Search Results for author:
Found 51 papers, 31 papers with code
Inductive Representation Learning on Large Graphs
Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions.
Ranked #1 on
Link Property Prediction
on ogbl-ddi
Representation Learning on Graphs: Methods and Applications
Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks.
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences.
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
We develop a data-efficient Graph Convolutional Network (GCN) algorithm PinSage, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i. e., items) that incorporate both graph structure as well as node feature information.
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research.
Hierarchical Graph Representation Learning with Differentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.
Ranked #1 on
Graph Classification
on REDDIT-MULTI-12K
GNNExplainer: Generating Explanations for Graph Neural Networks
We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures.
BIG-bench Machine Learning
Explainable artificial intelligence
+2
Redundancy-Free Computation Graphs for Graph Neural Networks
Graph Neural Networks (GNNs) are based on repeated aggregations of information across nodes' neighbors in a graph.
Position-aware Graph Neural Networks
Learning node embeddings that capture a node's position within the broader graph structure is crucial for many prediction tasks on graphs.
Neural Execution of Graph Algorithms
Graph Neural Networks (GNNs) are a powerful representational tool for solving problems on graph-structured inputs.
Improving Graph Attention Networks with Large Margin-based Constraints
Graph Attention Networks (GATs) are the state-of-the-art neural architecture for representation learning with graphs.
Hyperbolic Graph Convolutional Neural Networks
Here we propose Hyperbolic Graph Convolutional Neural Network (HGCN), the first inductive hyperbolic GCN that leverages both the expressiveness of GCNs and hyperbolic geometry to learn inductive node representations for hierarchical and scale-free graphs.
Ranked #1 on
Link Prediction
on PPI
(Accuracy metric)
Learning to Simulate Complex Physics with Graph Networks
Here we present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and deformable materials interacting with one another.
Multi-hop Attention Graph Neural Network
Currently, at every layer, attention is computed between connected pairs of nodes and depends solely on the representation of the two nodes.
Design Space for Graph Neural Networks
However, current research focuses on proposing and evaluating specific architectural designs of GNNs, as opposed to studying the more general design space of GNNs that consists of a Cartesian product of different design dimensions, such as the number of layers or the type of the aggregation function.
Identity-aware Graph Neural Networks
However, the expressive power of existing GNNs is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test, which means GNNs that are not able to predict node clustering coefficients and shortest path distances, and cannot differentiate between different d-regular graphs.
Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification
Recent work on aspect-level sentiment classification has demonstrated the efficacy of incorporating syntactic structures such as dependency trees with graph neural networks(GNN), but these approaches are usually vulnerable to parsing errors.
Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks
In this paper, we introuduce graph feature to feature (Fea2Fea) prediction pipelines in a low dimensional space to explore some preliminary results on structural feature correlation, which is based on graph neural network.
Ranked #1 on
Graph Classification
on Pubmed
Local Augmentation for Graph Neural Networks
To address this, we propose a simple and efficient data augmentation strategy, local augmentation, to learn the distribution of the node features of the neighbors conditioned on the central node's feature and enhance GNN's expressive power with generated features.
Neural Distance Embeddings for Biological Sequences
The development of data-dependent heuristics and representations for biological sequences that reflect their evolutionary distance is critical for large-scale biological research.
Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
Here we present ConE (Cone Embedding), a KG embedding model that is able to simultaneously model multiple hierarchical as well as non-hierarchical relations in a knowledge graph.
Ranked #1 on
Ancestor-descendant prediction
on WN18RR
Ancestor-descendant prediction
Knowledge Graph Completion
+2
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020
Researchers naturally adopt Automated Machine Learning on Graph Learning, aiming to reduce the human effort and achieve generally top-performing GNNs, but their methods focus more on the architecture search.
Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator
To model complex reservoir dynamics at both local and global scale, HGNS consists of a subsurface graph neural network (SGNN) to model the evolution of fluid flows, and a 3D-U-Net to model the evolution of pressure.
GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks
As GNN models are deployed to more mission-critical applications, we are in dire need for a common evaluation protocol of explainability methods of GNNs.
How Powerful is Implicit Denoising in Graph Neural Networks
However, the analysis of implicit denoising effect in graph neural networks remains open.
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
Current strategies use a decoupled approach of single-step retrosynthesis models and search algorithms, taking only the product as the input to predict the reactants for each planning step and ignoring valuable context information along the synthetic route.
