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Found 19 papers, 7 papers with code
Graph Neural Networks for Treatment Effect Prediction
Estimating causal effects in e-commerce tends to involve costly treatment assignments which can be impractical in large-scale settings.
On Quantified Observability Analysis in Multiagent Systems
In multiagent systems (MASs), agents' observation upon system behaviours may improve the overall team performance, but may also leak sensitive information to an observer.
Ownership Protection of Generative Adversarial Networks
Generative adversarial networks (GANs) have shown remarkable success in image synthesis, making GAN models themselves commercially valuable to legitimate model owners.
PriSampler: Mitigating Property Inference of Diffusion Models
In this work, we systematically present the first privacy study about property inference attacks against diffusion models, in which adversaries aim to extract sensitive global properties of the training set from a diffusion model, such as the proportion of the training data for certain sensitive properties.
Membership Inference of Diffusion Models
Recent years have witnessed the tremendous success of diffusion models in data synthesis.
"Double vaccinated, 5G boosted!": Learning Attitudes towards COVID-19 Vaccination from Social Media
To address the vaccine hesitancy which impairs the efforts of the COVID-19 vaccination campaign, it is imperative to understand public vaccination attitudes and timely grasp their changes.
A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter
Vaccine hesitancy is considered as one main cause of the stagnant uptake ratio of COVID-19 vaccines in Europe and the US where vaccines are sufficiently supplied.
Simplifying Node Classification on Heterophilous Graphs with Compatible Label Propagation
Graph Neural Networks (GNNs) have been predominant for graph learning tasks; however, recent studies showed that a well-known graph algorithm, Label Propagation (LP), combined with a shallow neural network can achieve comparable performance to GNNs in semi-supervised node classification on graphs with high homophily.
Unsupervised Network Embedding Beyond Homophily
Here, we formulate the unsupervised NE task as an r-ego network discrimination problem and develop the SELENE framework for learning on networks with homophily and heterophily.
Hilbert Sinkhorn Divergence for Optimal Transport
Sinkhorn divergence has become a very popular metric to compare probability distributions in optimal transport.
Model Extraction and Defenses on Generative Adversarial Networks
Then we study model extraction attacks against GANs from the perspective of accuracy extraction and fidelity extraction, according to the adversary's goals and background knowledge.
From #Jobsearch to #Mask: Improving COVID-19 Cascade Prediction with Spillover Effects
Experiments conducted on our dataset demonstrated that the use of the identified spillover effect significantly improves the state-of-the-art GNNs methods in predicting the popularity of not only preventive measure messages, but also other COVID-19 related messages.
Social and Information Networks Computers and Society
Semantic and Relational Spaces in Science of Science: Deep Learning Models for Article Vectorisation
Our results show that using NLP we can encode a semantic space of articles, while with GNN we are able to build a relational space where the social practices of a research community are also encoded.
Personalised Meta-path Generation for Heterogeneous GNNs
To fully unleash the power of HGRL, we present a novel framework, Personalised Meta-path based Heterogeneous Graph Neural Networks (PM-HGNN), to jointly generate meta-paths that are personalised for each individual node in a HIN and learn node representations for the target downstream task like node classification.
Multi-grained Semantics-aware Graph Neural Networks
Specifically, a differentiable pooling operator is proposed to adaptively generate a multi-grained structure that involves meso- and macro-level semantic information in the graph.
Hierarchical Message-Passing Graph Neural Networks
To deal with these two issues, we propose a novel Hierarchical Message-passing Graph Neural Networks framework.
An Exploratory Study of COVID-19 Information on Twitter in the Greater Region
The outbreak of the COVID-19 leads to a burst of information in major online social networks (OSNs).
A Dynamics-based Approach for the Target Control of Boolean Networks
We study the target control problem of asynchronous Boolean networks, to identify a set of nodes, the perturbation of which can drive the dynamics of the network from any initial state to the desired steady state (or attractor).
Fast Simulation of Probabilistic Boolean Networks (Technical Report)
Probabilistic Boolean networks (PBNs) is an important mathematical framework widely used for modelling and analysing biological systems.
