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Found 51 papers, 28 papers with code
How Does Message Passing Improve Collaborative Filtering?
A branch of research enhances CF methods by message passing used in graph neural networks, due to its strong capabilities of extracting knowledge from graph-structured data, like user-item bipartite graphs that naturally exist in CF.
Improving Out-of-Vocabulary Handling in Recommendation Systems
This work focuses on a complementary problem: recommending new users and items unseen (out-of-vocabulary, or OOV) at training time.
Node Duplication Improves Cold-start Link Prediction
Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown state-of-the-art performance in Link Prediction (LP) tasks.
LLaGA: Large Language and Graph Assistant
Graph Neural Networks (GNNs) have empowered the advance in graph-structured data analysis.
Neural Scaling Laws on Graphs
In this work, we delve into neural scaling laws on graphs from both model and data perspectives.
Graph Foundation Models
Graph Foundation Model (GFM) is a new trending research topic in the graph domain, aiming to develop a graph model capable of generalizing across different graphs and tasks.
Graph Transformers for Large Graphs
As such, a key innovation of this work lies in the creation of a fast neighborhood sampling technique coupled with a local attention mechanism that encompasses a 4-hop reception field, but achieved through just 2-hop operations.
A Topological Perspective on Demystifying GNN-Based Link Prediction Performance
Despite the widespread belief that low-degree nodes exhibit poorer LP performance, our empirical findings provide nuances to this viewpoint and prompt us to propose a better metric, Topological Concentration (TC), based on the intersection of the local subgraph of each node with the ones of its neighbors.
Revisiting Link Prediction: A Data Perspective
We recognize three fundamental factors critical to link prediction: local structural proximity, global structural proximity, and feature proximity.
RobustL2S: Speaker-Specific Lip-to-Speech Synthesis exploiting Self-Supervised Representations
Significant progress has been made in speaker dependent Lip-to-Speech synthesis, which aims to generate speech from silent videos of talking faces.
Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
Furthermore, new and diverse datasets have also been created to better evaluate the effectiveness of these new models.
CARL-G: Clustering-Accelerated Representation Learning on Graphs
CARL-G is adaptable to different clustering methods and CVIs, and we show that with the right choice of clustering method and CVI, CARL-G outperforms node classification baselines on 4/5 datasets with up to a 79x training speedup compared to the best-performing baseline.
Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
Recent studies on Graph Neural Networks(GNNs) provide both empirical and theoretical evidence supporting their effectiveness in capturing structural patterns on both homophilic and certain heterophilic graphs.
MParrotTTS: Multilingual Multi-speaker Text to Speech Synthesis in Low Resource Setting
We present MParrotTTS, a unified multilingual, multi-speaker text-to-speech (TTS) synthesis model that can produce high-quality speech.
ParrotTTS: Text-to-Speech synthesis by exploiting self-supervised representations
We present ParrotTTS, a modularized text-to-speech synthesis model leveraging disentangled self-supervised speech representations.
Link Prediction with Non-Contrastive Learning
In this work, we extensively evaluate the performance of existing non-contrastive methods for link prediction in both transductive and inductive settings.
A Practical, Progressively-Expressive GNN
Our model is practical and progressively-expressive, increasing in power with k and c. We demonstrate effectiveness on several benchmark datasets, achieving several state-of-the-art results with runtime and memory usage applicable to practical graphs.
Forget Unlearning: Towards True Data-Deletion in Machine Learning
Unlearning algorithms aim to remove deleted data's influence from trained models at a cost lower than full retraining.
Linkless Link Prediction via Relational Distillation
In this work, to combine the advantages of GNNs and MLPs, we start with exploring direct knowledge distillation (KD) methods for link prediction, i. e., predicted logit-based matching and node representation-based matching.
Empowering Graph Representation Learning with Test-Time Graph Transformation
In this work, we provide a data-centric view to tackle these issues and propose a graph transformation framework named GTrans which adapts and refines graph data at test time to achieve better performance.
Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
Besides, we observe that learning from multiple philosophies enhances not only the task generalization but also the single task performances, demonstrating that PARETOGNN achieves better task generalization via the disjoint yet complementary knowledge learned from different philosophies.
MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization
Training graph neural networks (GNNs) on large graphs is complex and extremely time consuming.
Flashlight: Scalable Link Prediction with Effective Decoders
However, HadamardMLP lacks the scalability for retrieving top scoring neighbors on large graphs, since to the best of our knowledge, there does not exist an algorithm to retrieve the top scoring neighbors for HadamardMLP decoders in sublinear complexity.
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?
This suggests a conflation of scoring function design, loss function design, and MP in prior work, with promising insights regarding the scalability of state-of-the-art KGC methods today, as well as careful attention to more suitable MP designs for KGC tasks tomorrow.
Graph Data Augmentation for Graph Machine Learning: A Survey
Overall, our work aims to clarify the landscape of existing literature in graph data augmentation and motivates additional work in this area, providing a helpful resource for researchers and practitioners in the broader graph machine learning domain.
