Search Results for author:
Found 32 papers, 12 papers with code
STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases
Answering real-world user queries, such as product search, often requires accurate retrieval of information from semi-structured knowledge bases or databases that involve blend of unstructured (e. g., textual descriptions of products) and structured (e. g., entity relations of products) information.
NetInfoF Framework: Measuring and Exploiting Network Usable Information
Given a node-attributed graph, and a graph task (link prediction or node classification), can we tell if a graph neural network (GNN) will perform well?
BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs
Foundation models (FMs) are able to leverage large volumes of unlabeled data to demonstrate superior performance across a wide range of tasks.
TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning
How can we enhance the node features acquired from Pretrained Models (PMs) to better suit downstream graph learning tasks?
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain.
Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices
While Graph Neural Networks (GNNs) are remarkably successful in a variety of high-impact applications, we demonstrate that, in link prediction, the common practices of including the edges being predicted in the graph at training and/or test have outsized impact on the performance of low-degree nodes.
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
Different from conventional knowledge distillation, GRAD jointly optimizes a GNN teacher and a graph-free student over the graph's nodes via a shared LM.
OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization
Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from widespread applications.
Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information
Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics.
Variational Causal Inference
Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e. g. gene expressions, impulse responses, human faces) and covariates are relatively limited.
Efficient and effective training of language and graph neural network models
The effectiveness in our framework is achieved by applying stage-wise fine-tuning of the BERT model first with heterogenous graph information and then with a GNN model.
A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features
Graph Neural Networks (GNNs) with numerical node features and graph structure as inputs have demonstrated superior performance on various supervised learning tasks with graph data.
TempoQR: Temporal Question Reasoning over Knowledge Graphs
The first computes a textual representation of a given question, the second combines it with the entity embeddings for entities involved in the question, and the third generates question-specific time embeddings.
Ranked #1 on
Question Answering
on CronQuestions
Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features
For supervised learning with tabular data, decision tree ensembles produced via boosting techniques generally dominate real-world applications involving iid training/test sets.
Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation
Consistency training is a popular method to improve deep learning models in computer vision and natural language processing.
Convergent Boosted Smoothing for Modeling GraphData with Tabular Node Features
Many practical modeling tasks require making predictions using tabular data composed of heterogeneous feature types (e. g., text-based, categorical, continuous, etc.).
Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks
Uncovering anomalies in attributed networks has recently gained popularity due to its importance in unveiling outliers and flagging adversarial behavior in a gamut of data and network science applications including {the Internet of Things (IoT)}, finance, security, to list a few.
PanRep: Universal node embeddings for heterogeneous graphs
Learning unsupervised node embeddings facilitates several downstream tasks such as node classification and link prediction.
COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature
Finally, we propose a document similarity engine that leverages low-dimensional graph embeddings from the CKG with semantic embeddings for similar article retrieval.
PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphs
Learning unsupervised node embeddings facilitates several downstream tasks such as node classification and link prediction.
Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing
This paper proposes an inductive RGCN for learning informative relation embeddings even in the few-shot learning regime.
Pruned Graph Scattering Transforms
Graph convolutional networks (GCNs) have achieved remarkable performance in a variety of network science learning tasks.
Tensor Graph Convolutional Networks for Multi-relational and Robust Learning
The era of "data deluge" has sparked renewed interest in graph-based learning methods and their widespread applications ranging from sociology and biology to transportation and communications.
Efficient and Stable Graph Scattering Transforms via Pruning
The resultant pruning algorithm is guided by a graph-spectrum-inspired criterion, and retains informative scattering features on-the-fly while bypassing the exponential complexity associated with GSTs.
Edge Dithering for Robust Adaptive Graph Convolutional Networks
Graph convolutional networks (GCNs) are vulnerable to perturbations of the graph structure that are either random, or, adversarially designed.
GraphSAC: Detecting anomalies in large-scale graphs
Alleviating this limitation, GraphSAC randomly draws subsets of nodes, and relies on graph-aware criteria to judiciously filter out sets contaminated by anomalous nodes, before employing a semi-supervised learning (SSL) module to estimate nominal label distributions per node.
A Recurrent Graph Neural Network for Multi-Relational Data
The era of data deluge has sparked the interest in graph-based learning methods in a number of disciplines such as sociology, biology, neuroscience, or engineering.
Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection
The resulting community detection approach is successful even when some links in the graphs are missing.
Semi-Blind Inference of Topologies and Dynamical Processes over Graphs
Alleviating the limited flexibility of existing approaches, this work advocates structural models for graph processes and develops novel algorithms for joint inference of the network topology and processes from partial nodal observations.
Kernel-based Inference of Functions over Graphs
The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced.
Inference of Spatio-Temporal Functions over Graphs via Multi-Kernel Kriged Kalman Filtering
Inference of space-time varying signals on graphs emerges naturally in a plethora of network science related applications.
Kernel-based Reconstruction of Space-time Functions on Dynamic Graphs
Graph-based methods pervade the inference toolkits of numerous disciplines including sociology, biology, neuroscience, physics, chemistry, and engineering.
