Search Results for author:
Found 30 papers, 27 papers with code
SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
Generating novel crystalline materials has potential to lead to advancements in fields such as electronics, energy storage, and catalysis.
Deconstructing equivariant representations in molecular systems
In this work, we report on a set of experiments using a simple equivariant graph convolution model on the QM9 dataset, focusing on correlating quantitative performance with the resulting molecular graph embeddings.
One Model, Any Conjunctive Query: Graph Neural Networks for Answering Complex Queries over Knowledge Graphs
Traditional query answering over knowledge graphs -- or broadly over relational data -- is one of the most fundamental problems in data management.
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
To address these challenges, we introduce Temporal Graph Benchmark 2. 0 (TGB 2. 0), a novel benchmarking framework tailored for evaluating methods for predicting future links on Temporal Knowledge Graphs and Temporal Heterogeneous Graphs with a focus on large-scale datasets, extending the Temporal Graph Benchmark.
Fully-inductive Node Classification on Arbitrary Graphs
Many existing methods following the inductive setup can generalize to test graphs with new structures, but assuming the feature and label spaces remain the same as the training ones.
FloorSet -- a VLSI Floorplanning Dataset with Design Constraints of Real-World SoCs
As novel machine learning (ML) approaches emerge to tackle such problems, there is a growing need for a modern benchmark that comprises a large training dataset and performance metrics that better reflect real-world constraints and objectives compared to existing benchmarks.
A Foundation Model for Zero-shot Logical Query Reasoning
Complex logical query answering (CLQA) in knowledge graphs (KGs) goes beyond simple KG completion and aims at answering compositional queries comprised of multiple projections and logical operations.
Position: Graph Foundation Models are Already Here
Graph Foundation Models (GFMs) are emerging as a significant research topic in the graph domain, aiming to develop graph models trained on extensive and diverse data to enhance their applicability across various tasks and domains.
Towards Foundation Models for Knowledge Graph Reasoning
The key challenge of designing foundation models on KGs is to learn such transferable representations that enable inference on any graph with arbitrary entity and relation vocabularies.
Ranked #1 on
Link Prediction
on CoDEx Medium
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods focused on solid-state materials with periodic crystal structures.
Approximate Answering of Graph Queries
We will first provide an overview of the different query types which can be supported by these methods and datasets typically used for evaluation, as well as an insight into their limitations.
Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases
Extending the idea of graph databases (graph DBs), NGDB consists of a Neural Graph Storage and a Neural Graph Engine.
Attending to Graph Transformers
Recently, transformer architectures for graphs emerged as an alternative to established techniques for machine learning with graphs, such as (message-passing) graph neural networks.
Weisfeiler and Leman Go Relational
Namely, we investigate the limitations in the expressive power of the well-known Relational GCN and Compositional GCN architectures and shed some light on their practical learning performance.
Inductive Logical Query Answering in Knowledge Graphs
Exploring the efficiency--effectiveness trade-off, we find the inductive relational structure representation method generally achieves higher performance, while the inductive node representation method is able to answer complex queries in the inference-only regime without any training on queries and scales to graphs of millions of nodes.
A Decade of Knowledge Graphs in Natural Language Processing: A Survey
In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry.
Long Range Graph Benchmark
Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer.
Ranked #3 on
Link Prediction
on PCQM-Contact
A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
Experiments on both transductive and inductive knowledge graph reasoning benchmarks show that A*Net achieves competitive performance with existing state-of-the-art path-based methods, while merely visiting 10% nodes and 10% edges at each iteration.
Ranked #10 on
Link Property Prediction
on ogbl-wikikg2
Recipe for a General, Powerful, Scalable Graph Transformer
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks.
Ranked #1 on
Graph Property Prediction
on ogbg-ppa
Neural-Symbolic Models for Logical Queries on Knowledge Graphs
Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning.
Ranked #3 on
Complex Query Answering
on FB15k-237
A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs
The link prediction task on knowledge graphs without explicit negative triples in the training data motivates the usage of rank-based metrics.
An Open Challenge for Inductive Link Prediction on Knowledge Graphs
An emerging trend in representation learning over knowledge graphs (KGs) moves beyond transductive link prediction tasks over a fixed set of known entities in favor of inductive tasks that imply training on one graph and performing inference over a new graph with unseen entities.
Ranked #1 on
Inductive Link Prediction
on ILPC22-Small
Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application
We show that the segmentation model reduces the ratio of detection disagreements from 0. 162 to 0. 085 (p < 0. 05).
Improving Inductive Link Prediction Using Hyper-Relational Facts
In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks.
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs
To this end, we propose NodePiece, an anchor-based approach to learn a fixed-size entity vocabulary.
Ranked #18 on
Link Property Prediction
on ogbl-wikikg2
Query Embedding on Hyper-relational Knowledge Graphs
Besides that, we propose a method to answer such queries and demonstrate in our experiments that qualifiers improve query answering on a diverse set of query patterns.
Message Passing for Hyper-Relational Knowledge Graphs
We also demonstrate that existing benchmarks for evaluating link prediction (LP) performance on hyper-relational KGs suffer from fundamental flaws and thus develop a new Wikidata-based dataset - WD50K.
Ranked #2 on
Link Prediction
on JF17K
El Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC Tasks
Pre-training large-scale language models (LMs) requires huge amounts of text corpora.
Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult.
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation
Stereotactic radiosurgery is a minimally-invasive treatment option for a large number of patients with intracranial tumors.
