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Found 29 papers, 23 papers with code
Revisiting and Benchmarking Graph Autoencoders: A Contrastive Learning Perspective
Graph autoencoders (GAEs) are self-supervised learning models that can learn meaningful representations of graph-structured data by reconstructing the input graph from a low-dimensional latent space.
L^2CL: Embarrassingly Simple Layer-to-Layer Contrastive Learning for Graph Collaborative Filtering
Surprisingly, we find that L2CL, using only one-hop contrastive learning paradigm, is able to capture intrinsic semantic structures and improve the quality of node representation, leading to a simple yet effective architecture.
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
Another promising line of research involves the adoption of modularity maximization, a popular and effective measure for community detection, as the guiding principle for clustering tasks.
One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes
Accordingly, we propose a graph fairness framework based on invariant learning, namely FairINV, which enables the training of fair GNNs to accommodate various sensitive attributes within a single training session.
State Space Models on Temporal Graphs: A First-Principles Study
Recently, state space models (SSMs), which are framed as discretized representations of an underlying continuous-time linear dynamical system, have garnered substantial attention and achieved breakthrough advancements in independent sequence modeling.
Fair Graph Representation Learning via Sensitive Attribute Disentanglement
In particular, the objective of group fairness is to ensure that the decisions made by GNNs are independent of the sensitive attribute.
SGHormer: An Energy-Saving Graph Transformer Driven by Spikes
However, the costs behind outstanding performances of GTs are higher energy consumption and computational overhead.
Rethinking and Simplifying Bootstrapped Graph Latents
Graph contrastive learning (GCL) has emerged as a representative paradigm in graph self-supervised learning, where negative samples are commonly regarded as the key to preventing model collapse and producing distinguishable representations.
The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation
Experiments on several benchmark datasets demonstrate that FairGKD, which does not require access to demographic information, significantly improves the fairness of GNNs by a large margin while maintaining their utility.
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data.
Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs
Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily.
SAILOR: Structural Augmentation Based Tail Node Representation Learning
In the pursuit of promoting the expressiveness of GNNs for tail nodes, we explore how the deficiency of structural information deteriorates the performance of tail nodes and propose a general Structural Augmentation based taIL nOde Representation learning framework, dubbed as SAILOR, which can jointly learn to augment the graph structure and extract more informative representations for tail nodes.
Oversmoothing: A Nightmare for Graph Contrastive Learning?
Oversmoothing is a common phenomenon observed in graph neural networks (GNNs), in which an increase in the network depth leads to a deterioration in their performance.
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensionality to learn informative and discriminative full-precision representations, raising concerns about computation, memory footprint, and energy consumption burden (largely overlooked) for real-world applications.
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs
Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones.
Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
We approach the problem by our proposed STEP, a self-supervised temporal pruning framework that learns to remove potentially redundant edges from input dynamic graphs.
Spectral Adversarial Training for Robust Graph Neural Network
In this work, we seek to address these challenges and propose Spectral Adversarial Training (SAT), a simple yet effective adversarial training approach for GNNs.
Are All Edges Necessary? A Unified Framework for Graph Purification
Graph Neural Networks (GNNs) as deep learning models working on graph-structure data have achieved advanced performance in many works.
Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks
We explore a new direction in that we can capture the evolving dynamics of temporal graphs with spiking neural networks (SNNs) instead of RNNs.
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery.
What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders
The last years have witnessed the emergence of a promising self-supervised learning strategy, referred to as masked autoencoding.
Spiking Graph Convolutional Networks
Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information.
GUARD: Graph Universal Adversarial Defense
To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against adversarial attacks.
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack
Despite the progress, applying DGL to real-world applications faces a series of reliability threats including inherent noise, distribution shift, and adversarial attacks.
Neighboring Backdoor Attacks on Graph Convolutional Network
To address such a challenge, we set the trigger as a single node, and the backdoor is activated when the trigger node is connected to the target node.
Understanding Structural Vulnerability in Graph Convolutional Networks
In this work, we theoretically and empirically demonstrate that structural adversarial examples can be attributed to the non-robust aggregation scheme (i. e., the weighted mean) of GCNs.
GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software
Graph Neural Networks (GNNs) have recently shown to be powerful tools for representing and analyzing graph data.
Adversarial Attack on Large Scale Graph
Currently, most works on attacking GNNs are mainly using gradient information to guide the attack and achieve outstanding performance.
A Survey of Adversarial Learning on Graphs
To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically.
