Semi-Supervised Learning on Graphs Based on Local Label Distributions. - Evgheniy Faerman; Felix Borutta; Julian Busch; Matthias Schubert (CoRR, 2018)

Structural Graph Representations based on Multiscale Local Network Topologies. - Felix Borutta; Julian Busch; Evgeniy Faerman; Adina Klink; Matthias Schubert (WI, 2019)

PushNet: Efficient and Adaptive Neural Message Passing. - Julian Busch; Jiaxing Pi; Thomas Seidl 001 (ECAI, 2020)

Grace - Limiting the Number of Grid Cells for Clustering High-Dimensional Data. - Anna Beer 001; Daniyal Kazempour; Julian Busch; Alexander Tekles; Thomas Seidl 001 (LWDA, 2020)

Ada-LLD: Adaptive Node Similarity Using Multi-Scale Local Label Distributions. - Evgeniy Faerman; Felix Borutta; Julian Busch; Matthias Schubert (WI/IAT, 2020)

Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering. - Julian Busch; Evgeniy Faerman; Matthias Schubert; Thomas Seidl 001 (CoRR, 2020)

Implicit Hough Transform Neural Networks for Subspace Clustering. - Julian Busch; Maximilian Hünemörder; Janis Held; Peer Kröger; Thomas Seidl 001 (ICDM (Workshops), 2021)

NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification. - Julian Busch; Anton Kocheturov; Volker Tresp; Thomas Seidl 001 (SSDBM, 2021)

Representation learning on complex data. - Julian Busch (2021)

DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification. - Sandra Gilhuber; Julian Busch; Daniel Rotthues; Christian M. M. Frey; Thomas Seidl 001 (ECML/PKDD (1), 2023)

Geometry-aware PINNs for Turbulent Flow Prediction. - Shinjan Ghosh; Julian Busch; Georgia Olympia Brikis; Biswadip Dey (CoRR, 2024)