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Found 28 papers, 12 papers with code
UNREAL:Unlabeled Nodes Retrieval and Labeling for Heavily-imbalanced Node Classification
To select which unlabeled nodes to add, we propose geometric ranking to rank unlabeled nodes.
Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task.
xASTNN: Improved Code Representations for Industrial Practice
The proposed xASTNN has three advantages.
Hyperbolic Curvature Graph Neural Network
To this end, we explore the properties of the local discrete curvature of graph topology and the continuous global curvature of embedding space.
Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation
Recent studies show that Graph Neural Networks(GNNs) are vulnerable and easily fooled by small perturbations, which has raised considerable concerns for adapting GNNs in various safety-critical applications.
Deep Explainable Learning with Graph Based Data Assessing and Rule Reasoning
Learning an explainable classifier often results in low accuracy model or ends up with a huge rule set, while learning a deep model is usually more capable of handling noisy data at scale, but with the cost of hard to explain the result and weak at generalization.
Hyperbolic Graph Representation Learning: A Tutorial
We first give a brief introduction to graph representation learning as well as some preliminary Riemannian and hyperbolic geometry.
Transposed Variational Auto-encoder with Intrinsic Feature Learning for Traffic Forecasting
In this technical report, we present our solutions to the Traffic4cast 2022 core challenge and extended challenge.
Geometry Aligned Variational Transformer for Image-conditioned Layout Generation
First, self-attention mechanism is adopted to model the contextual relationship within layout elements, while cross-attention mechanism is used to fuse the visual information of conditional images.
Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics
To infer region-wise proximity under flexible factor-wise combinations and enable dynamic neighborhood aggregations, we further disentangle compounded influences of exogenous factors on region-wise proximity and learn to aggregate them.
HICF: Hyperbolic Informative Collaborative Filtering
The results reveal that (1) tail items get more emphasis in hyperbolic space than that in Euclidean space, but there is still ample room for improvement; (2) head items receive modest attention in hyperbolic space, which could be considerably improved; (3) and nonetheless, the hyperbolic models show more competitive performance than Euclidean models.
Boosting Factorization Machines via Saliency-Guided Mixup
Second, considering that MixFM may generate redundant or even detrimental instances, we further put forward a novel Factorization Machine powered by Saliency-guided Mixup (denoted as SMFM).
Learning-based AC-OPF Solvers on Realistic Network and Realistic Loads
Deep learning approaches for the Alternating Current-Optimal Power Flow (AC-OPF) problem are under active research in recent years.
Composition-aware Graphic Layout GAN for Visual-textual Presentation Designs
Through both quantitative and qualitative evaluations, we demonstrate that the proposed model can synthesize high-quality graphic layouts according to image compositions.
Discovering Representative Attribute-stars via Minimum Description Length
However, there are generally two limitations that hinder their practical use: (1) they have multiple parameters that are hard to set but greatly influence results, (2) and they generally focus on identifying complex subgraphs while ignoring relationships between attributes of nodes. Graphs are a popular data type found in many domains.
BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction
Regarding missing link inference of diverse networks, we revisit the link prediction techniques and identify the importance of both the structural and attribute information.
HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization
Hyperbolic space offers a spacious room to learn embeddings with its negative curvature and metric properties, which can well fit data with tree-like structures.
TeleGraph: A Benchmark Dataset for Hierarchical Link Prediction
Link prediction is a key problem for network-structured data, attracting considerable research efforts owing to its diverse applications.
Hyperbolic Graph Neural Networks: A Review of Methods and Applications
Graph neural networks generalize conventional neural networks to graph-structured data and have received widespread attention due to their impressive representation ability.
Enhancing Hyperbolic Graph Embeddings via Contrastive Learning
Inspired by the recently active and emerging self-supervised learning, in this study, we attempt to enhance the representation power of hyperbolic graph models by drawing upon the advantages of contrastive learning.
Boosting Image Outpainting with Semantic Layout Prediction
Secondly, another GAN model is trained to synthesize real images based on the extended semantic layouts.
Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space
To explore these properties of a complex temporal network, we propose a hyperbolic temporal graph network (HTGN) that fully takes advantage of the exponential capacity and hierarchical awareness of hyperbolic geometry.
Scaling Up Graph Neural Networks Via Graph Coarsening
Scalability of graph neural networks remains one of the major challenges in graph machine learning.
An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networks
Alarm root cause analysis is a significant component in the day-to-day telecommunication network maintenance, and it is critical for efficient and accurate fault localization and failure recovery.
Spatio-Temporal Hybrid Graph Convolutional Network for Traffic Forecasting in Telecommunication Networks
We conduct extensive experiments on real-world traffic datasets collected from telecommunication networks.
Imbalanced classification: a paradigm-based review
For each pair of resampling techniques and classification methods, we use simulation studies and a real data set on credit card fraud to study the performance under different evaluation metrics.
Progressive Feature Polishing Network for Salient Object Detection
Feature matters for salient object detection.
BOLT-SSI: A Statistical Approach to Screening Interaction Effects for Ultra-High Dimensional Data
In this paper, we first propose a simple method for sure screening interactions (SSI).
Methodology
