Search Results for author: Peter J. Mucha

Found 14 papers, 7 papers with code

One Node at a Time: Node-Level Network Classification

no code implementations3 Aug 2022 Saray Shai, Isaac Jacobs, Peter J. Mucha

Network classification aims to group networks (or graphs) into distinct categories based on their structure.

Classification

Structural underpinnings of control in multiplex networks

no code implementations15 Mar 2021 Pragya Srivastava, Peter J. Mucha, Emily Falk, Fabio Pasqualetti, Danielle S. Bassett

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

1 code implementation25 Jun 2020 Zachary M. Boyd, Nicolas Fraiman, Jeremy L. Marzuola, Peter J. Mucha, Braxton Osting, Jonathan Weare

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.

Dimensionality Reduction Link Prediction

Multilayer Modularity Belief Propagation To Assess Detectability Of Community Structure

1 code implementation13 Aug 2019 William H. Weir, Benjamin Walker, Lenka Zdeborová, Peter J. Mucha

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

2 code implementations3 Apr 2019 Dane Taylor, Mason A. Porter, Peter J. Mucha

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

no code implementations24 Oct 2018 Scott Emmons, Peter J. Mucha

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.

Community Detection

Stochastic Block Models with Multiple Continuous Attributes

no code implementations7 Mar 2018 Natalie Stanley, Thomas Bonacci, Roland Kwitt, Marc Niethammer, Peter J. Mucha

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.

Collaborative Filtering Community Detection +2

Compressing networks with super nodes

1 code implementation13 Jun 2017 Natalie Stanley, Roland Kwitt, Marc Niethammer, Peter J. Mucha

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

1 code implementation12 Jun 2017 William H. Weir, Scott Emmons, Ryan Gibson, Dane Taylor, Peter J. Mucha

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

no code implementations18 Oct 2015 Lucas G. S. Jeub, Michael W. Mahoney, Peter J. Mucha, Mason A. Porter

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

no code implementations7 Jul 2015 Natalie Stanley, Saray Shai, Dane Taylor, Peter J. Mucha

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.

Stochastic Block Model

A testing based extraction algorithm for identifying significant communities in networks

no code implementations3 Dec 2014 James D. Wilson, Simi Wang, Peter J. Mucha, Shankar Bhamidi, Andrew B. Nobel

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.

Community Detection

Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks

1 code implementation15 Mar 2014 Lucas G. S. Jeub, Prakash Balachandran, Mason A. Porter, Peter J. Mucha, Michael W. Mahoney

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

1 code implementation22 Feb 2009 Mason A. Porter, Jukka-Pekka Onnela, Peter J. Mucha

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

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