2 citations · 5 across the 12 of their papers we have counts for
11 papers · 1 filter
Graph Mixing Additive Networks
Maya Bechler-Speicher, Andrea Zerio, Maor Huri +5
We introduce GMAN, a flexible, interpretable, and expressive framework that extends Graph Neural Additive Networks (GNANs) to learn from sets of sparse time-series data. GMAN repre…
Spectral Graph Neural Networks are Incomplete on Graphs with a Simple Spectrum
Snir Hordan, Maya Bechler-Speicher, Gur Lifshitz +1
Spectral features are widely incorporated within Graph Neural Networks (GNNs) to improve their expressive power, or their ability to distinguish among non-isomorphic graphs. One po…
SuperMAN: Interpretable and Expressive Networks over Temporally Sparse Heterogeneous Data
Maya Bechler-Speicher, Andrea Zerio, Maor Huri +5
Real-world temporal data often consists of multiple signal types recorded at irregular, asynchronous intervals. For instance, in the medical domain, different types of blood tests…
Depth-Width tradeoffs in Algorithmic Reasoning of Graph Tasks with Transformers
Gilad Yehudai, Clayton Sanford, Maya Bechler-Speicher +3
Transformers have revolutionized the field of machine learning. In particular, they can be used to solve complex algorithmic problems, including graph-based tasks. In such algorith…
Towards Invariance to Node Identifiers in Graph Neural Networks
Maya Bechler-Speicher, Moshe Eliasof, Carola-Bibiane Schonlieb +2
Message-Passing Graph Neural Networks (GNNs) are known to have limited expressive power, due to their message passing structure. One mechanism for circumventing this limitation is…
On the Utilization of Unique Node Identifiers in Graph Neural Networks
Maya Bechler-Speicher, Moshe Eliasof, Carola-Bibiane Schönlieb +2
Graph Neural Networks have inherent representational limitations due to their message-passing structure. Recent work has suggested that these limitations can be overcome by using u…
A General Recipe for Contractive Graph Neural Networks -- Technical Report
Maya Bechler-Speicher, Moshe Eliasof
Graph Neural Networks (GNNs) have gained significant popularity for learning representations of graph-structured data due to their expressive power and scalability. However, despit…
Cayley Graph Propagation
JJ Wilson, Maya Bechler-Speicher, Petar Veličković
In spite of the plethora of success stories with graph neural networks (GNNs) on modelling graph-structured data, they are notoriously vulnerable to over-squashing, whereby tasks n…
The Interpretable and Effective Graph Neural Additive Networks
Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach
Graph Neural Networks (GNNs) have emerged as the predominant approach for learning over graph-structured data. However, most GNNs operate as black-box models and require post-hoc e…
Graph Neural Networks Use Graphs When They Shouldn't
Maya Bechler-Speicher, Ido Amos, Ran Gilad-Bachrach +1
Predictions over graphs play a crucial role in various domains, including social networks and medicine. Graph Neural Networks (GNNs) have emerged as the dominant approach for learn…
TREE-G: Decision Trees Contesting Graph Neural Networks
Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach
When dealing with tabular data, models based on decision trees are a popular choice due to their high accuracy on these data types, their ease of application, and explainability pr…