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games
Explaining machine learning models is an important and increasingly popular area of research interest.
Imbalanced Graph Classification via Graph-of-Graph Neural Networks
To this end, we introduce a novel framework, Graph-of-Graph Neural Networks (G$^2$GNN), which alleviates the graph imbalance issue by deriving extra supervision globally from neighboring graphs and locally from stochastic augmentations of graphs.
Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation
Conversely, multi-layer perceptrons (MLPs) have no graph dependency and infer much faster than GNNs, even though they are less accurate than GNNs for node classification in general.
Ranked #3 on
Node Classification
on AMZ Computers
Graph Condensation for Graph Neural Networks
Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns.
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness
We choose the subgraph encoder to be a GNN (mainly MPNNs, considering scalability) to design a general framework that serves as a wrapper to up-lift any GNN.
Ranked #16 on
Graph Property Prediction
on ogbg-molpcba
Is Homophily a Necessity for Graph Neural Networks?
We find that this claim is not quite true, and in fact, GCNs can achieve strong performance on heterophilous graphs under certain conditions.
Automated Self-Supervised Learning for Graphs
Then we propose the AutoSSL framework which can automatically search over combinations of various self-supervised tasks.
Sketch-Based Anomaly Detection in Streaming Graphs
This higher-order sketch has the useful property of preserving the dense subgraph structure (dense subgraphs in the input turn into dense submatrices in the data structure).
KNH: Multi-View Modeling with K-Nearest Hyperplanes Graph for Misinformation Detection
In this work, we introduce a novel generalization of graphs i. e., K-Nearest Hyperplanes graph (KNH) where the nodes are defined by higher order Euclidean subspaces for multi-view modeling of the nodes.
Identifying Misinformation from Website Screenshots
To capture this overall look, we take screenshots of news articles served by either misinformative or trustworthy web domains and leverage a tensor decomposition based semi-supervised classification technique.
FairOD: Fairness-aware Outlier Detection
Fairness and Outlier Detection (OD) are closely related, as it is exactly the goal of OD to spot rare, minority samples in a given population.
The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks
Contextual embeddings derived from transformer-based neural language models have shown state-of-the-art performance for various tasks such as question answering, sentiment analysis, and textual similarity in recent years.
Action Sequence Augmentation for Early Graph-based Anomaly Detection
With Eland, anomaly detection performance at an earlier stage is better than non-augmented methods that need significantly more observed data by up to 15% on the Area under the ROC curve.
A Unified View on Graph Neural Networks as Graph Signal Denoising
In this work, we establish mathematically that the aggregation processes in a group of representative GNN models including GCN, GAT, PPNP, and APPNP can be regarded as (approximately) solving a graph denoising problem with a smoothness assumption.
Data Augmentation for Graph Neural Networks
Our work shows that neural edge predictors can effectively encode class-homophilic structure to promote intra-class edges and demote inter-class edges in given graph structure, and our main contribution introduces the GAug graph data augmentation framework, which leverages these insights to improve performance in GNN-based node classification via edge prediction.
Ranked #1 on
Node Classification
on Flickr
Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps
In this paper, we study a novel problem of explainable user engagement prediction for social network Apps.
Semi-Supervised Multi-aspect Detection of Misinformation using Hierarchical Joint Decomposition
Distinguishing between misinformation and real information is one of the most challenging problems in today's interconnected world.
Minority Reports Defense: Defending Against Adversarial Patches
We propose a defense against patch attacks based on partially occluding the image around each candidate patch location, so that a few occlusions each completely hide the patch.
SliceNDice: Mining Suspicious Multi-attribute Entity Groups with Multi-view Graphs
Given the reach of web platforms, bad actors have considerable incentives to manipulate and defraud users at the expense of platform integrity.
Did We Get It Right? Predicting Query Performance in E-commerce Search
In this paper, we address the problem of evaluating whether results served by an e-commerce search engine for a query are good or not.
Semi-supervised Content-based Detection of Misinformation via Tensor Embeddings
Most existing works on this topic focus on manual feature extraction and supervised classification models leveraging a large number of labeled (fake or real) articles.
False Information on Web and Social Media: A Survey
False information can be created and spread easily through the web and social media platforms, resulting in widespread real-world impact.
The Many Faces of Link Fraud
Most past work on social network link fraud detection tries to separate genuine users from fraudsters, implicitly assuming that there is only one type of fraudulent behavior.
FLOCK: Combating Astroturfing on Livestreaming Platforms
Livestreaming platforms have become increasingly popular in recent years as a means of sharing and advertising creative content.
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection
To combine these 2 approaches, we formulate our Bayesian Inference for Rating Data (BIRD) model, a flexible Bayesian model of user rating behavior.
Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective
How can we detect suspicious users in large online networks?
