no code implementations • 14 Mar 2024 • Zhaoliang Chen, Zhihao Wu, Ylli Sadikaj, Claudia Plant, Hong-Ning Dai, Shiping Wang, Wenzhong Guo
Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method.
1 code implementation • 5 Feb 2024 • Andreas Stephan, Lukas Miklautz, Kevin Sidak, Jan Philip Wahle, Bela Gipp, Claudia Plant, Benjamin Roth
We, therefore, propose Text-Guided Image Clustering, i. e., generating text using image captioning and visual question-answering (VQA) models and subsequently clustering the generated text.
no code implementations • 19 Dec 2023 • Collin Leiber, Dominik Mautz, Claudia Plant, Christian Böhm
In this paper, we propose a framework that utilizes the Minimum Description Length Principle (MDL) to detect the number of subspaces and clusters per subspace automatically.
no code implementations • 19 Dec 2023 • Lena G. M. Bauer, Collin Leiber, Christian Böhm, Claudia Plant
This accelerates computation and provides an approximation of the Dip- to Dip-p-value transformation for every single sample size.
1 code implementation • ICDM 2023 • Peiyan Li, Liming Pan, Kai Li, Claudia Plant, Christian Böhm
In this study, we present SSF, an innovative hyperlink prediction methodology based on Subgraph Structural Features.
1 code implementation • 3 Nov 2023 • Ylli Sadikaj, Yllka Velaj, Sahar Behzadi, Claudia Plant
Many different algorithms have been proposed in the literature: for simple graphs, for graphs with attributes associated to nodes, and for graphs where edges represent different types of relations among nodes.
no code implementations • 14 Apr 2023 • Zhaoliang Chen, Zhihao Wu, Zhenghong Lin, Shiping Wang, Claudia Plant, Wenzhong Guo
In light of this, we propose an Alternating Graph-regularized Neural Network (AGNN) composed of Graph Convolutional Layer (GCL) and Graph Embedding Layer (GEL).
no code implementations • 13 Apr 2023 • Zhaoliang Chen, Zhihao Wu, Luying Zhong, Claudia Plant, Shiping Wang, Wenzhong Guo
Heterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks. One challenge of heterogeneous graph learning is the design of learnable meta-paths, which significantly influences the quality of learned embeddings. Thus, in this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically studies meta-paths containing multi-hop neighbors from an adaptive aggregation of multi-order adjacency matrices.
no code implementations • 13 Dec 2022 • Christina Pacher, Irene Schicker, Rosmarie deWit, Katerina Hlavackova-Schindler, Claudia Plant
Both clustering and outlier detection play an important role for meteorological measurements.
no code implementations • 9 Dec 2022 • Zhaoliang Chen, Lele Fu, Shunxin Xiao, Shiping Wang, Claudia Plant, Wenzhong Guo
Due to the powerful capability to gather information of neighborhood nodes, in this paper, we apply Graph Convolutional Network (GCN) to cope with heterogeneous-graph data originating from multi-view data, which is still under-explored in the field of GCN.
no code implementations • 16 Nov 2022 • Zhaoliang Chen, Lele Fu, Jie Yao, Wenzhong Guo, Claudia Plant, Shiping Wang
In practical applications, multi-view data depicting objectives from assorted perspectives can facilitate the accuracy increase of learning algorithms.
no code implementations • 13 Oct 2022 • Lukas Miklautz, Martin Teuffenbach, Pascal Weber, Rona Perjuci, Walid Durani, Christian Böhm, Claudia Plant
Further, we propose DECCS (Deep Embedded Clustering with Consensus representationS), a deep consensus clustering method that learns a consensus representation by enhancing the embedded space to such a degree that all ensemble members agree on a common clustering result.
no code implementations • 14 Jun 2022 • Michal Balazia, Katerina Hlavackova-Schindler, Petr Sojka, Claudia Plant
We apply the graphical Granger model (GGM) to obtain the so-called Granger causal graph among joints as a discriminative and visually interpretable representation of a person's gait.
no code implementations • 10 Jun 2022 • Amirkasra Jalaldoust, Katerina Hlavackova-Schindler, Claudia Plant
The synthetic experiments demonstrate superiority of our method incausal graph discovery compared to the baseline methods with respect to the size of the data.
no code implementations • 9 Dec 2021 • Robert Fritze, Claudia Plant
We have created a wrapper library that allows to use OpenCL on Android devices.
no code implementations • 27 Apr 2021 • Xin Sun, Zenghui Song, Yongbo Yu, Junyu Dong, Claudia Plant, Christian Boehm
This paper proposes a network embedding framework to capture the transfer behaviors on structured networks via deep prediction models.
1 code implementation • 6 Nov 2020 • Christian Böhm, Claudia Plant
To fully exploit the performance potential of modern multi-core processors, machine learning and data mining algorithms for big data must be parallelized in multiple ways.
1 code implementation • 24 Mar 2020 • Wei Ye, Dominik Mautz, Christian Boehm, Ambuj Singh, Claudia Plant
In contrast to global clustering, local clustering aims to find only one cluster that is concentrating on the given seed vertex (and also on the designated attributes for attributed graphs).