Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex
EK Lee, H Balasubramanian, A Tsolias, SU Anakwe… - Elife, 2021 - elifesciences.org
Cortical circuits are thought to contain a large number of cell types that coordinate to
produce behavior. Current in vivo methods rely on clustering of specified features of …
produce behavior. Current in vivo methods rely on clustering of specified features of …
Modularity of the ABCD random graph model with community structure
Abstract The Artificial Benchmark for Community Detection (ABCD) graph is a random graph
model with community structure and power-law distribution for both degrees and community …
model with community structure and power-law distribution for both degrees and community …
Fast consensus clustering in complex networks
Algorithms for community detection are usually stochastic, leading to different partitions for
different choices of random seeds. Consensus clustering has proven to be an effective …
different choices of random seeds. Consensus clustering has proven to be an effective …
Community detection algorithm using hypergraph modularity
Community Detection Algorithm Using Hypergraph Modularity | SpringerLink Skip to main
content Advertisement SpringerLink Account Menu Find a journal Publish with us Track your …
content Advertisement SpringerLink Account Menu Find a journal Publish with us Track your …
Evaluating node embeddings of complex networks
A Dehghan-Kooshkghazi, B Kamiński… - Journal of Complex …, 2022 - academic.oup.com
Graph embedding is a transformation of nodes of a graph into a set of vectors. A good
embedding should capture the graph topology, node-to-node relationship and other relevant …
embedding should capture the graph topology, node-to-node relationship and other relevant …
Artificial benchmark for community detection with outliers (ABCD+ o)
The A rtificial B enchmark for C ommunity D etection graph (ABCD) is a random graph model
with community structure and power-law distribution for both degrees and community sizes …
with community structure and power-law distribution for both degrees and community sizes …
An unsupervised framework for comparing graph embeddings
Graph embedding is the transformation of vertices of a graph into set of vectors. A good
embedding should capture the graph topology, vertex-to-vertex relationship and other …
embedding should capture the graph topology, vertex-to-vertex relationship and other …
Ensemble clustering for graphs: comparisons and applications
V Poulin, F Théberge - Applied Network Science, 2019 - Springer
We recently proposed a new ensemble clustering algorithm for graphs (ECG) based on the
concept of consensus clustering. In this paper, we provide experimental evidence to the …
concept of consensus clustering. In this paper, we provide experimental evidence to the …
A multi-purposed unsupervised framework for comparing embeddings of undirected and directed graphs
Graph embedding is a transformation of nodes of a network into a set of vectors. A good
embedding should capture the underlying graph topology and structure, node-to-node …
embedding should capture the underlying graph topology and structure, node-to-node …
Outliers in the ABCD random graph model with community structure (ABCD+ O)
The A rtificial B enchmark for C ommunity D etection graph (ABCD) is a random graph model
with community structure and power-law distribution for both degrees and community sizes …
with community structure and power-law distribution for both degrees and community sizes …