142 citations · 458 across the 31 of their papers we have counts for
34 papers
Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies
Chirag Shah, Ryen W. White, Reid Andersen +13
Log data can reveal valuable information about how users interact with Web search services, what they want, and how satisfied they are. However, analyzing user intents in log data…
Stationary Algorithmic Balancing For Dynamic Email Re-Ranking Problem
Jiayi Liu, Jennifer Neville
Email platforms need to generate personalized rankings of emails that satisfy user preferences, which may vary over time. We approach this as a recommendation problem based on thre…
DYMOND: DYnamic MOtif-NoDes Network Generative Model
Giselle Zeno, Timothy La Fond, Jennifer Neville
Motifs, which have been established as building blocks for network structure, move beyond pair-wise connections to capture longer-range correlations in connections and activity. In…
Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness
Hogun Park, Jennifer Neville
Node representation learning in a network is an important machine learning technique for encoding relational information in a continuous vector space while preserving the inherent…
Creating generalizable downstream graph models with random projections
Anton Amirov, Chris Quirk, Jennifer Neville
We investigate graph representation learning approaches that enable models to generalize across graphs: given a model trained using the representations from one graph, our goal is…
Federated Graph Representation Learning using Self-Supervision
Susheel Suresh, Danny Godbout, Arko Mukherjee +3
Federated graph representation learning (FedGRL) brings the benefits of distributed training to graph structured data while simultaneously addressing some privacy and compliance co…
Lightweight Compositional Embeddings for Incremental Streaming Recommendation
Mengyue Hang, Tobias Schnabel, Longqi Yang +1
Most work in graph-based recommender systems considers a {\em static} setting where all information about test nodes (i.e., users and items) is available upfront at training time.…
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
Susheel Suresh, Vinith Budde, Jennifer Neville +2
Graph neural networks (GNNs) have achieved tremendous success on multiple graph-based learning tasks by fusing network structure and node features. Modern GNN models are built upon…
Adversarial Graph Augmentation to Improve Graph Contrastive Learning
Susheel Suresh, Pan Li, Cong Hao +1
Self-supervised learning of graph neural networks (GNN) is in great need because of the widespread label scarcity issue in real-world graph/network data. Graph contrastive learning…
A Hybrid Model for Learning Embeddings and Logical Rules Simultaneously from Knowledge Graphs
Susheel Suresh, Jennifer Neville
The problem of knowledge graph (KG) reasoning has been widely explored by traditional rule-based systems and more recently by knowledge graph embedding methods. While logical rules…
Cluster-Based Social Reinforcement Learning
Mahak Goindani, Jennifer Neville
Social Reinforcement Learning methods, which model agents in large networks, are useful for fake news mitigation, personalized teaching/healthcare, and viral marketing, but it is c…
A Collective Learning Framework to Boost GNN Expressiveness
Mengyue Hang, Jennifer Neville, Bruno Ribeiro
Graph Neural Networks (GNNs) have recently been used for node and graph classification tasks with great success, but GNNs model dependencies among the attributes of nearby neighbor…
Deep Lifetime Clustering
S Chandra Mouli, Leonardo Teixeira, Jennifer Neville +1
The goal of lifetime clustering is to develop an inductive model that maps subjects into clusters according to their underlying (unobserved) lifetime distribution. We introduce…
Community detection over a heterogeneous population of non-aligned networks
Guilherme Gomes, Vinayak Rao, Jennifer Neville
Clustering and community detection with multiple graphs have typically focused on aligned graphs, where there is a mapping between nodes across the graphs (e.g., multi-view, multi-…
Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction
Mengyue Hang, Ian Pytlarz, Jennifer Neville
With the availability of vast amounts of user visitation history on location-based social networks (LBSN), the problem of Point-of-Interest (POI) prediction has been extensively st…
Multi-level hypothesis testing for populations of heterogeneous networks
Guilherme Gomes, Vinayak Rao, Jennifer Neville
In this work, we consider hypothesis testing and anomaly detection on datasets where each observation is a weighted network. Examples of such data include brain connectivity networ…
Stochastic Gradient Descent for Relational Logistic Regression via Partial Network Crawls
Jiasen Yang, Bruno Ribeiro, Jennifer Neville
Research in statistical relational learning has produced a number of methods for learning relational models from large-scale network data. While these methods have been successfull…
Identifying User Survival Types via Clustering of Censored Social Network Data
S Chandra Mouli, Abhishek Naik, Bruno Ribeiro +1
The goal of cluster analysis in survival data is to identify clusters that are decidedly associated with the survival outcome. Previous research has explored this problem primarily…
Size-Consistent Statistics for Anomaly Detection in Dynamic Networks
Timothy La Fond, Jennifer Neville, Brian Gallagher
An important task in network analysis is the detection of anomalous events in a network time series. These events could merely be times of interest in the network timeline or they…
Combining Gradient Boosting Machines with Collective Inference to Predict Continuous Values
Iman Alodah, Jennifer Neville
Gradient boosting of regression trees is a competitive procedure for learning predictive models of continuous data that fits the data with an additive non-parametric model. The cla…
Using Bayesian Network Representations for Effective Sampling from Generative Network Models
Pablo Robles-Granda, Sebastian Moreno, Jennifer Neville
Bayesian networks (BNs) are used for inference and sampling by exploiting conditional independence among random variables. Context specific independence (CSI) is a property of grap…
Graphlet Decomposition: Framework, Algorithms, and Applications
Nesreen K. Ahmed, Jennifer Neville, Ryan A. Rossi +2
From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks at both the global macro-level as well as th…
Anomaly Detection in Dynamic Networks of Varying Size
Timothy La Fond, Jennifer Neville, Brian Gallagher
Dynamic networks, also called network streams, are an important data representation that applies to many real-world domains. Many sets of network data such as e-mail networks, soci…
Graph Sample and Hold: A Framework for Big-Graph Analytics
Nesreen K. Ahmed, Nick Duffield, Jennifer Neville +1
Sampling is a standard approach in big-graph analytics; the goal is to efficiently estimate the graph properties by consulting a sample of the whole population. A perfect sample is…