1 citations · 3 across the 19 of their papers we have counts for
8 papers · 1 filter
Parallel Algorithms for Higher-Order Temporal H-Index Computation
Lutz Oettershagen
Ranking vertices by importance is a fundamental task in network mining. The H-index provides such a ranking by asking how many neighbors are themselves well connected; its higher-o…
Fair Top-k Katz Centrality via Graph Design
Ivan Qin, Prudence Wong, Lutz Oettershagen
Centrality measures are widely used to rank nodes in networked data, but fairness interventions for graph centrality typically target global score mass or modify the centrality ope…
Unsupervised Multi-Scale Gromov-Wasserstein Hypergraph Alignment
Lutz Oettershagen, Honglian Wang, Aristides Gionis
We study unsupervised hypergraph alignment, where the goal is to infer node correspondences between two hypergraphs using only structural information, without node features, labels…
Linguistic Monoculture in LLM-Assisted Language Use
Suhas Thejaswi, Juhi Kulshreshta, Lutz Oettershagen
Writing and communication are increasingly mediated by large language models (LLMs) that are being used to draft, revise and polish text. Although such assistance can improve clari…
Query-Limited Community Recovery in Stochastic Block Models
Sabyasachi Basu, Manuj Mukherjee, Lutz Oettershagen +1
We study exact community recovery in the two-community stochastic block model on vertices under limited and noisy access to network data. The learner may query a noisy neighbor…
Efficient Banzhaf-Based Data Valuation for -Nearest Neighbors Classification
Guangyi Zhang, Lutz Oettershagen, Lixu Wang +1
Data valuation, the task of quantifying the contribution of individual data points to model performance, has emerged as a fundamental challenge in machine learning. Game-theoretic…
Top-k on a Budget: Adaptive Ranking with Weak and Strong Oracles
Lutz Oettershagen
Identifying the top- items is fundamental but often prohibitive when exact valuations are expensive. We study a two-oracle setting with a fast, noisy weak oracle and a scarce, h…
Streaming Stochastic Submodular Maximization with On-Demand User Requests
Honglian Wang, Sijing Tu, Lutz Oettershagen +1
We explore a novel problem in streaming submodular maximization, inspired by the dynamics of news-recommendation platforms. We consider a setting where users can visit a news websi…