FIMP: Foundation Model-Informed Message Passing for Graph Neural Networks
Foundation models have revolutionized the landscape of Deep Learning (DL), serving as a versatile platform which can be adapted to a wide range of downstream tasks.
Efficient Automatic Machine Learning via Design Graphs
FALCON features 1) a task-agnostic module, which performs message passing on the design graph via a Graph Neural Network (GNN), and 2) a task-specific module, which conducts label propagation of the known model performance information on the design graph.
Diffuser: Efficient Transformers with Multi-hop Attention Diffusion for Long Sequences
Efficient Transformers have been developed for long sequence modeling, due to their subquadratic memory and time complexity.
Learning Graph Search Heuristics
At training time, we aggregate datasets of search trajectories and ground-truth shortest path distances, which we use to train a specialized graph neural network-based heuristic function using backpropagation through steps of the pathfinding process.
Adversarially Robust Neural Architecture Search for Graph Neural Networks
To tackle these challenges, we propose a novel Robust Neural Architecture search framework for GNNs (G-RNA).
MUDiff: Unified Diffusion for Complete Molecule Generation
Our model is a promising approach for designing stable and diverse molecules and can be applied to a wide range of tasks in molecular modeling.
HiPool: Modeling Long Documents Using Graph Neural Networks
Encoding long sequences in Natural Language Processing (NLP) is a challenging problem.
BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs
To make each mini-batch have fewer false negatives, we design the proximity graph of randomly-selected instances.
Hyperbolic Representation Learning: Revisiting and Advancing
To address this, we propose a simple yet effective method, hyperbolic informed embedding (HIE), by incorporating cost-free hierarchical information deduced from the hyperbolic distance of the node to origin (i. e., induced hyperbolic norm) to advance existing \hlms.
DeSCo: Towards Generalizable and Scalable Deep Subgraph Counting
We introduce DeSCo, a scalable neural deep subgraph counting pipeline, designed to accurately predict both the count and occurrence position of queries on target graphs post single training.
GInX-Eval: Towards In-Distribution Evaluation of Graph Neural Network Explanations
Diverse explainability methods of graph neural networks (GNN) have recently been developed to highlight the edges and nodes in the graph that contribute the most to the model predictions.
MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data
Discovering genes with similar functions across diverse biomedical contexts poses a significant challenge in gene representation learning due to data heterogeneity.
Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Models
These analogous problems are related to the input one, with reusable solutions and problem-solving strategies.
BLIS-Net: Classifying and Analyzing Signals on Graphs
We make several crucial changes to the original geometric scattering architecture which we prove increase the ability of our network to capture information about the input signal and show that BLIS-Net achieves superior performance on both synthetic and real-world data sets based on traffic flow and fMRI data.
TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery
Temporal graphs are widely used to model dynamic systems with time-varying interactions.
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion
The objective of GNN explainability is to discern the underlying graph structures that have the most significant impact on model predictions.
Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation
However, recursively aggregating neighboring information with graph convolutions leads to indistinguishable node features in deep layers, which is known as the over-smoothing issue.
Generative Explanations for Graph Neural Network: Methods and Evaluations
Graph Neural Networks (GNNs) achieve state-of-the-art performance in various graph-related tasks.
Learning High-Order Relationships of Brain Regions
In response to this gap, we propose a novel method named HYBRID which aims to extract MIMR high-order relationships from fMRI data.
Relational Deep Learning: Graph Representation Learning on Relational Databases
The core idea is to view relational databases as a temporal, heterogeneous graph, with a node for each row in each table, and edges specified by primary-foreign key links.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
Representation Learning for Frequent Subgraph Mining
SPMiner combines graph neural networks, order embedding space, and an efficient search strategy to identify network subgraph patterns that appear most frequently in the target graph.
An Item is Worth a Prompt: Versatile Image Editing with Disentangled Control
Building on the success of text-to-image diffusion models (DPMs), image editing is an important application to enable human interaction with AI-generated content.
Efficient High-Resolution Time Series Classification via Attention Kronecker Decomposition
The high-resolution time series classification problem is essential due to the increasing availability of detailed temporal data in various domains.
From Similarity to Superiority: Channel Clustering for Time Series Forecasting
Motivated by our observation of a correlation between the time series model's performance boost against channel mixing and the intrinsic similarity on a pair of channels, we developed a novel and adaptable Channel Clustering Module (CCM).
