EGBTER: Capturing Degree Distribution, Clustering Coefficients, and Community Structure in a Single Random Graph Model
Random graph models are important constructs for data analytic applications as well as pure mathematical developments, as they provide capabilities for network synthesis and principled analysis. Several models have been developed with the aim of faithfully preserving important graph metrics and substructures. With the goal of capturing degree distribution, clustering coefficient, and communities in a single random graph model, we propose a new model to address shortcomings in a progression of network modeling capabilities. The Block Two-Level Erdos-Rényi (BTER) model of Seshadhri et aI., designed to allow prescription of expected degree and clustering coefficient distributions, neglects community modeling, while the Generalized BTER (GBTER) model of Bridges et aI., designed to add community modeling capabilities to BTER, struggles to faithfully represent all three characteristics simultaneously. In this work, we fit BTER and two GBTER configurations to several real-world networks and compare the results with that of our new model, the Extended GBTER (EGBTER) model. Our results support that EBGTER adds a community-modeling flexibility to BTER, while retaining a satisfactory level of accuracy in terms of degree and clustering coefficient. Our insights and empirical testing of previous models as well as the new model are novel contributions to the literature.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1479801
- Resource Relation:
- Conference: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2018) - Barcelona, , Spain - 8/28/2018 9:00:00 AM-8/31/2018 9:00:00 AM
- Country of Publication:
- United States
- Language:
- English
Similar Records
Multi-Level Anomaly Detection on Time-Varying Graph Data
A multi-level anomaly detection algorithm for time-varying graph data with interactive visualization