Zobrazeno 1 - 10
of 219
pro vyhledávání: '"Ceylan, Ismail"'
Graph Transformers are popular neural networks that extend the well-known Transformer architecture to the graph domain. These architectures operate by applying self-attention on graph nodes and incorporating graph structure through the use of positio
Externí odkaz:
http://arxiv.org/abs/2410.18676
Traditional query answering over knowledge graphs -- or broadly over relational data -- is one of the most fundamental problems in data management. Motivated by the incompleteness of modern knowledge graphs, a new setup for query answering has emerge
Externí odkaz:
http://arxiv.org/abs/2409.13959
Message Passing Neural Networks (MPNNs) are a staple of graph machine learning. MPNNs iteratively update each node's representation in an input graph by aggregating messages from the node's neighbors, which necessitates a memory complexity of the ord
Externí odkaz:
http://arxiv.org/abs/2405.20724
Autor:
Davis, Oscar, Kessler, Samuel, Petrache, Mircea, Ceylan, İsmail İlkan, Bronstein, Michael, Bose, Avishek Joey
Generative modeling over discrete data has recently seen numerous success stories, with applications spanning language modeling, biological sequence design, and graph-structured molecular data. The predominant generative modeling paradigm for discret
Externí odkaz:
http://arxiv.org/abs/2405.14664
We present a new angle on the expressive power of graph neural networks (GNNs) by studying how the predictions of real-valued GNN classifiers, such as those classifying graphs probabilistically, evolve as we apply them on larger graphs drawn from som
Externí odkaz:
http://arxiv.org/abs/2403.03880
A large body of work has investigated the properties of graph neural networks and identified several limitations, particularly pertaining to their expressive power. Their inability to count certain patterns (e.g., cycles) in a graph lies at the heart
Externí odkaz:
http://arxiv.org/abs/2402.08595
Autor:
Huang, Xingyue, Orth, Miguel Romero, Barceló, Pablo, Bronstein, Michael M., Ceylan, İsmail İlkan
Link prediction with knowledge graphs has been thoroughly studied in graph machine learning, leading to a rich landscape of graph neural network architectures with successful applications. Nonetheless, it remains challenging to transfer the success o
Externí odkaz:
http://arxiv.org/abs/2402.04062
Autor:
Morris, Christopher, Frasca, Fabrizio, Dym, Nadav, Maron, Haggai, Ceylan, İsmail İlkan, Levie, Ron, Lim, Derek, Bronstein, Michael, Grohe, Martin, Jegelka, Stefanie
Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across a broad spectrum of disciplines, from life to social and engineering sciences. Despite their pra
Externí odkaz:
http://arxiv.org/abs/2402.02287
Graph neural networks are popular architectures for graph machine learning, based on iterative computation of node representations of an input graph through a series of invariant transformations. A large class of graph neural networks follow a standa
Externí odkaz:
http://arxiv.org/abs/2310.01267
Large-scale knowledge bases are at the heart of modern information systems. Their knowledge is inherently uncertain, and hence they are often materialized as probabilistic databases. However, probabilistic database management systems typically lack t
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A88780
https://tud.qucosa.de/api/qucosa%3A88780/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A88780/attachment/ATT-0/