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Found 10 papers, 6 papers with code
A Systematic Evaluation of Node Embedding Robustness
We find that node classification results are impacted more than network reconstruction ones, that degree-based and label-based attacks are on average the most damaging and that label heterophily can strongly influence attack performance.
CSNE: Conditional Signed Network Embedding
Experiments on a variety of real-world networks confirm that CSNE outperforms the state-of-the-art on the task of sign prediction.
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?
Network embedding methods map a network's nodes to vectors in an embedding space, in such a way that these representations are useful for estimating some notion of similarity or proximity between pairs of nodes in the network.
Block-Approximated Exponential Random Graphs
An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs.
Semi-supervised Learning in Network-Structured Data via Total Variation Minimization
This lends naturally to learning the labels by total variation (TV) minimization, which we solve by applying a recently proposed primal-dual method for non-smooth convex optimization.
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction
In this paper we present EvalNE, a Python toolbox for evaluating network embedding methods on link prediction tasks.
Social and Information Networks
Recovery Conditions and Sampling Strategies for Network Lasso
By generalizing the concept of the compatibility condition put forward by van de Geer and Buehlmann as a powerful tool for the analysis of plain Lasso, we derive a sufficient condition, i. e., the network compatibility condition, on the underlying network topology such that network Lasso accurately learns a clustered underlying graph signal.
When is Network Lasso Accurate?
By leveraging concepts of compressed sensing, we address this gap and derive precise conditions on the underlying network topology and sampling set which guarantee the network Lasso for a particular loss function to deliver an accurate estimate of the entire underlying graph signal.
Semi-Supervised Learning via Sparse Label Propagation
This learning algorithm allows for a highly scalable implementation as message passing over the underlying data graph.
Scalable Semi-Supervised Learning over Networks using Nonsmooth Convex Optimization
We propose a scalable method for semi-supervised (transductive) learning from massive network-structured datasets.
