
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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Adversarial Graph Augmentation to Improve Graph Contrastive Learning
Selfsupervised learning of graph neural networks (GNN) is in great need...
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ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis
Analyzing the readability of articles has been an important sociolinguis...
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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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A Collective Learning Framework to Boost GNN Expressiveness
Graph Neural Networks (GNNs) have recently been used for node and graph ...
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ClusterBased Social Reinforcement Learning
Social Reinforcement Learning methods, which model agents in large netwo...
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Deep Lifetime Clustering
The goal of lifetime clustering is to develop an inductive model that ma...
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Community detection over a heterogeneous population of nonaligned networks
Clustering and community detection with multiple graphs have typically f...
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Multilevel hypothesis testing for populations of heterogeneous networks
In this work, we consider hypothesis testing and anomaly detection on da...
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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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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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Graphlet Decomposition: Framework, Algorithms, and Applications
From social science to biology, numerous applications often rely on grap...
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Learning the Latent State Space of TimeVarying Graphs
From social networks to Internet applications, a wide variety of electro...
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Network Sampling: From Static to Streaming Graphs
Network sampling is integral to the analysis of social, information, and...
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Dynamic Behavioral MixedMembership Model for Large Evolving Networks
The majority of realworld networks are dynamic and extremely large (e.g...
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Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important to...
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RoleDynamics: Fast Mining of Large Dynamic Networks
To understand the structural dynamics of a largescale social, biologica...
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Representations and Ensemble Methods for Dynamic Relational Classification
Temporal networks are ubiquitous and evolve over time by the addition, d...
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Jennifer Neville
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