Search Results for author:
Found 28 papers, 15 papers with code
Griffin: Towards a Graph-Centric Relational Database Foundation Model
We introduce Griffin, the first foundation model attemptation designed specifically for Relational Databases (RDBs).
Influence Maximization in Temporal Social Networks with a Cold-Start Problem: A Supervised Approach
Influence Maximization (IM) in temporal graphs focuses on identifying influential "seeds" that are pivotal for maximizing network expansion.
LIFT: Improving Long Context Understanding of Large Language Models through Long Input Fine-Tuning
This paper presents Long Input Fine-Tuning (LIFT), a novel framework for long-context modeling that can improve the long-context performance of arbitrary (short-context) LLMs by dynamically adapting model parameters based on the long input.
Using Random Noise Equivariantly to Boost Graph Neural Networks Universally
Recent advances in Graph Neural Networks (GNNs) have explored the potential of random noise as an input feature to enhance expressivity across diverse tasks.
Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions?
Diffusion models have gained popularity in graph generation tasks; however, the extent of their expressivity concerning the graph distributions they can learn is not fully understood.
Exact Acceleration of Subgraph Graph Neural Networks by Eliminating Computation Redundancy
Many recent studies have proposed the use of graph convolution methods over the numerous subgraphs of each graph, a concept known as subgraph graph neural networks (subgraph GNNs), to enhance GNNs' ability to distinguish non-isomorphic graphs.
How Different AI Chatbots Behave? Benchmarking Large Language Models in Behavioral Economics Games
The deployment of large language models (LLMs) in diverse applications requires a thorough understanding of their decision-making strategies and behavioral patterns.
GL-Fusion: Rethinking the Combination of Graph Neural Network and Large Language model
Recent research on integrating Large Language Models (LLMs) with Graph Neural Networks (GNNs) typically follows two approaches: LLM-centered models, which convert graph data into tokens for LLM processing, and GNN-centered models, which use LLMs to encode text features into node and edge representations for GNN input.
Reconsidering the Performance of GAE in Link Prediction
Various graph neural networks (GNNs) with advanced training techniques and model designs have been proposed for link prediction tasks.
Ranked #1 on
Link Property Prediction
on ogbl-ppa
Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors
Graph neural networks (GNNs) have achieved remarkable success in a variety of machine learning tasks over graph data.
Geometric Representation Condition Improves Equivariant Molecule Generation
Compared to directly generating a molecule, the relatively easy-to-generate representation in the first-stage guides the second-stage generation to reach a high-quality molecule in a more goal-oriented and much faster way.
Efficient Neural Common Neighbor for Temporal Graph Link Prediction
However, such methods focus on learning individual node representations, but overlook the pairwise representation learning nature of link prediction and fail to capture the important pairwise features of links such as common neighbors (CN).
Graph as Point Set
In contrast, this paper introduces a novel graph-to-set conversion method that bijectively transforms interconnected nodes into a set of independent points and then uses a set encoder to learn the graph representation.
On the Completeness of Invariant Geometric Deep Learning Models
We then show that GeoNGNN, the geometric counterpart of one of the simplest subgraph graph neural networks (subgraph GNNs), can effectively break these corner cases' symmetry and thus achieve E(3)-completeness.
PyTorch Geometric High Order: A Unified Library for High Order Graph Neural Network
We introduce PyTorch Geometric High Order (PyGHO), a library for High Order Graph Neural Networks (HOGNNs) that extends PyTorch Geometric (PyG).
Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes
Second, on $1$-simplices or edge level, we bridge edge-level random walk and Hodge $1$-Laplacians and design corresponding edge PE respectively.
Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power
Many of the proposed GNN models with provable cycle counting power are based on subgraph GNNs, i. e., extracting a bag of subgraphs from the input graph, generating representations for each subgraph, and using them to augment the representation of the input graph.
P-vectors: A Parallel-Coupled TDNN/Transformer Network for Speaker Verification
Typically, the Time-Delay Neural Network (TDNN) and Transformer can serve as a backbone for Speaker Verification (SV).
From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks
Relational pooling is a framework for building more expressive and permutation-invariant graph neural networks.
Improving Graph Neural Networks on Multi-node Tasks with Labeling Tricks
When we want to learn a node-set representation involving multiple nodes, a common practice in previous works is to directly aggregate the single-node representations obtained by a GNN.
Is Distance Matrix Enough for Geometric Deep Learning?
In this work, we first construct families of novel and symmetric geometric graphs that Vanilla DisGNN cannot distinguish even when considering all-pair distances, which greatly expands the existing counterexample families.
Neural Common Neighbor with Completion for Link Prediction
In this work, we propose a novel link prediction model and further boost it by studying graph incompleteness.
Ranked #1 on
Link Property Prediction
on ogbl-ddi
Graph Neural Network with Local Frame for Molecular Potential Energy Surface
Projected onto a frame, equivariant features like 3D coordinates are converted to invariant features, so that we can capture geometric information with these projections and decouple the symmetry requirement from GNN design.
Two-Dimensional Weisfeiler-Lehman Graph Neural Networks for Link Prediction
As pointed out by previous works, this two-step procedure results in low discriminating power, as 1-WL-GNNs by nature learn node-level representations instead of link-level.
How Powerful are Spectral Graph Neural Networks
We also establish a connection between the expressive power of spectral GNNs and Graph Isomorphism (GI) testing, the latter of which is often used to characterize spatial GNNs' expressive power.
GLASS: GNN with Labeling Tricks for Subgraph Representation Learning
And training a GLASS model only takes 28% time needed for a SubGNN on average.
Decentralized Baseband Processing with Gaussian Message Passing Detection for Uplink Massive MU-MIMO Systems
Decentralized baseband processing (DBP) architecture, which partitions the base station antennas into multiple antenna clusters, has been recently proposed to alleviate the excessively high interconnect bandwidth, chip input/output data rates, and detection complexity for massive multi-user multiple-input multiple-output (MU-MIMO) systems.
