Suhas Thejaswi
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View article: Capacitated Fair-Range Clustering: Hardness and Approximation Algorithms
Capacitated Fair-Range Clustering: Hardness and Approximation Algorithms Open
Capacitated fair-range $k$-clustering generalizes classical $k$-clustering by incorporating both capacity constraints and demographic fairness. In this setting, each facility has a capacity limit and may belong to one or more demographic g…
View article: Fair Clustering for Data Summarization: Improved Approximation Algorithms and Complexity Insights
Fair Clustering for Data Summarization: Improved Approximation Algorithms and Complexity Insights Open
Data summarization tasks are often modeled as $k$-clustering problems, where the goal is to choose $k$ data points, called cluster centers, that best represent the dataset by minimizing a clustering objective. A popular objective is to min…
View article: Diversity-aware clustering: Computational Complexity and Approximation Algorithms
Diversity-aware clustering: Computational Complexity and Approximation Algorithms Open
In this work, we study diversity-aware clustering problems where the data points are associated with multiple attributes resulting in intersecting groups. A clustering solution needs to ensure that the number of chosen cluster centers from…
View article: Fair Column Subset Selection
Fair Column Subset Selection Open
The problem of column subset selection asks for a subset of columns from an input matrix such that the matrix can be reconstructed as accurately as possible within the span of the selected columns. A natural extension is to consider a sett…
View article: Clustering with Fair-Center Representation
Clustering with Fair-Center Representation Open
We study a variant of classical clustering formulations in the context of algorithmic fairness, known as diversity-aware clustering. In this variant we are given a collection of facility subsets, and a solution must contain at least a spec…
View article: Clustering with fair-center representation: parameterized approximation algorithms and heuristics
Clustering with fair-center representation: parameterized approximation algorithms and heuristics Open
We study a variant of classical clustering formulations in the context of algorithmic fairness, known as diversity-aware clustering. In this variant we are given a collection of facility subsets, and a solution must contain at least a spec…
View article: Restless reachability problems in temporal graphs
Restless reachability problems in temporal graphs Open
We study a family of reachability problems under waiting-time restrictions in temporal and vertex-colored temporal graphs. Given a temporal graph and a set of source vertices, we find the set of vertices that are reachable from a source vi…
View article: Finding Path Motifs in Large Temporal Graphs Using Algebraic Fingerprints
Finding Path Motifs in Large Temporal Graphs Using Algebraic Fingerprints Open
We study a family of pattern-detection problems in vertex-colored temporal graphs. In particular, given a vertex-colored temporal graph and a multiset of colors as a query, we search for temporal paths in the graph that contain the colors …
View article: Finding path motifs in large temporal graphs using algebraic\n fingerprints
Finding path motifs in large temporal graphs using algebraic\n fingerprints Open
We study a family of pattern-detection problems in vertex-colored temporal\ngraphs. In particular, given a vertex-colored temporal graph and a multiset of\ncolors as a query, we search for temporal paths in the graph that contain the\ncolo…
View article: Pattern detection in large temporal graphs using algebraic fingerprints
Pattern detection in large temporal graphs using algebraic fingerprints Open
| openaire: EC/H2020/871042/EU//SoBigData-PlusPlus
View article: Engineering Motif Search for Large Motifs
Engineering Motif Search for Large Motifs Open
Given a vertex-colored graph H and a multiset M of colors as input, the graph motif problem asks us to decide whether H has a connected induced subgraph whose multiset of colors agrees with M. The graph motif problem is NP-complete but kno…