Proceedings of the 15th USENIX conference on System administration
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
BLINC: multilevel traffic classification in the dark
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Systematic topology analysis and generation using degree correlations
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Orbis: rescaling degree correlations to generate annotated internet topologies
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Network monitoring using traffic dispersion graphs (tdgs)
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Tuning Topology Generators Using Spectral Distributions
SIPEW '08 Proceedings of the SPEC international workshop on Performance Evaluation: Metrics, Models and Benchmarks
Graph Edit Distance without Correspondence from Continuous-Time Quantum Walks
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Internet traffic classification demystified: myths, caveats, and the best practices
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
Exploiting dynamicity in graph-based traffic analysis: techniques and applications
Proceedings of the 5th international conference on Emerging networking experiments and technologies
Graph-based P2P traffic classification at the internet backbone
INFOCOM'09 Proceedings of the 28th IEEE international conference on Computer Communications Workshops
Weighted spectral distribution for internet topology analysis: theory and applications
IEEE/ACM Transactions on Networking (TON)
Link homophily in the application layer and its usage in traffic classification
INFOCOM'10 Proceedings of the 29th conference on Information communications
Network topologies: inference, modeling, and generation
IEEE Communications Surveys & Tutorials
| Hi-index | 0.00 |
This paper proposes a novel non-parametric technique for clustering networks based on their structure. Many topological measures have been introduced in the literature to characterize topological properties of networks. These measures provide meaningful information about the structural properties of a network, but many networks share similar values of a given measure [1]. Furthermore, strong correlation between these measures occur on real-world graphs [2], so that using them to distinguish arbitrary graphs is difficult in practice [3]. Although a very complicated way to represent the information and the structural properties of a graph, the graph spectrum [4] is believed to be a signature of a graph [5]. A weighted form of the distribution of the graph spectrum, called the weighted spectral distribution (WSD), is proposed here as a feature vector. This feature vector may be related to actual structure in a graph and in addition may be used to form a metric between graphs; thus ideal for clustering purposes. To distinguish graphs, we propose to rely on two ways to project a weighted form of the eigenvalues of a graph into a low-dimensional space. The lower dimensional projection, turns out to nicely distinguish different classes of graphs, e.g. graphs from network topology generators [6-8], Internet application graphs [9], and dK-random graphs [10]. This technique can be used advantageously to separate graphs that would otherwise require complex sets of topological measures to be distinguished [9].