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Found 27 papers, 15 papers with code
Unleashing the Power of LLMs as Multi-Modal Encoders for Text and Graph-Structured Data
Graph-structured information offers rich contextual information that can enhance language models by providing structured relationships and hierarchies, leading to more expressive embeddings for various applications such as retrieval, question answering, and classification.
Covering a Graph with Dense Subgraph Families, via Triangle-Rich Sets
A fundamental task in graph mining is to discover these dense subgraphs.
All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks
In this paper, we investigate how LLMs can be leveraged in a computationally efficient fashion to benefit rich graph-structured data, a modality relatively unexplored in LLM literature.
AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning
Large language model (LLM) agents have demonstrated impressive capabilities in utilizing external tools and knowledge to boost accuracy and reduce hallucinations.
STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases
To address the gap, we develop STARK, a large-scale Semi-structure retrieval benchmark on Textual and Relational Knowledge Bases.
An Interpretable Ensemble of Graph and Language Models for Improving Search Relevance in E-Commerce
This lack of interpretability hinders the development and adoption of new techniques in the field.
Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training
Notably, CONVMATCH achieves up to 95% of the prediction performance of GNNs on node classification while trained on graphs summarized down to 1% the size of the original graph.
ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding
ForeSeer transfers reviews from similar products on a large product graph and exploits these reviews to predict aspects that might emerge in future reviews.
Communication-Free Distributed GNN Training with Vertex Cut
Here, we introduce CoFree-GNN, a novel distributed GNN training framework that significantly speeds up the training process by implementing communication-free training.
Ranked #2 on
Node Classification
on Reddit
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation.
Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation
Backed by our theoretical analysis, instead of maximizing the recovery of cross-instance node dependencies -- which has been considered the key behind closing the performance gap between model aggregation and centralized training -- , our framework leverages randomized assignment of nodes or super-nodes (i. e., collections of original nodes) to partition the training graph such that it improves data uniformity and minimizes the discrepancy of gradient and loss function across instances.
You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction
We identify this setting as Graph Intersection-induced Transfer Learning (GITL), which is motivated by practical applications in e-commerce or academic co-authorship predictions.
Search Behavior Prediction: A Hypergraph Perspective
With these hyperedges, we augment the original bipartite graph into a new \textit{hypergraph}.
TuneUp: A Simple Improved Training Strategy for Graph Neural Networks
Extensive evaluation of TuneUp on five diverse GNN architectures, three types of prediction tasks, and both transductive and inductive settings shows that TuneUp significantly improves the performance of the base GNN on tail nodes, while often even improving the performance on head nodes.
Text Enriched Sparse Hyperbolic Graph Convolutional Networks
In TESH-GCN, we extract semantic node information, which successively acts as a connection signal to extract relevant nodes' local neighborhood and graph-level metapath features from the sparse adjacency tensor in a reformulated hyperbolic graph convolution layer.
Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search
This paper introduces the "Shopping Queries Dataset", a large dataset of difficult Amazon search queries and results, publicly released with the aim of fostering research in improving the quality of search results.
Learning Backward Compatible Embeddings
We formalize the problem where the goal is for the embedding team to keep updating the embedding version, while the consumer teams do not have to retrain their models.
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment
In this paper, we explore multilingual KG completion, which leverages limited seed alignment as a bridge, to embrace the collective knowledge from multiple languages.
Ranked #3 on
Knowledge Graph Completion
on DPB-5L (French)
Task-Agnostic Graph Explanations
They are also unable to provide explanations in cases where the GNN is trained in a self-supervised manner, and the resulting representations are used in future downstream tasks.
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods
We propose Cold Brew, a teacher-student distillation approach to address the SCS and noisy-neighbor challenges for GNNs.
Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs
Current approaches employ spatial geometries such as boxes to learn query representations that encompass the answer entities and model the logical operations of projection and intersection.
Task-Agnostic Graph Neural Explanations
TAGE enables the explanation of GNN embedding models without downstream tasks and allows efficient explanation of multitask models.
Scalable Feature Selection for (Multitask) Gradient Boosted Trees
Gradient Boosted Decision Trees (GBDTs) are widely used for building ranking and relevance models in search and recommendation.
Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs
Promising approaches to tackle this problem include embedding the KG units (e. g., entities and relations) in a Euclidean space such that the query embedding contains the information relevant to its results.
Regularized Graph Convolutional Networks for Short Text Classification
The lack of structure in short text sequences limits the success of popular NLP methods based on deep learning.
Learning Robust Models for e-Commerce Product Search
Showing items that do not match search query intent degrades customer experience in e-commerce.
Learning Diffusion using Hyperparameters
Despite this obstacle, we can shrink the best-known sample complexity bound for learning IC by a factor of |E|/d where |E| is the number of edges in the graph and d is the dimension of the hyperparameter.
