
Unsupervised Joint knode Graph Representations with Compositional EnergyBased Models
Existing Graph Neural Network (GNN) methods that learn inductive unsuper...
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Deceptive Deletions for Protecting Withdrawn Posts on Social Platforms
Oversharing poorlyworded thoughts and personal information is prevalen...
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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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Membership Inference Attacks and Defenses in Supervised Learning via Generalization Gap
This work studies membership inference (MI) attack against classifiers, ...
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Infinity Learning: Learning Markov Chains from Aggregate SteadyState Observations
We consider the task of learning a parametric Continuous Time Markov Cha...
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Deep Lifetime Clustering
The goal of lifetime clustering is to develop an inductive model that ma...
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On the Equivalence between Node Embeddings and Structural Graph Representations
This work provides the first unifying theoretical framework for node emb...
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Are Graph Neural Networks Miscalibrated?
Graph Neural Networks (GNNs) have proven to be successful in many classi...
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Relational Pooling for Graph Representations
This work generalizes graph neural networks (GNNs) beyond those based on...
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Janossy Pooling: Learning Deep PermutationInvariant Functions for VariableSize Inputs
We consider a simple and overarching representation for permutationinva...
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Feedforward Neural Networks for Caching: Enough or Too Much?
We propose a caching policy that uses a feedforward neural network (FNN)...
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Graph Pattern Mining and Learning through Userdefined Relations (Extended Version)
In this work we propose RGPM, a parallel computing framework for graph ...
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From Monte Carlo to Las Vegas: Improving Restricted Boltzmann Machine Training Through Stopping Sets
We propose a Las Vegas transformation of Markov Chain Monte Carlo (MCMC)...
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SBGSketch: A SelfBalanced Sketch for LabeledGraph Stream Summarization
Applications in various domains rely on processing graph streams, e.g., ...
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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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Selective Harvesting over Networks
Active search (AS) on graphs focuses on collecting certain labeled nodes...
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TribeFlow: Mining & Predicting User Trajectories
Which song will Smith listen to next? Which restaurant will Alice go to ...
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Bayesian Inference of Online Social Network Statistics via Lightweight Random Walk Crawls
Online social networks (OSN) contain extensive amount of information abo...
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Bruno Ribeiro
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