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09/16/2023
S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs
The traditional Dialogue State Tracking (DST) problem aims to track user...
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08/12/2023
Stationary Algorithmic Balancing For Dynamic Email Re-Ranking Problem
Email platforms need to generate personalized rankings of emails that sa...
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08/01/2023
DYMOND: DYnamic MOtif-NoDes Network Generative Model
Motifs, which have been established as building blocks for network struc...
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04/24/2023
Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness
Node representation learning in a network is an important machine learni...
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02/17/2023
Creating generalizable downstream graph models with random projections
We investigate graph representation learning approaches that enable mode...
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10/27/2022
Federated Graph Representation Learning using Self-Supervision
Federated graph representation learning (FedGRL) brings the benefits of ...
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02/04/2022
Lightweight Compositional Embeddings for Incremental Streaming Recommendation
Most work in graph-based recommender systems considers a static setting ...
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06/11/2021
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
Graph neural networks (GNNs) have achieved tremendous success on multipl...
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06/10/2021
Adversarial Graph Augmentation to Improve Graph Contrastive Learning
Self-supervised learning of graph neural networks (GNN) is in great need...
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03/06/2021
ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis
Analyzing the readability of articles has been an important sociolinguis...
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09/22/2020
A Hybrid Model for Learning Embeddings and Logical Rules Simultaneously from Knowledge Graphs
The problem of knowledge graph (KG) reasoning has been widely explored b...
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03/26/2020
A Collective Learning Framework to Boost GNN Expressiveness
Graph Neural Networks (GNNs) have recently been used for node and graph ...
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03/02/2020
Cluster-Based Social Reinforcement Learning
Social Reinforcement Learning methods, which model agents in large netwo...
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10/01/2019
Deep Lifetime Clustering
The goal of lifetime clustering is to develop an inductive model that ma...
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04/04/2019
Community detection over a heterogeneous population of non-aligned networks
Clustering and community detection with multiple graphs have typically f...
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09/07/2018
Multi-level hypothesis testing for populations of heterogeneous networks
In this work, we consider hypothesis testing and anomaly detection on da...
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07/24/2017
Stochastic Gradient Descent for Relational Logistic Regression via Partial Network Crawls
Research in statistical relational learning has produced a number of met...
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07/01/2016
Combining Gradient Boosting Machines with Collective Inference to Predict Continuous Values
Gradient boosting of regression trees is a competitive procedure for lea...
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06/13/2015
Graphlet Decomposition: Framework, Algorithms, and Applications
From social science to biology, numerous applications often rely on grap...
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03/14/2014
Learning the Latent State Space of Time-Varying Graphs
From social networks to Internet applications, a wide variety of electro...
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11/14/2012
Network Sampling: From Static to Streaming Graphs
Network sampling is integral to the analysis of social, information, and...
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05/09/2012
Dynamic Behavioral Mixed-Membership Model for Large Evolving Networks
The majority of real-world networks are dynamic and extremely large (e.g...
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03/30/2012
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important to...
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03/09/2012
Role-Dynamics: Fast Mining of Large Dynamic Networks
To understand the structural dynamics of a large-scale social, biologica...
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11/22/2011
