Learning Network Representations
3 papers with code • 0 benchmarks • 0 datasets
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Latest papers with no code
Learning Network Representations with Disentangled Graph Auto-Encoder
Learning disentangled graph representations with (variational) graph auto-encoder poses significant challenges, and remains largely unexplored in the existing literature.
Privacy-Preserving Representation Learning for Text-Attributed Networks with Simplicial Complexes
Finally, I will study a privacy-preserving deterministic differentially private alternating direction method of multiplier to learn secure representation outputs from SNNs that capture multi-scale relationships and facilitate the passage from local structure to global invariant features on text-attributed networks.
Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data
Finally, we demonstrate the applicability of lsNGC to estimating causality in large, real-world systems by inferring directional nonlinear, causal relationships among a large number of relatively short time series acquired from functional Magnetic Resonance Imaging (fMRI) data of the human brain.
Global and Local Feature Learning for Ego-Network Analysis
This social network can be efficiently analyzed after learning representations of the ego and its alters in a low-dimensional, real vector space.
Dynamic Joint Variational Graph Autoencoders
Learning network representations is a fundamental task for many graph applications such as link prediction, node classification, graph clustering, and graph visualization.
Learning Features of Network Structures Using Graphlets
In this paper, we explore the role of \emph{graphlets} in network classification for both static and temporal networks.
