InfoGraph learns graph-level representations via mutual information maximization.
problem Learning graph-level representations for unsupervised and semi-supervised scenarios.
method Maximizes mutual information between graph-level representation and substructure representations.
result InfoGraph outperforms state-of-the-art methods on graph classification and molecular property prediction.
UGRAPHEMB embeds graphs into vectors preserving their proximity, achieving competitive results.
problem Graph-level representation learning in an unsupervised and inductive manner.
method UGRAPHEMB uses graph-graph proximity to embed graphs into a vector space. MSNA generates multi-scale node attention for graph-level embedding.
result UGRAPHEMB achieves competitive accuracy in graph classification, similarity ranking, and visualization tasks.
Study compares hypergraph and graph-level models for higher-order relational learning.
problem Evaluating effectiveness of hypergraph-level vs. graph-level models in relational learning.
method Systematic evaluation of various hypergraph and graph-level architectures.
result Graph-level models applied to hypergraph expansions outperform hypergraph-level models.
Predicating macroscopic influences of drugs on human body, like efficacy and toxicity, is a central problem of small-molecule based drug discovery. Molecules can be represented as an undirected graph, and we can utilize graph convolution networks to predication molecular properties. However, graph convolutional network…
Graph representation learning improves with domain knowledge.
problem Efficiently learning graph representations from scarce labels.
method Multi-task knowledge distillation combining graph metrics.
result Improves prediction performance, especially with limited training data.
Recently a variety of methods have been developed to encode graphs into low-dimensional vectors that can be easily exploited by machine learning algorithms. The majority of these methods start by embedding the graph nodes into a low-dimensional vector space, followed by using some scheme to aggregate the node embedding…
Devign uses graph neural networks to identify vulnerabilities efficiently.
problem Challenging and tedious process of identifying vulnerabilities in software systems.
method Devign employs a graph neural network to classify graph-level vulnerabilities using comprehensive code semantic representations.
result Devign significantly outperforms state-of-the-art models in vulnerability identification.
SHAKE-GNN scales GNNs for large graphs with multi-scale representations.
problem Scaling Graph Neural Networks (GNNs) to large graphs.
method SHAKE-GNN uses a hierarchy of Kirchhoff Forests for stochastic multi-resolution graph decompositions.
result SHAKE-GNN achieves competitive performance on large-scale graph classification benchmarks.
Graph embedding improves fMRI classification and reveals brain region differences in ASD.
problem Difficult to embed informative brain fMRI representations due to high dimensionality and low SNR.
method Modelled fMRI as a graph, used GNN to learn from graph data, incorporated mutual information loss (Infomax).
result Infomax graph embedding improves classification performance and reveals separable nodal representations of ASD and HC groups.
DEMO-Net improves graph neural networks by focusing on node degree.
problem Limited analysis of graph convolution properties and lack of degree-specific graph structure.
method Proposes DEMO-Net, a degree-specific graph neural network that recursively identifies 1-hop neighborhood structures and uses multi-task learning for node representation learning.
result Demonstrates effectiveness and efficiency of DEMO-Net on node and graph classification benchmarks.
GraphPPD models graph-level uncertainty using GNN embeddings.
problem Capturing uncertainty in graph-level predictions.
method Variational modelling for posterior predictive distribution.
result Effective uncertainty-aware predictions on graph-level tasks.
Graph Interplay (GIP) improves GSSL performance by enhancing graph-level communications.
problem Improving graph self-supervised learning performance without labeled data.
method Graph Interplay (GIP) introduces random inter-graph edges within standard batches to enhance GSSL methods.
result GIP significantly outperforms existing GSSL methods across multiple benchmarks.
This paper proposes a method to learn graph representations by partitioning edges into communities.
problem Graph neural networks ignore how edges are formed, leading to suboptimal representation learning.
method Introduces a generative model to partition edges into community-specific weighted edges, then uses these for GNN-based inference and classification.
result The method learns discriminative representations for both node-level and graph-level classification tasks.
PatchGT uses non-trainable graph patches to improve graph representation learning.
problem Learning high-level information in graph tasks with direct Transformer models.
method PatchGT segments graphs into non-trainable patches, uses GNN for patch-level learning, and Transformer for graph-level learning.
result PatchGT achieves higher expressiveness and competitive performance on benchmark datasets.
