14 citations · 38 across the 15 of their papers we have counts for
8 papers · 1 filter
Do graph neural network states contain graph properties?
Tom Pelletreau-Duris, Ruud van Bakel, Michael Cochez
Deep neural networks (DNNs) achieve state-of-the-art performance on many tasks, but this often requires increasingly larger model sizes, which in turn leads to more complex interna…
Scaling R-GCN Training with Graph Summarization
Alessandro Generale, Till Blume, Michael Cochez
Training of Relational Graph Convolutional Networks (R-GCN) is a memory intense task. The amount of gradient information that needs to be stored during training for real-world grap…
Updating Embeddings for Dynamic Knowledge Graphs
Christopher Wewer, Florian Lemmerich, Michael Cochez
Data in Knowledge Graphs often represents part of the current state of the real world. Thus, to stay up-to-date the graph data needs to be updated frequently. To utilize informatio…
Complex Query Answering with Neural Link Predictors
Erik Arakelyan, Daniel Daza, Pasquale Minervini +1
Neural link predictors are immensely useful for identifying missing edges in large scale Knowledge Graphs. However, it is still not clear how to use these models for answering more…
Privacy Attacks on Network Embeddings
Michael Ellers, Michael Cochez, Tobias Schumacher +2
Data ownership and data protection are increasingly important topics with ethical and legal implications, e.g., with the right to erasure established in the European General Data P…
Transferring knowledge from monitored to unmonitored areas for forecasting parking spaces
Andrei Ionita, André Pomp, Michael Cochez +2
Smart cities around the world have begun monitoring parking areas in order to estimate available parking spots and help drivers looking for parking. The current results are promisi…