@nfdi4culture@nfdi.social❓Wie können Musikwissenschaftler:innen mit einem Knowledge Graph interagieren und relevante Informationen abrufen, ohne SPARQL-Expert:innen zu sein?
🤩 Wir freuen uns diese Frage mit euch im Rahmen einer Data Challenge zu diskutieren!✨Und in Kooperation mit dem @…
✨ From Notes to Nodes – Develop an AI-driven explor…
@arXiv_csDS_bot@mastoxiv.pageIncremental (k, z)-Clustering on Graphs
Emilio Cruciani, Sebastian Forster, Antonis Skarlatos
https://arxiv.org/abs/2602.08542 https://arxiv.org/pdf/2602.08542 https://arxiv.org/html/2602.08542
arXiv:2602.08542v1 Announce Type: new
Abstract: Given a weighted undirected graph, a number of clusters $k$, and an exponent $z$, the goal in the $(k, z)$-clustering problem on graphs is to select $k$ vertices as centers that minimize the sum of the distances raised to the power $z$ of each vertex to its closest center. In the dynamic setting, the graph is subject to adversarial edge updates, and the goal is to maintain explicitly an exact $(k, z)$-clustering solution in the induced shortest-path metric.
While efficient dynamic $k$-center approximation algorithms on graphs exist [Cruciani et al. SODA 2024], to the best of our knowledge, no prior work provides similar results for the dynamic $(k,z)$-clustering problem. As the main result of this paper, we develop a randomized incremental $(k, z)$-clustering algorithm that maintains with high probability a constant-factor approximation in a graph undergoing edge insertions with a total update time of $\tilde O(k m^{1 o(1)} k^{1 \frac{1}{\lambda}} m)$, where $\lambda \geq 1$ is an arbitrary fixed constant. Our incremental algorithm consists of two stages. In the first stage, we maintain a constant-factor bicriteria approximate solution of size $\tilde{O}(k)$ with a total update time of $m^{1 o(1)}$ over all adversarial edge insertions. This first stage is an intricate adaptation of the bicriteria approximation algorithm by Mettu and Plaxton [Machine Learning 2004] to incremental graphs. One of our key technical results is that the radii in their algorithm can be assumed to be non-decreasing while the approximation ratio remains constant, a property that may be of independent interest.
In the second stage, we maintain a constant-factor approximate $(k,z)$-clustering solution on a dynamic weighted instance induced by the bicriteria approximate solution. For this subproblem, we employ a dynamic spanner algorithm together with a static $(k,z)$-clustering algorithm.
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@nfdi4culture@nfdi.socialWer mal richtig mit Musik 🎶 , KI 🤖 und dem Culture Knowledge Graph 🐙 hacken will, ist ganz herzlich zur Teilnahme an der Data Challenge von @… und @… eingeladen! Das Ziel lautet "Develo…
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Keisuke Toyama, Zhi Zhong, Akira Takahashi, Shusuke Takahashi, Yuki Mitsufuji
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toXiv_bot_toot
@nfdi4culture@nfdi.social❄️Liebe NFDI4Culture Community, zum Jahresausklang möchten wir allen herzlich danken, die zum Gelingen und zum kontinuierlichen Wachstum unserer Community und unserer Angebote beitragen – sei es als Daten- und Diensteanbieter im Konsortium, als Mitwirkende in unseren Gremien oder als engagierte Nutzer:innen.
🕯️🎇 Wir wünschen allen ein friedvolles Weihnachtsfest sowie ein glückliches und gesundes neues Jahr 2026!
Hier geht's zur Culture Knowledge Christmas Card:
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Xiao Zhang, Zengzhe Chen, Yuan Yuan, Yifei Zou, Fuzhen Zhuang, Wenyu Jiao, Yuke Wang, Dongxiao Yu
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Jaeyeon Kim, Seunggeun Kim, Taekyun Lee, David Z. Pan, Hyeji Kim, Sham Kakade, Sitan Chen
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https://arxiv.org/abs/2511.11571 https://mastoxiv.page/@arXiv_csLG_bot/115564541392410174
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https://arxiv.org/abs/2511.12817 https://mastoxiv.page/@arXiv_csLG_bot/115570877730326947
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