27 citations · 44 across the 8 of their papers we have counts for
9 papers
PlanE: Representation Learning over Planar Graphs
Radoslav Dimitrov, Zeyang Zhao, Ralph Abboud +1
Graph neural networks are prominent models for representation learning over graphs, where the idea is to iteratively compute representations of nodes of an input graph through a se…
Shortest Path Networks for Graph Property Prediction
Ralph Abboud, Radoslav Dimitrov, İsmail İlkan Ceylan
Most graph neural network models rely on a particular message passing paradigm, where the idea is to iteratively propagate node representations of a graph to each node in the direc…
Temporal Knowledge Graph Completion using Box Embeddings
Johannes Messner, Ralph Abboud, İsmail İlkan Ceylan
Knowledge graph completion is the task of inferring missing facts based on existing data in a knowledge graph. Temporal knowledge graph completion (TKGC) is an extension of this ta…
Node Classification Meets Link Prediction on Knowledge Graphs
Ralph Abboud, İsmail İlkan Ceylan
Node classification and link prediction are widely studied in graph representation learning. While both transductive node classification and link prediction operate over a single i…
The Surprising Power of Graph Neural Networks with Random Node Initialization
Ralph Abboud, İsmail İlkan Ceylan, Martin Grohe +1
Graph neural networks (GNNs) are effective models for representation learning on relational data. However, standard GNNs are limited in their expressive power, as they cannot disti…
BoxE: A Box Embedding Model for Knowledge Base Completion
Ralph Abboud, İsmail İlkan Ceylan, Thomas Lukasiewicz +1
Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB). A promising approach for KBC is to emb…