SubGNN tackles subgraph prediction challenges in graphs.
problem Subgraphs in graphs are challenging to predict due to their internal topology and external connectivity.
method SubGNN introduces a novel subgraph routing mechanism to learn disentangled subgraph representations.
result SubGNN achieves considerable performance gains on subgraph classification tasks, outperforming strong baseline methods.
Unified benchmark for GLAD and GLOD methods across 35 datasets.
problem Gap between GLAD and GLOD research due to distinct evaluation setups.
method Comprehensive evaluation framework that unifies GLAD and GLOD.
result Multi-dimensional analyses of existing methods' strengths and limitations.
Extract common latent factors from graphs for better representation learning.
problem Graph-level representation learning challenges due to limited labeled data and poor negative sample selection.
method Graph-wise Common Latent Factor Extraction (GCFX) using deepGCFX model.
result Improved graph-level and node-level tasks performance compared to state-of-the-art methods.
PathBoost boosts graph-level predictions using path-based features.
problem Graph-level classification and regression challenges.
method Gradient tree boosting method for graph-level prediction.
result PathBoost outperforms graph neural networks and graph kernel approaches in many cases.
AWARE improves graph prediction by aggregating walks with attention schemes.
problem Improving graph prediction accuracy using walk aggregation.
method Integrates attention schemes into walk-aggregating GNNs.
result AWARE outperforms existing methods in graph-level prediction tasks.
A new graph classification method using persistent homology.
problem Graph classification with graph connectivity structure.
method Learnable filter function for persistent homology computation.
result Empirically, the method compares favorably to previous techniques.
Minimal graph level sets are concave if boundary is concave.
problem Understanding curvature of minimal graph level sets.
method Proved an inequality and showed geometric properties.
result Level sets of minimal graphs are concave if boundary is concave.
New approach learns graph representations by contrasting first-order neighbors and graph diffusion views.
problem Learning node and graph level representations from graph data.
method Self-supervised approach using contrastive learning of multi-scale encodings.
result Achieves state-of-the-art performance on 8 out of 8 benchmarks.
HGP-SL pools and learns graph structure for hierarchical representation learning.
problem Graph pooling is overlooked in GNN models, limiting hierarchical representation learning.
method Integrates graph pooling and structure learning into a unified module.
result HGP-SL improves graph classification performance on benchmarks.
MathNet uses wavelets for graph representation and learning.
problem Graph Neural Networks (GNNs) for graph classification and regression.
method Multiresolution Haar-like wavelets, graph convolution, and pooling.
result MathNet achieves notable accuracy gains on graph classification and regression tasks.
Graph Kalman filters adapt classical filters to graph data.
problem Adapting classical Kalman filters to graph data.
method Generalizes Kalman filters to attributed graphs, learning state-transition and readout functions end-to-end.
result Adapted Kalman filters can predict graph outputs.
Proposes an unsupervised graph neural network for entire graph representation.
problem Lack of unsupervised methods for entire graph representation.
method Combines hierarchical graph neural networks and mutual information maximization.
result Improves state-of-the-art performance on multiple graph level tasks.
Graph InfoClust learns node representations by capturing cluster-level information, improving graph mining tasks.
problem Leveraging cluster-level node information for unsupervised graph representation learning.
method Graph InfoClust (GIC) uses a differentiable K-means method to compute clusters and jointly optimizes mutual information between nodes of the same cluster.
result GIC outperforms state-of-the-art methods in various downstream tasks with a 0.9% to 6.1% gain.
GraphDINO learns neuronal morphologies from unlabeled data.
problem Unsupervised learning of neuronal morphologies from unlabeled data.
method Transformer-based approach with novel attention mechanism and data augmentation.
result GraphDINO yields morphological clusterings on par with expert classification.
SLIM model tackles graph classification by resolving part-interaction dilemmas.
problem Difficulty in modeling graph parts and their interactions in graph classification.
method SLIM model, which solves resolution dilemmas and leverages explicit interactions.
result SLIM offers improved interpretability, accuracy, and new insights in graph representation learning.
