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Found 77 papers, 51 papers with code
Towards A Universal Graph Structural Encoder
To address these challenges, we propose GFSE, a universal graph structural encoder designed to capture transferable structural patterns across diverse domains such as molecular graphs, social networks, and citation networks.
Beyond Feature Importance: Feature Interactions in Predicting Post-Stroke Rigidity with Graph Explainable AI
This study addresses the challenge of predicting post-stroke rigidity by emphasizing feature interactions through graph-based explainable AI.
Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA
ExpRAG performs retrieval through a coarse-to-fine process, utilizing an EHR-based report ranker to efficiently identify similar patients, followed by an experience retriever to extract task-relevant content for enhanced medical reasoning.
MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering
To bridge this gap, we introduce Multimodal Time Series Benchmark (MTBench), a large-scale benchmark designed to evaluate large language models (LLMs) on time series and text understanding across financial and weather domains.
Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences
This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions and user experiences, especially in the context of 6G systems.
MindLLM: A Subject-Agnostic and Versatile Model for fMRI-to-Text Decoding
Decoding functional magnetic resonance imaging (fMRI) signals into text has been a key challenge in the neuroscience community, with the potential to advance brain-computer interfaces and uncover deeper insights into brain mechanisms.
Flow Matching for Collaborative Filtering
Generative models have shown great promise in collaborative filtering by capturing the underlying distribution of user interests and preferences.
HiPoNet: A Topology-Preserving Multi-View Neural Network For High Dimensional Point Cloud and Single-Cell Data
We also show an application of HiPoNet on spatial transcriptomics datasets using spatial co-ordinates as one of the views.
Low-Rank Adaptation for Foundation Models: A Comprehensive Review
The rapid advancement of foundation modelslarge-scale neural networks trained on diverse, extensive datasetshas revolutionized artificial intelligence, enabling unprecedented advancements across domains such as natural language processing, computer vision, and scientific discovery.
Lorentzian Residual Neural Networks
Hyperbolic neural networks have emerged as a powerful tool for modeling hierarchical data structures prevalent in real-world datasets.
Breaking Information Cocoons: A Hyperbolic Graph-LLM Framework for Exploration and Exploitation in Recommender Systems
To address these challenges, we propose HERec, a hyperbolic graph-LLM framework that effectively balances exploration and exploitation in recommender systems.
SANDWICH: Towards an Offline, Differentiable, Fully-Trainable Wireless Neural Ray-Tracing Surrogate
Wireless ray-tracing (RT) is emerging as a key tool for three-dimensional (3D) wireless channel modeling, driven by advances in graphical rendering.
Reaction-conditioned De Novo Enzyme Design with GENzyme
Here, we introduce \textsc{GENzyme}, a \textit{de novo} enzyme design model that takes a catalytic reaction as input and generates the catalytic pocket, full enzyme structure, and enzyme-substrate binding complex.
Long Sequence Modeling with Attention Tensorization: From Sequence to Tensor Learning
The resulting Tensorized Attention can be adopted as efficient transformer backbones to extend input context length with improved memory and time efficiency.
P-FOLIO: Evaluating and Improving Logical Reasoning with Abundant Human-Written Reasoning Chains
We show that human-written reasoning chains significantly boost the logical reasoning capabilities of LLMs via many-shot prompting and fine-tuning.
Hyperbolic Fine-tuning for Large Language Models
Through extensive experiments, we demonstrate that HypLoRA significantly enhances the performance of LLMs on reasoning tasks, particularly for complex reasoning problems.
Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications
Leveraging these findings, we develop and open-source Tele-LLMs, the first series of language models ranging from 1B to 8B parameters, specifically tailored for telecommunications.
Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability
When new knowledge emerges, AnoT maps it onto a node in the rule graph and traverses the rule graph recursively to derive the anomaly score of the knowledge.
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
In this survey, we provide a comprehensive review of the latest progress on heterophilic graph learning, including an extensive summary of benchmark datasets and evaluation of homophily metrics on synthetic graphs, meticulous classification of the most updated supervised and unsupervised learning methods, thorough digestion of the theoretical analysis on homophily/heterophily, and broad exploration of the heterophily-related applications.
Hypformer: Exploring Efficient Hyperbolic Transformer Fully in Hyperbolic Space
Our experimental results confirm the effectiveness and efficiency of Hypformer across various datasets, demonstrating its potential as an effective and scalable solution for large-scale data representation and large models.
Towards Understanding Sensitive and Decisive Patterns in Explainable AI: A Case Study of Model Interpretation in Geometric Deep Learning
Specifically, this study compares the effectiveness of two main streams of interpretation methods: post-hoc methods and self-interpretable methods, in detecting these patterns.
RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design
We introduce RNA-FrameFlow, the first generative model for 3D RNA backbone design.
DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs
Moreover, we conduct extensive benchmark experiments on DTGB, evaluating 7 popular dynamic graph learning algorithms and their variants of adapting to text attributes with LLM embeddings, along with 6 powerful large language models (LLMs).
Protein-Nucleic Acid Complex Modeling with Frame Averaging Transformer
Moreover, we curate five real-world protein-aptamer interaction datasets and show that the contact map predicted by FAFormer serves as a strong binding indicator for aptamer screening.
Explaining Graph Neural Networks via Structure-aware Interaction Index
The Shapley value is a prominent tool for interpreting black-box machine learning models thanks to its strong theoretical foundation.
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).
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.
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.
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.
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.
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.
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.
Generative Explanations for Graph Neural Network: Methods and Evaluations
Graph Neural Networks (GNNs) achieve state-of-the-art performance in various graph-related tasks.
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.
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.
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.
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.
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.
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.
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.
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.
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.
HiPool: Modeling Long Documents Using Graph Neural Networks
Encoding long sequences in Natural Language Processing (NLP) is a challenging problem.
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.
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).
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.
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.
FIMP: Foundation Model-Informed Message Passing for Graph Neural Networks
FIMP outperforms strong baselines, demonstrating that it can effectively leverage state-of-the-art foundation models in graph tasks.
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.
How Powerful is Implicit Denoising in Graph Neural Networks
However, the analysis of implicit denoising effect in graph neural networks remains open.
FOLIO: Natural Language Reasoning with First-Order Logic
We present FOLIO, a human-annotated, logically complex and diverse dataset for reasoning in natural language (NL), equipped with first-order logic (FOL) annotations.
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.
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.
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.
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
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.
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.
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
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.
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.
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.
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.
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.
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)
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.
Neural Execution of Graph Algorithms
Graph Neural Networks (GNNs) are a powerful representational tool for solving problems on graph-structured inputs.
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.
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.
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
+3
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
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.
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.
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences.
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.
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
