Inversion method for content-based networks
arXiv:0711.1128 · doi:10.1103/PhysRevE.77.036122
Abstract
In this paper, we generalize a recently introduced Expectation Maximization (EM) method for graphs and apply it to content-based networks. The EM method provides a classification of the nodes of a graph, and allows to infer relations between the different classes. Content-based networks are ideal models for graphs displaying any kind of community or/and multipartite structure. We show both numerically and analytically that the generalized EM method is able to recover the process that led to the generation of such networks. We also investigate the conditions under which our generalized EM method can recover the underlying contents-based structure in the presence of randomness in the connections. Two entropies, Sq and Sc, are defined to measure the quality of the node classification and to what extent the connectivity of a given network is content-based. Sq and Sc are also useful in determining the number of classes for which the classification is optimal.
12 pages, 10 figs
References in corpus (26)
- The structure and function of complex networks
- Community structure in social and biological networks
- Finding and evaluating community structure in networks
- Modularity and community structure in networks
- Fast algorithm for detecting community structure in networks
- Uncovering the overlapping community structure of complex networks in nature and society
- Hierarchical organization of modularity in metabolic networks
- Resolution limit in community detection
- Defining and identifying communities in networks
- Statistical Mechanics of Community Detection
- Quantifying social group evolution
- Modularity from Fluctuations in Random Graphs and Complex Networks
- Synchronization reveals topological scales in complex networks
- Detecting fuzzy community structures in complex networks with a Potts model
- Mixture models and exploratory analysis in networks
- Class of correlated random networks with hidden variables
- Distance, dissimilarity index, and network community structure
- A General Formalism for Inhomogeneous Random Graphs
- Limited resolution in complex network community detection with Potts model approach
- Network Landscape from a Brownian Particle's Perspective
- Finding instabilities in the community structure of complex networks
- When are networks truly modular?
- Diffusion on Complex Networks : A way to probe their large scale topological structures
- Spontaneous Self-Assembly of Transcription Factor Based Gene Regulation Networks
- Analytical Solution of a Stochastic Content Based Network Model
- Random model for RNA interference yields scale free network
Cited by in corpus (17)
- Community detection in graphs
- Community detection algorithms: a comparative analysis
- Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities
- Clustering and Community Detection in Directed Networks: A Survey
- Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics
- Community detection, link prediction, and layer interdependence in multilayer networks
- Maximum likelihood: extracting unbiased information from complex networks
- Coauthorship and Citation Networks for Statisticians
- Statistical significance of communities in networks
- Exploring the structural regularities in networks
- Wikipedia information flow analysis reveals the scale-free architecture of the Semantic Space
- Complex Networks and Symmetry I: A Review
- Automatic Network Fingerprinting through Single-Node Motifs
- Detecting groups of similar components in complex networks
- Spectral clustering algorithms for the detection of clusters in block-cyclic and block-acyclic graphs
- Stability of Maximum likelihood based clustering methods: exploring the backbone of classifications (Who is keeping you in that community?)
- On role extraction for digraphs via neighbourhood pattern similarity