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Found 21 papers, 10 papers with code
Let Your Graph Do the Talking: Encoding Structured Data for LLMs
How can we best encode structured data into sequential form for use in large language models (LLMs)?
The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure
Subsequently, we define the notion of a "winning ticket" for graph structure - an extremely sparse subset of edges that can deliver a robust approximation of the entire graph's performance.
UGSL: A Unified Framework for Benchmarking Graph Structure Learning
We implement a wide range of existing models in our framework and conduct extensive analyses of the effectiveness of different components in the framework.
HUGE: Huge Unsupervised Graph Embeddings with TPUs
A high-performance graph embedding architecture leveraging Tensor Processing Units (TPUs) with configurable amounts of high-bandwidth memory is presented that simplifies the graph embedding problem and can scale to graphs with billions of nodes and trillions of edges.
Examining the Effects of Degree Distribution and Homophily in Graph Learning Models
In this work we examine how two additional synthetic graph generators can improve GraphWorld's evaluation; LFR, a well-established model in the graph clustering literature and CABAM, a recent adaptation of the Barabasi-Albert model tailored for GNN benchmarking.
Unsupervised Embedding Quality Evaluation
Unsupervised learning has recently significantly gained in popularity, especially with deep learning-based approaches.
On Classification Thresholds for Graph Attention with Edge Features
In CSBM the nodes and edge features are obtained from a mixture of Gaussians and the edges from a stochastic block model.
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank
Personalized PageRank (PPR) is a fundamental tool in unsupervised learning of graph representations such as node ranking, labeling, and graph embedding.
TF-GNN: Graph Neural Networks in TensorFlow
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow.
Tackling Provably Hard Representative Selection via Graph Neural Networks
Representative Selection (RS) is the problem of finding a small subset of exemplars from a dataset that is representative of the dataset.
Synthetic Graph Generation to Benchmark Graph Learning
This shockingly small sample size (~10) allows for only limited scientific insight into the problem.
GraphWorld: Fake Graphs Bring Real Insights for GNNs
Using GraphWorld, a user has fine-grained control over graph generator parameters, and can benchmark arbitrary GNN models with built-in hyperparameter tuning.
GRASP: Graph Alignment through Spectral Signatures
In this paper, we transfer the shape-analysis concept of functional maps from the continuous to the discrete case, and treat the graph alignment problem as a special case of the problem of finding a mapping between functions on graphs.
InstantEmbedding: Efficient Local Node Representations
In this paper, we introduce InstantEmbedding, an efficient method for generating single-node representations using local PageRank computations.
Graph Clustering with Graph Neural Networks
Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction.
FREDE: Anytime Graph Embeddings
Low-dimensional representations, or embeddings, of a graph's nodes facilitate several practical data science and data engineering tasks.
Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs
Graph comparison is a fundamental operation in data mining and information retrieval.
The Shape of Data: Intrinsic Distance for Data Distributions
The ability to represent and compare machine learning models is crucial in order to quantify subtle model changes, evaluate generative models, and gather insights on neural network architectures.
SGR: Self-Supervised Spectral Graph Representation Learning
Representing a graph as a vector is a challenging task; ideally, the representation should be easily computable and conducive to efficient comparisons among graphs, tailored to the particular data and analytical task at hand.
NetLSD: Hearing the Shape of a Graph
However, it is a hard task in terms of the expressiveness of the employed similarity measure and the efficiency of its computation.
Social and Information Networks
VERSE: Versatile Graph Embeddings from Similarity Measures
Embedding a web-scale information network into a low-dimensional vector space facilitates tasks such as link prediction, classification, and visualization.
