New framework assesses graph-learning datasets for better evaluation.
problem Insufficient evaluation of graph-learning datasets and methods.
method Introduces Rings framework for dataset ablations and proposes performance separability and mode complementarity measures.
result Demonstrates utility of Rings framework for graph-learning dataset evaluation.
TUDataset provides benchmark datasets for graph learning.
problem Lack of meaningful benchmark datasets and standardized evaluation procedures for graph learning.
method Collection and standardization of 120 graph datasets from various applications.
result Standardized evaluation procedures and baseline experiments provided.
OGB provides diverse graph datasets for robust ML research.
problem Challenges in scalable and robust graph machine learning.
method Unified evaluation protocol, diverse datasets, and automated pipeline.
result Significant scalability and generalization challenges identified.
Enhances graph classification models on small datasets.
problem Over-fitting and undergeneralization on small-scale benchmark datasets.
method Data augmentation via graph structure transformation and model evolution framework.
result Average improvement of 3 - 13% accuracy on graph classification tasks.
Graph learning is often unnecessary for common benchmarks, as node features can suffice.
problem The necessity of graph learning in common graph benchmarks is often assumed.
method We compared graph learning to feature-only models on seven datasets and found that graph structure often adds little to performance.
result Node features can often suffice for common graph benchmarks, challenging the orthodoxy.
Deep learning improves community detection in graph datasets.
problem Community detection in graph datasets using deep learning.
method Proposes a deep learning approach using Gumbel Softmax for clustering graph nodes.
result The new approach significantly outperforms traditional clustering methods.
New non-homophilous graph datasets and methods for scalable learning.
problem Evaluation of graph learning methods on non-homophilous graphs.
method Introducing LINKX, a simple yet strong method for scalable non-homophilous graph learning.
result LINKX achieves state-of-the-art performance on non-homophilous graphs.
Graphs indistinguishable by GNNs are fully characterized.
problem Limited expressiveness of GNNs in distinguishing non-isomorphic graphs.
method Theory of covering spaces to characterize GNN equivalence classes.
result Arbitrarily many non-isomorphic graphs that GNNs cannot distinguish.
CHILI datasets tackle inorganic nanomaterials, advancing graph machine learning.
problem Challenges in modelling inorganic crystalline materials and nanomaterials with graph ML.
method Presented two large-scale datasets of inorganic nanomaterials, defined property and structure prediction tasks.
result Benchmarked performance of graph ML methods on inorganic nanomaterials, highlighting areas for future work.
GCC pre-trains graph neural networks to transfer across diverse datasets.
problem Non-transferable graph models trained for specific datasets.
method Graph Contrastive Coding (GCC) framework using contrastive learning.
result GCC pre-trained models achieve competitive performance across multiple datasets.
Unified framework models graph data as a mixture of graphons using graph moments.
problem Graph datasets often mix from multiple underlying distributions.
method Model graph data as a mixture of graphons, using graph moments to cluster graphs.
result Graphs from similar graphons exhibit similar motif densities, enabling principled estimation of graphon mixture components.
Wiki-CS dataset benchmarks Graph Neural Networks using Wikipedia articles.
problem Benchmarking Graph Neural Networks on a new domain with structural differences.
method Derived from Wikipedia, nodes represent Computer Science articles, edges from hyperlinks, 10 classes for different branches, evaluated semi-supervised node classification and link prediction.
result Graph Neural Networks perform well on Wiki-CS, showing structural differences from earlier benchmarks.
A new method for spectral barycentre of graph datasets.
problem Creating a summary graph from a set of graphs with community structure.
method Using multiscale spectral distance based on normalized graph Laplacian eigenvalues.
result The barycentre inherits the topological structure of the graphs in the sample dataset.
In this paper, we conduct an empirical investigation of neural query graph ranking approaches for the task of complex question answering over knowledge graphs. We experiment with six different ranking models and propose a novel self-attention based slot matching model which exploits the inherent structure of query grap…
Previously, we proposed a physically inspired rule to organize the data points in a sparse yet effective structure, called the in-tree (IT) graph, which is able to capture a wide class of underlying cluster structures in the datasets, especially for the density-based datasets. Although there are some redundant edges or…
A new neural network model for molecular graphs that learns efficiently and accurately.
problem Learning on molecular graphs with cycles and complex structures.
method Hierarchical inter-message passing using raw graph and junction tree representations.
result The model outperforms classical GNNs in detecting cycles and is efficient to train.
