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Found 17 papers, 15 papers with code
Faster Approximation Algorithms for Parameterized Graph Clustering and Edge Labeling
Graph clustering is a fundamental task in network analysis where the goal is to detect sets of nodes that are well-connected to each other but sparsely connected to the rest of the graph.
On the Optimal Recovery of Graph Signals
Learning a smooth graph signal from partially observed data is a well-studied task in graph-based machine learning.
Seven open problems in applied combinatorics
We present and discuss seven different open problems in applied combinatorics.
Correlation Clustering via Strong Triadic Closure Labeling: Fast Approximation Algorithms and Practical Lower Bounds
Correlation clustering is a widely studied framework for clustering based on pairwise similarity and dissimilarity scores, but its best approximation algorithms rely on impractical linear programming relaxations.
Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components
We develop the first approximation algorithms for this problem, where the approximations can be quickly computed via reduction to a sparse graph cut problem, with graph sparsity controlled by the desired approximation factor.
The Generalized Mean Densest Subgraph Problem
Finding dense subgraphs of a large graph is a standard problem in graph mining that has been studied extensively both for its theoretical richness and its many practical applications.
Generative hypergraph clustering: from blockmodels to modularity
Many graph algorithms for this task are based on variants of the stochastic blockmodel, a random graph with flexible cluster structure.
Hypergraph Clustering for Finding Diverse and Experienced Groups
In contrast to related problems on fair or balanced clustering, we model diversity in terms of variety of past experience (instead of, e. g., protected attributes), with a goal of forming groups that have both experience and diversity with respect to participation in edge types.
Minimizing Localized Ratio Cut Objectives in Hypergraphs
However, there are only a few specialized approaches for localized clustering in hypergraphs.
Parameterized Correlation Clustering in Hypergraphs and Bipartite Graphs
For a certain choice of parameters it is also related to our hypergraph objective.
Clustering in graphs and hypergraphs with categorical edge labels
Here, we develop a computational framework for the problem of clustering hypergraphs with categorical edge labels --- or different interaction types --- where clusters corresponds to groups of nodes that frequently participate in the same type of interaction.
Learning Resolution Parameters for Graph Clustering
We begin by formalizing the notion of a parameter fitness function, which measures how well a fixed input clustering approximately solves a generalized clustering objective for a specific resolution parameter value.
A Parallel Projection Method for Metric Constrained Optimization
In this paper we present a parallel projection method for metric-constrained optimization which allows us to speed up the convergence rate in practice.
Flow-Based Local Graph Clustering with Better Seed Set Inclusion
Our first contribution is a generalized objective function that allows practitioners to place strict and soft penalties on excluding specific seed nodes from the output set.
Social and Information Networks Data Structures and Algorithms
A Projection Method for Metric-Constrained Optimization
We outline a new approach for solving optimization problems which enforce triangle inequalities on output variables.
Unifying Sparsest Cut, Cluster Deletion, and Modularity Clustering Objectives with Correlation Clustering
Graph clustering, or community detection, is the task of identifying groups of closely related objects in a large network.
Data Structures and Algorithms Social and Information Networks
Correlation Clustering with Low-Rank Matrices
In this paper we explore how to solve the correlation clustering objective exactly when the data to be clustered can be represented by a low-rank matrix.
