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Found 18 papers, 13 papers with code
Zero-shot Logical Query Reasoning on any Knowledge Graph
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.
Large Language Models can Learn Rules
In the deduction stage, the LLM is then prompted to employ the learned rule library to perform reasoning to answer test questions.
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.
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
GraphText: Graph Reasoning in Text Space
Furthermore, GraphText paves the way for interactive graph reasoning, allowing both humans and LLMs to communicate with the model seamlessly using natural language.
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.
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*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
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding
However, there is a lack of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field.
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
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery
However, lacking domain knowledge (e. g., which tasks to work on), standard benchmarks and data preprocessing pipelines are the main obstacles for machine learning researchers to work in this domain.
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction
To further improve the capacity of the path formulation, we propose the Neural Bellman-Ford Network (NBFNet), a general graph neural network framework that solves the path formulation with learned operators in the generalized Bellman-Ford algorithm.
Ranked #1 on
Link Prediction
on FB15k-237
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
Molecular graph generation is a fundamental problem for drug discovery and has been attracting growing attention.
Ranked #1 on
Molecular Graph Generation
on MOSES
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text.
Self-Adaptive Network Pruning
Our method introduces a general Saliency-and-Pruning Module (SPM) for each convolutional layer, which learns to predict saliency scores and applies pruning for each channel.
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
In this paper, we propose GraphVite, a high-performance CPU-GPU hybrid system for training node embeddings, by co-optimizing the algorithm and the system.
Ranked #1 on
Node Classification
on YouTube
Saliency Supervision: An Intuitive and Effective Approach for Pain Intensity Regression
However, due to the limitation in data sources and the subjectiveness in pain intensity values, it is hard to adopt modern deep neural networks for this problem without domain-specific auxiliary design.
Context Aware Document Embedding
Recently, doc2vec has achieved excellent results in different tasks.