This paper presents a large Bitcoin transaction graph dataset for research.
problem Lack of a comprehensive dataset for Bitcoin research.
method Created a large-scale transactions graph dataset with timestamps.
result Established a large publicly available dataset for advanced research.
Simple Euclidean models outperform hyperbolic graph learning models.
problem The effectiveness of hyperbolic graph learning models is questioned.
method Careful analysis of hyperbolic graph representation learning, identifying and addressing issues with baselines, modeling assumptions, and metric usage.
result Simple Euclidean models often outperform hyperbolic graph learning models, even on hyperbolic datasets.
PDNAS optimizes GNN architectures for diverse datasets.
problem Inadequate adaptability and combinatorial search space in GNNs.
method Dual architecture search (micro- and macro-architectures) with gradient-based optimization.
result PDNAS finds deeper GNNs with better performance on diverse datasets.
In this work, we provide a new formulation for Graph Convolutional Neural Networks (GCNNs) for link prediction on graph data that addresses common challenges for biomedical knowledge graphs (KGs). We introduce a regularized attention mechanism to GCNNs that not only improves performance on clean datasets, but also favo…
New method constructs graphs from data efficiently, suitable for large datasets.
problem Memory and runtime limitations of traditional TMFG for large datasets.
method Uses k-Nearest Neighbors Graphs and memory management for scalable graph construction.
result Provides a parsimonious way to construct graphs for learning tasks.
Learning powerful data embeddings has become a center piece in machine learning, especially in natural language processing and computer vision domains. The crux of these embeddings is that they are pretrained on huge corpus of data in a unsupervised fashion, sometimes aided with transfer learning. However currently in …
Low-dimensional embeddings of knowledge graphs and behavior graphs have proved remarkably powerful in varieties of tasks, from predicting unobserved edges between entities to content recommendation. The two types of graphs can contain distinct and complementary information for the same entities/nodes. However, previous…
Enhances graph neural networks by creating virtual data examples.
problem Lack of examples to identify optimal graph rationales in graph applications.
method Introduces environment replacement to create virtual data examples and proposes a framework for rationale-environment separation and representation learning.
result Demonstrates the effectiveness and efficiency of the augmentation-based graph rationalization framework on molecular and polymer datasets.
We present GraphTSNE, a novel visualization technique for graph-structured data based on t-SNE. The growing interest in graph-structured data increases the importance of gaining human insight into such datasets by means of visualization. Among the most popular visualization techniques, classical t-SNE is not suitable o…
Grale designs graphs for graph learning, improving performance on large datasets.
problem Finding the right graph for semi-supervised learning with billions of nodes.
method Fuses multiple similarity measures using locality sensitive hashing to create task-specific graphs.
result Grale detects a large number of malicious actors, increasing recall by 89%.
Study reveals significant performance flips in GLOD using repurposed graph classification datasets.
problem Performance discrepancies in graph-level outlier detection using repurposed classification datasets.
method Repurposed binary classification datasets for GLOD; analyzed ROC-AUC performance.
result Performance of GLOD models significantly flips depending on which class is down-sampled.
Unified framework sparsifies GNNs for faster inference on large graphs.
problem Space and computational bottlenecks in GNNs due to graph size and connectivity.
method Unified GNN sparsification (UGS) framework that prunes graph adjacency matrix and model weights.
result Graph lottery tickets (GLTs) can be trained in isolation to match full model performance.
In this paper we provide a principled approach to solve a transductive classification problem involving a similar graph (edges tend to connect nodes with same labels) and a dissimilar graph (edges tend to connect nodes with opposing labels). Most of the existing methods, e.g., Information Regularization (IR), Weighted …
A new GCN variant tackles large eigengaps in dense graphs and hypergraphs.
problem Large eigengaps in dense graphs and hypergraphs hinder popular GCN architectures.
method Uses pseudoinverse of the Laplacian and low-rank approximation for efficient computation.
result Improves runtime and accuracy in various experiments with real-world datasets.
