Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Veldt, Nate"'
Autor:
Bengali, Vedangi, Veldt, Nate
A minimum $s$-$t$ cut in a hypergraph is a bipartition of vertices that separates two nodes $s$ and $t$ while minimizing a hypergraph cut function. The cardinality-based hypergraph cut function assigns a cut penalty to each hyperedge based on the num
Externí odkaz:
http://arxiv.org/abs/2409.16195
Cluster deletion is an NP-hard graph clustering objective with applications in computational biology and social network analysis, where the goal is to delete a minimum number of edges to partition a graph into cliques. We first provide a tighter anal
Externí odkaz:
http://arxiv.org/abs/2404.16131
Dense subgraph discovery is a fundamental primitive in graph and hypergraph analysis which among other applications has been used for real-time story detection on social media and improving access to data stores of social networking systems. We prese
Externí odkaz:
http://arxiv.org/abs/2310.13792
Autor:
Bengali, Vedangi, Veldt, Nate
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. We present faster approximation algorithms for an NP-hard para
Externí odkaz:
http://arxiv.org/abs/2306.04884
A recent trend in data mining has explored (hyper)graph clustering algorithms for data with categorical relationship types. Such algorithms have applications in the analysis of social, co-authorship, and protein interaction networks, to name a few. M
Externí odkaz:
http://arxiv.org/abs/2305.17598
Learning a smooth graph signal from partially observed data is a well-studied task in graph-based machine learning. We consider this task from the perspective of optimal recovery, a mathematical framework for learning a function from observational da
Externí odkaz:
http://arxiv.org/abs/2304.00474
Autor:
Aksoy, Sinan G., Bennink, Ryan, Chen, Yuzhou, Frías, José, Gel, Yulia R., Kay, Bill, Naumann, Uwe, Marrero, Carlos Ortiz, Petyuk, Anthony V., Roy, Sandip, Segovia-Dominguez, Ignacio, Veldt, Nate, Young, Stephen J.
We present and discuss seven different open problems in applied combinatorics. The application areas relevant to this compilation include quantum computing, algorithmic differentiation, topological data analysis, iterative methods, hypergraph cut alg
Externí odkaz:
http://arxiv.org/abs/2303.11464
Autor:
Veldt, Nate
Hypergraph clustering is a basic algorithmic primitive for analyzing complex datasets and systems characterized by multiway interactions, such as group email conversations, groups of co-purchased retail products, and co-authorship data. This paper pr
Externí odkaz:
http://arxiv.org/abs/2301.12274
Autor:
Veldt, Nate
This paper presents a fast and simple new 2-approximation algorithm for minimum weighted vertex cover. The unweighted version of this algorithm is equivalent to a well-known greedy maximal independent set algorithm. We prove that this independent set
Externí odkaz:
http://arxiv.org/abs/2209.04673
Optimal LP Rounding and Linear-Time Approximation Algorithms for Clustering Edge-Colored Hypergraphs
Autor:
Veldt, Nate
We study the approximability of an existing framework for clustering edge-colored hypergraphs, which is closely related to chromatic correlation clustering and is motivated by machine learning and data mining applications where the goal is to cluster
Externí odkaz:
http://arxiv.org/abs/2208.06506