49 citations · 135 across the 24 of their papers we have counts for
13 papers · 1 filter
Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights
Zhikai Chen, Haitao Mao, Jingzhe Liu +8
Given the ubiquity of graph data and its applications in diverse domains, building a Graph Foundation Model (GFM) that can work well across different graphs and tasks with a unifie…
Understanding Transformer Reasoning Capabilities via Graph Algorithms
Clayton Sanford, Bahare Fatemi, Ethan Hall +5
Which transformer scaling regimes are able to perfectly solve different classes of algorithmic problems? While tremendous empirical advances have been attained by transformer-based…
Let Your Graph Do the Talking: Encoding Structured Data for LLMs
Bryan Perozzi, Bahare Fatemi, Dustin Zelle +4
How can we best encode structured data into sequential form for use in large language models (LLMs)? In this work, we introduce a parameter-efficient method to explicitly represent…
Talk like a Graph: Encoding Graphs for Large Language Models
Bahare Fatemi, Jonathan Halcrow, Bryan Perozzi
Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications such as social networks, recommender systems, and computational finance.…
UGSL: A Unified Framework for Benchmarking Graph Structure Learning
Bahare Fatemi, Sami Abu-El-Haija, Anton Tsitsulin +5
Graph neural networks (GNNs) demonstrate outstanding performance in a broad range of applications. While the majority of GNN applications assume that a graph structure is given, so…
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs
Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Kaidi Cao +4
Precise hardware performance models play a crucial role in code optimizations. They can assist compilers in making heuristic decisions or aid autotuners in identifying the optimal…
Learning to Substitute Ingredients in Recipes
Bahare Fatemi, Quentin Duval, Rohit Girdhar +2
Recipe personalization through ingredient substitution has the potential to help people meet their dietary needs and preferences, avoid potential allergens, and ease culinary explo…
Using Graph Algorithms to Pretrain Graph Completion Transformers
Jonathan Pilault, Michael Galkin, Bahare Fatemi +3
Recent work on Graph Neural Networks has demonstrated that self-supervised pretraining can further enhance performance on downstream graph, link, and node classification tasks. How…
Knowledge Hypergraph Embedding Meets Relational Algebra
Bahare Fatemi, Perouz Taslakian, David Vazquez +1
Embedding-based methods for reasoning in knowledge hypergraphs learn a representation for each entity and relation. Current methods do not capture the procedural rules underlying t…
SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks
Bahare Fatemi, Layla El Asri, Seyed Mehran Kazemi
Graph neural networks (GNNs) work well when the graph structure is provided. However, this structure may not always be available in real-world applications. One solution to this pr…
Knowledge Hypergraphs: Prediction Beyond Binary Relations
Bahare Fatemi, Perouz Taslakian, David Vazquez +1
Knowledge graphs store facts using relations between two entities. In this work, we address the question of link prediction in knowledge hypergraphs where relations are defined on…
Improved Knowledge Graph Embedding using Background Taxonomic Information
Bahare Fatemi, Siamak Ravanbakhsh, David Poole
Knowledge graphs are used to represent relational information in terms of triples. To enable learning about domains, embedding models, such as tensor factorization models, can be u…
Structure Learning for Relational Logistic Regression: An Ensemble Approach
Nandini Ramanan, Gautam Kunapuli, Tushar Khot +5
We consider the problem of learning Relational Logistic Regression (RLR). Unlike standard logistic regression, the features of RLRs are first-order formulae with associated weight…