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Found 14 papers, 7 papers with code
One Node at a Time: Node-Level Network Classification
Network classification aims to group networks (or graphs) into distinct categories based on their structure.
Structural underpinnings of control in multiplex networks
For this purpose, we calculate the exact expression of optimal control energy in terms of layer spectra and the relative alignment between the eigenmodes of the input layer and the deeper target layer.
A metric on directed graphs and Markov chains based on hitting probabilities
The shortest-path, commute time, and diffusion distances on undirected graphs have been widely employed in applications such as dimensionality reduction, link prediction, and trip planning.
Multilayer Modularity Belief Propagation To Assess Detectability Of Community Structure
We compare our approach with a widely used community detection tool, GenLouvain, across a range of synthetic, multilayer benchmark networks, demonstrating that our method performs comparably to the state of the art.
Social and Information Networks Data Analysis, Statistics and Probability Physics and Society
Tunable Eigenvector-Based Centralities for Multiplex and Temporal Networks
Characterizing the importances of nodes in social, biological, information and technological networks is a core topic for the network-science and data-science communities.
Social and Information Networks Physics and Society 05C82
A Map Equation with Metadata: Varying the Role of Attributes in Community Detection
In this work, we introduce a tuning parameter to the content map equation that allows users of the Infomap community detection algorithm to control the metadata's relative importance for identifying network structure.
Stochastic Block Models with Multiple Continuous Attributes
While there are recent examples in the literature that combine connectivity and attribute information to inform community detection, our model is the first augmented stochastic block model to handle multiple continuous attributes.
Compressing networks with super nodes
Community detection is a commonly used technique for identifying groups in a network based on similarities in connectivity patterns.
Social and Information Networks Physics and Society
Post-processing partitions to identify domains of modularity optimization
We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm to prune and prioritize different network community structures identified across multiple runs of possibly various computational heuristics.
Social and Information Networks Physics and Society
A Local Perspective on Community Structure in Multilayer Networks
The analysis of multilayer networks is among the most active areas of network science, and there are now several methods to detect dense "communities" of nodes in multilayer networks.
Social and Information Networks Probability Adaptation and Self-Organizing Systems Data Analysis, Statistics and Probability Physics and Society
Clustering Network Layers With the Strata Multilayer Stochastic Block Model
While each layer provides its own set of information, community structure across layers can be collectively utilized to discover and quantify underlying relational patterns between nodes.
A testing based extraction algorithm for identifying significant communities in networks
In addition, we carry out a simulation study to assess the effectiveness of ESSC in networks with various types of community structure, including networks with overlapping communities and those with background vertices.
Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks
In this paper, we adopt a complementary perspective that "communities" are associated with bottlenecks of locally-biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size.
Social and Information Networks Disordered Systems and Neural Networks Combinatorics Adaptation and Self-Organizing Systems Physics and Society
Communities in Networks
We survey some of the concepts, methods, and applications of community detection, which has become an increasingly important area of network science.
Physics and Society Statistical Mechanics Computers and Society Discrete Mathematics Statistics Theory Adaptation and Self-Organizing Systems Computational Physics Statistics Theory
