136 citations · 553 across the 42 of their papers we have counts for
46 papers
Cluster Validation Indices as Self-Supervised Objectives for Text Representation Learning
Kishor Kumar Bhaumik, Nicolas Roque dos Santos, Neil Shah +2
Self-supervised fine-tuning refines the embedding space of a pretrained language encoder without labels. However, the commonly used approaches are computationally expensive. Specif…
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…
Flashlight: Scalable Link Prediction with Effective Decoders
Yiwei Wang, Bryan Hooi, Yozen Liu +3
Link prediction (LP) has been recognized as an important task in graph learning with its broad practical applications. A typical application of LP is to retrieve the top scoring ne…
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?
Juanhui Li, Harry Shomer, Jiayuan Ding +5
Knowledge graphs (KGs) facilitate a wide variety of applications. Despite great efforts in creation and maintenance, even the largest KGs are far from complete. Hence, KG completio…
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…
Attributed Graph Modeling with Vertex Replacement Grammars
Satyaki Sikdar, Neil Shah, Tim Weninger
Recent work at the intersection of formal language theory and graph theory has explored graph grammars for graph modeling. However, existing models and formalisms can only operate…
Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs
Hyeonjeong Shin, Taehyung Kwon, Neil Shah +1
A variety of tasks on dynamic graphs, including anomaly detection, community detection, compression, and graph understanding, have been formulated as problems of identifying consti…
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…
Sketch-Based Anomaly Detection in Streaming Graphs
Siddharth Bhatia, Mohit Wadhwa, Kenji Kawaguchi +3
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges and subgraphs in an online manner, for the purpose of detecting unusual behavior, usin…
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…