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Found 9 papers, 2 papers with code
A Differentially Private Clustering Algorithm for Well-Clustered Graphs
We study differentially private (DP) algorithms for recovering clusters in well-clustered graphs, which are graphs whose vertex set can be partitioned into a small number of sets, each inducing a subgraph of high inner conductance and small outer conductance.
A Sublinear-Time Spectral Clustering Oracle with Improved Preprocessing Time
We address the problem of designing a sublinear-time spectral clustering oracle for graphs that exhibit strong clusterability.
Sublinear-Time Clustering Oracle for Signed Graphs
We provide a local clustering oracle for signed graphs with such a clear community structure, that can answer membership queries, i. e., "Given a vertex $v$, which community does $v$ belong to?
Towards a Query-Optimal and Time-Efficient Algorithm for Clustering with a Faulty Oracle
In this model, given a set of $n$ items which belong to $k$ unknown groups (or clusters), our goal is to recover the clusters by asking pairwise queries to an oracle.
Local Algorithms for Estimating Effective Resistance
Effective resistance is an important metric that measures the similarity of two vertices in a graph.
Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem
In most settings the expected reoptimization time for such tailored algorithms is linear in the number of added edges.
Average Sensitivity of Spectral Clustering
To make reliable and efficient decisions based on spectral clustering, we assess the stability of spectral clustering against edge perturbations in the input graph using the notion of average sensitivity, which is the expected size of the symmetric difference of the output clusters before and after we randomly remove edges.
More Effective Randomized Search Heuristics for Graph Coloring Through Dynamic Optimization
We show that EAs can solve the graph coloring problem for bipartite graphs more efficiently by using dynamic optimization.
Mixed-Order Spectral Clustering for Networks
This paper proposes a new Mixed-Order Spectral Clustering (MOSC) approach to model both second-order and third-order structures simultaneously, with two MOSC methods developed based on Graph Laplacian (GL) and Random Walks (RW).
