23 citations · 72 across the 13 of their papers we have counts for
11 papers · 1 filter
Automatic Parameter Selection for Non-Redundant Clustering
Collin Leiber, Dominik Mautz, Claudia Plant +1
High-dimensional datasets often contain multiple meaningful clusterings in different subspaces. For example, objects can be clustered either by color, weight, or size, revealing di…
Spectral Clustering of Attributed Multi-relational Graphs
Ylli Sadikaj, Yllka Velaj, Sahar Behzadi +1
Graph clustering aims at discovering a natural grouping of the nodes such that similar nodes are assigned to a common cluster. Many different algorithms have been proposed in the l…
Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs
Zhaoliang Chen, Zhihao Wu, Luying Zhong +3
Heterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks.One challenge of heterogeneous graph learning is the…
AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing
Zhaoliang Chen, Zhihao Wu, Zhenghong Lin +3
Graph Convolutional Network (GCN) with the powerful capacity to explore graph-structural data has gained noticeable success in recent years. Nonetheless, most of the existing GCN-b…
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree
Christina Pacher, Irene Schicker, Rosmarie deWit +2
Both clustering and outlier detection play an important role for meteorological measurements. We present the AWT algorithm, a clustering algorithm for time series data that also pe…
Multi-view Graph Convolutional Networks with Differentiable Node Selection
Zhaoliang Chen, Lele Fu, Shunxin Xiao +3
Multi-view data containing complementary and consensus information can facilitate representation learning by exploiting the intact integration of multi-view features. Because most…
Deep Clustering With Consensus Representations
Lukas Miklautz, Martin Teuffenbach, Pascal Weber +4
The field of deep clustering combines deep learning and clustering to learn representations that improve both the learned representation and the performance of the considered clust…
Causal Discovery in Hawkes Processes by Minimum Description Length
Amirkasra Jalaldoust, Katerina Hlavackova-Schindler, Claudia Plant
Hawkes processes are a special class of temporal point processes which exhibit a natural notion of causality, as occurrence of events in the past may increase the probability of ev…
Network Embedding via Deep Prediction Model
Xin Sun, Zenghui Song, Yongbo Yu +3
Network-structured data becomes ubiquitous in daily life and is growing at a rapid pace. It presents great challenges to feature engineering due to the high non-linearity and spars…
Massively Parallel Graph Drawing and Representation Learning
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. Today's…
Incorporating User's Preference into Attributed Graph Clustering
Wei Ye, Dominik Mautz, Christian Boehm +2
Graph clustering has been studied extensively on both plain graphs and attributed graphs. However, all these methods need to partition the whole graph to find cluster structures. S…