136 citations · 553 across the 42 of their papers we have counts for
24 papers · 1 filter
CARL-G: Clustering-Accelerated Representation Learning on Graphs
William Shiao, Uday Singh Saini, Yozen Liu +3
Self-supervised learning on graphs has made large strides in achieving great performance in various downstream tasks. However, many state-of-the-art methods suffer from a number of…
Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
Juanhui Li, Harry Shomer, Haitao Mao +5
Link prediction attempts to predict whether an unseen edge exists based on only a portion of edges of a graph. A flurry of methods have been introduced in recent years that attempt…
Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
Haitao Mao, Zhikai Chen, Wei Jin +5
Recent studies on Graph Neural Networks(GNNs) provide both empirical and theoretical evidence supporting their effectiveness in capturing structural patterns on both homophilic and…
A Practical, Progressively-Expressive GNN
Lingxiao Zhao, Louis Härtel, Neil Shah +1
Message passing neural networks (MPNNs) have become a dominant flavor of graph neural networks (GNNs) in recent years. Yet, MPNNs come with notable limitations; namely, they are at…
Link Prediction with Non-Contrastive Learning
William Shiao, Zhichun Guo, Tong Zhao +3
A recent focal area in the space of graph neural networks (GNNs) is graph self-supervised learning (SSL), which aims to derive useful node representations without labeled data. Not…
Linkless Link Prediction via Relational Distillation
Zhichun Guo, William Shiao, Shichang Zhang +4
Graph Neural Networks (GNNs) have shown exceptional performance in the task of link prediction. Despite their effectiveness, the high latency brought by non-trivial neighborhood da…
Empowering Graph Representation Learning with Test-Time Graph Transformation
Wei Jin, Tong Zhao, Jiayuan Ding +3
As powerful tools for representation learning on graphs, graph neural networks (GNNs) have facilitated various applications from drug discovery to recommender systems. Nevertheless…
Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
Mingxuan Ju, Tong Zhao, Qianlong Wen +4
Self-supervised learning (SSL) for graph neural networks (GNNs) has attracted increasing attention from the graph machine learning community in recent years, owing to its capabilit…
MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization
Xiaotian Han, Tong Zhao, Yozen Liu +2
Training graph neural networks (GNNs) on large graphs is complex and extremely time consuming. This is attributed to overheads caused by sparse matrix multiplication, which are sid…
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games
Shichang Zhang, Yozen Liu, Neil Shah +1
Explaining machine learning models is an important and increasingly popular area of research interest. The Shapley value from game theory has been proposed as a prime approach to c…
Imbalanced Graph Classification via Graph-of-Graph Neural Networks
Yu Wang, Yuying Zhao, Neil Shah +1
Graph Neural Networks (GNNs) have achieved unprecedented success in identifying categorical labels of graphs. However, most existing graph classification problems with GNNs follow…
Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation
Shichang Zhang, Yozen Liu, Yizhou Sun +1
Graph Neural Networks (GNNs) are popular for graph machine learning and have shown great results on wide node classification tasks. Yet, they are less popular for practical deploym…
Graph Condensation for Graph Neural Networks
Wei Jin, Lingxiao Zhao, Shichang Zhang +3
Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns. To alleviate the concerns, w…
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness
Lingxiao Zhao, Wei Jin, Leman Akoglu +1
Message Passing Neural Networks (MPNNs) are a common type of Graph Neural Network (GNN), in which each node's representation is computed recursively by aggregating representations…
Is Homophily a Necessity for Graph Neural Networks?
Yao Ma, Xiaorui Liu, Neil Shah +1
Graph neural networks (GNNs) have shown great prowess in learning representations suitable for numerous graph-based machine learning tasks. When applied to semi-supervised node cla…
Automated Self-Supervised Learning for Graphs
Wei Jin, Xiaorui Liu, Xiangyu Zhao +3
Graph self-supervised learning has gained increasing attention due to its capacity to learn expressive node representations. Many pretext tasks, or loss functions have been designe…
KNH: Multi-View Modeling with K-Nearest Hyperplanes Graph for Misinformation Detection
Sara Abdali, Neil Shah, Evangelos E. Papalexakis
Graphs are one of the most efficacious structures for representing datapoints and their relations, and they have been largely exploited for different applications. Previously, the…
Identifying Misinformation from Website Screenshots
Sara Abdali, Rutuja Gurav, Siddharth Menon +4
Can the look and the feel of a website give information about the trustworthiness of an article? In this paper, we propose to use a promising, yet neglected aspect in detecting the…
FairOD: Fairness-aware Outlier Detection
Shubhranshu Shekhar, Neil Shah, Leman Akoglu
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. However, when being a minority (as de…
Action Sequence Augmentation for Early Graph-based Anomaly Detection
Tong Zhao, Bo Ni, Wenhao Yu +3
The proliferation of web platforms has created incentives for online abuse. Many graph-based anomaly detection techniques are proposed to identify the suspicious accounts and behav…
A Unified View on Graph Neural Networks as Graph Signal Denoising
Yao Ma, Xiaorui Liu, Tong Zhao +3
Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data. A single GNN layer typically consists of a feature transformation and a…
Data Augmentation for Graph Neural Networks
Tong Zhao, Yozen Liu, Leonardo Neves +3
Data augmentation has been widely used to improve generalizability of machine learning models. However, comparatively little work studies data augmentation for graphs. This is larg…
Semi-supervised Content-based Detection of Misinformation via Tensor Embeddings
Gisel Bastidas Guacho, Sara Abdali, Neil Shah +1
Fake news may be intentionally created to promote economic, political and social interests, and can lead to negative impacts on humans beliefs and decisions. Hence, detection of fa…
Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective
Neil Shah, Alex Beutel, Brian Gallagher +1
How can we detect suspicious users in large online networks? Online popularity of a user or product (via follows, page-likes, etc.) can be monetized on the premise of higher ad cli…