We present an attention mechanism inspired from definition of screened Coulomb potential. This attention mechanism was used to interpret the Graph Attention (GAT) model layers and training dataset by using a flexible and scalable framework (CoulGAT) developed for this purpose. Using CoulGAT, a forest of plain and resne…
FakeEdge tackles dataset shift in link prediction tasks.
problem Dataset shift between training and testing sets in link prediction.
method Model-agnostic technique to mitigate graph topological gap.
result Extensive experiments show FakeEdge's superiority on multiple datasets.
Proposes DeGLIF to denoise graph data for label noise robustness.
problem Label noise in graph data makes node classification challenging.
method Uses leave-one-out influence function to denoise graph data.
result DeGLIF improves accuracy in node classification on noisy datasets.
Graph Convolutional Networks (GCNs) have proven to be successful tools for semi-supervised learning on graph-based datasets. For sparse graphs, linear and polynomial filter functions have yielded impressive results. For large non-sparse graphs, however, network training and evaluation becomes prohibitively expensive. B…
Non-local GNNs improve performance on disassortative graphs.
problem Efficiency and performance issues in local aggregation for disassortative graphs.
method Proposes a non-local aggregation framework with attention-guided sorting.
result Significantly outperforms previous methods on disassortative graphs.
The study analyzes and benchmarks graph conformal prediction methods.
problem Uncertainty quantification in graph node classification.
method Analysis and scaling of existing graph conformal prediction methods.
result Justified recommendations for future graph conformal prediction research.
A new model for multiview data analysis using graph autoencoders.
problem Nonlinear multiview canonical correlation analysis for large datasets.
method Variational approach with graph convolutional neural networks.
result Competitive performance on classification, clustering, and recommendation tasks.
We address the problem of prediction of multivariate data process using an underlying graph model. We develop a method that learns a sparse partial correlation graph in a tuning-free and computationally efficient manner. Specifically, the graph structure is learned recursively without the need for cross-validation or p…
Developing new ideas and algorithms in the fields of graph processing and relational learning requires public datasets. While Wikidata is the largest open source knowledge graph, involving more than fifty million entities, it is larger than needed in many cases and even too large to be processed easily. Still, it is a …
Graph Convolutional Neural Networks (GCNNs) are generalizations of CNNs to graph-structured data, in which convolution is guided by the graph topology. In many cases where graphs are unavailable, existing methods manually construct graphs or learn task-driven adaptive graphs. In this paper, we propose Graph Learning Ne…
We study the effect of structural variation in graph data on the predictive performance of graph kernels. To this end, we introduce a novel, noise-robust adaptation of the GraphHopper kernel and validate it on benchmark data, obtaining modestly improved predictive performance on a range of datasets. Next, we investigat…
Graphs are complex objects that do not lend themselves easily to typical learning tasks. Recently, a range of approaches based on graph kernels or graph neural networks have been developed for graph classification and for representation learning on graphs in general. As the developed methodologies become more sophistic…
New kernel speeds up graph regression in physics.
problem Handling large, sparse graphs with continuous node attributes in physics.
method Introduced Sliced Wasserstein Weisfeiler-Lehman (SWWL) graph kernel for Gaussian process regression.
result The SWWL kernel is efficient and positive definite, reducing complexity.
LineMVGNN improves AML detection by integrating multi-view graph learning.
problem Ineffective and scalable AML systems using rule-based methods.
method LineMVGNN combines multi-view graph neural networks with line-graph features.
result LineMVGNN outperforms state-of-the-art methods in detecting money laundering.
Develops methods to analyze manifold singularities using graph Laplacian.
problem Analyzing geometric properties of singularities in datasets.
method Theory and methods using the graph Laplacian to provide explicit bounds on manifold singularities.
result Explicit bounds on the graph Laplacian for functions near manifold singularities.
New dataset and techniques detect money laundering patterns in crypto.
problem Detecting money laundering in cryptocurrency networks.
method Subgraph representation learning using scalable GNNs.
result Accurately classify new criminal activity in cryptocurrency.
New centrality-based graph shift operators improve graph neural networks.
problem Improving graph neural networks by enhancing graph shift operators.
method Proposed Centrality Graph Shift Operators (CGSOs) using global centrality metrics.
result CGSOs lead to improved performance in graph neural networks on real-world datasets.
This paper explores different graph neural network functions to improve graph isomorphism.
problem Lack of robust implementation for graph neural networks due to limited analysis of underlying functions.
method Examines various alternative functions for different modules in GNNs using benchmark datasets.
result Generally used underlying techniques do not always capture the overall graph structure.