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Found 10 papers, 7 papers with code
Discovery of Multi-Level Network Differences Across Populations of Heterogeneous Connectomes
A connectome is a map of the structural and/or functional connections in the brain.
Overlapping community detection in networks via sparse spectral decomposition
We consider the problem of estimating overlapping community memberships in a network, where each node can belong to multiple communities.
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs
We examine two related, complementary inference tasks: the detection of anomalous graphs within a time series, and the detection of temporally anomalous vertices.
Methodology
The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks
We describe how this omnibus embedding can itself induce correlation, leading us to distinguish between inherent correlation -- the correlation that arises naturally in multisample network data -- and induced correlation, which is an artifice of the joint embedding methodology.
Graph matching between bipartite and unipartite networks: to collapse, or not to collapse, that is the question
Graph matching consists of aligning the vertices of two unlabeled graphs in order to maximize the shared structure across networks; when the graphs are unipartite, this is commonly formulated as minimizing their edge disagreements.
Simultaneous prediction and community detection for networks with application to neuroimaging
Here we present a method for supervised community detection, aiming to find a partition of the network into communities that is most useful for predicting a particular response.
Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities
Information-theoretic quantities, such as conditional entropy and mutual information, are critical data summaries for quantifying uncertainty.
Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks
Given a pair of graphs with the same number of vertices, the inexact graph matching problem consists in finding a correspondence between the vertices of these graphs that minimizes the total number of induced edge disagreements.
Joint Embedding of Graphs
Feature extraction and dimension reduction for networks is critical in a wide variety of domains.
Efficient Distributed Estimation of Inverse Covariance Matrices
In distributed systems, communication is a major concern due to issues such as its vulnerability or efficiency.