GraphToken encodes structured data for LLMs, improving graph reasoning tasks.
problem Efficiently encoding structured data for large language models.
method GraphToken learns an encoding function to extend prompts with explicit structured information.
result Significant improvements across node, edge, and graph-level tasks on the GraphQA benchmark.
ASAP improves graph pooling for hierarchical graph representations.
problem Pooling in graphs fails to effectively capture substructure or scale to large graphs.
method ASAP uses self-attention and modified GNN to capture node importance and learn sparse soft cluster assignments.
result Combining ASAP with GNN architectures leads to state-of-the-art results on graph classification benchmarks.
WEGL embeds graphs in a vector space for faster machine learning.
problem Efficiently embedding graphs for machine learning tasks.
method Wasserstein distance for node embedding similarity, Monge maps for graph representation.
result State-of-the-art classification performance with superior computational efficiency.
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.
Propagation-regularization improves GNN performance by infusing extra graph information.
problem The effectiveness of graph Laplacian regularization in GNNs is questioned and improved upon.
method Introducing Propagation-regularization (P-reg) to enhance GNN performance.
result P-reg boosts GNN performance on various tasks across multiple datasets.
FairDTD improves fairness in GNNs by distilling dual teacher knowledge, balancing utility and bias.
problem Bias in GNN predictions due to sensitive attributes.
method Dual-Teacher Distillation with a causal graph model, feature and structure teachers, and graph-level distillation.
result Achieves optimal fairness while preserving high model utility.
PiNet improves graph classification efficiency and accuracy.
problem Graph level classification challenges.
method Attention-based pooling mechanism for graph convolution operations.
result Superior performance and high sample efficiency.
A new aggregation strategy improves GNN performance and learning dynamics.
problem Improving expressivity and learning dynamics of GNNs.
method Proposes a variance-preserving aggregation function (VPA) for GNNs.
result VPA leads to increased predictive performance and improved learning dynamics.
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.
Deep learning on graph structures has shown exciting results in various applications. However, few attentions have been paid to the robustness of such models, in contrast to numerous research work for image or text adversarial attack and defense. In this paper, we focus on the adversarial attacks that fool the model by…
Graph similarity computation is one of the core operations in many graph-based applications, such as graph similarity search, graph database analysis, graph clustering, etc. Since computing the exact distance/similarity between two graphs is typically NP-hard, a series of approximate methods have been proposed with a t…
ABI adapts to graph data for fast, scalable inference.
problem Challenges in inference on graph-structured data.
method Amortized Bayesian Inference (ABI) framework for graph data.
result ABI successfully addresses challenges in graph data inference.
GraphBench creates a unified benchmark for graph learning tasks.
problem Fragmented benchmarking practices and inconsistent evaluation protocols in graph learning.
method Developed a comprehensive benchmark suite with standardized evaluation protocols.
result Established principled baselines for future research in graph learning.
New strategy improves GNN pre-training for graph datasets.
problem Effective pre-training for graph neural networks on graph datasets.
method Expressive pre-training of GNNs at both node and graph levels.
result Significant improvement in generalization across downstream tasks.
iGCL preserves graph semantics in latent space augmentations.
problem Manual tuning of augmentation ratios and unexpected graph changes.
method iGCL uses a Variational Graph Auto-Encoder to learn augmentations in the latent space, optimizing an upper bound for contrastive loss.
result iGCL achieves state-of-the-art performance on graph-level and node-level tasks.
EvoNet predicts events in time-series data by evolving state graphs.
problem Predicting events in time-series data with interpretable patterns.
method Evolutionary State Graph (ESG) and EvoNet model.
result EvoNet outperforms baselines and provides insights into event predictions.
AgentNet is a graph neural network that learns to walk graphs intelligently, outperforming traditional methods.
problem Graph-level tasks, especially distinguishing and classifying graphs.
method AgentNet uses a computational model inspired by sublinear algorithms, where neural agents walk the graph and collectively decide the output.
result AgentNet can distinguish and separate graphs that are hard to distinguish, outperforming traditional graph neural networks.
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.
Generative model for inferring graph from time series data.
problem Generating graphs conditioned on multivariate time series data.
method Time Series Conditioned Graph Generation-Generative Adversarial Networks (TSGG-GAN).
result Demonstrates effectiveness and generalizability of TSGG-GAN on synthetic and real-world datasets.