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Found 9 papers, 7 papers with code
DynWalks: Global Topology and Recent Changes Awareness Dynamic Network Embedding
Dynamic network embedding aims to learn low dimensional embeddings for unseen and seen nodes by using any currently available snapshots of a dynamic network.
GloDyNE: Global Topology Preserving Dynamic Network Embedding
The main and common objective of Dynamic Network Embedding (DNE) is to efficiently update node embeddings while preserving network topology at each time step.
Robust Dynamic Network Embedding via Ensembles
It is natural to ask if existing DNE methods can perform well for an input dynamic network without smooth changes.
Attributed Network Embedding for Incomplete Attributed Networks
Attributed networks are ubiquitous since a network often comes with auxiliary attribute information e. g. a social network with user profiles.
Learning Topological Representation for Networks via Hierarchical Sampling
To tackle this issue, we propose a new NRL framework, named HSRL, to help existing NRL methods capture both the local and global topological information of a network.
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Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs
Considering the NIE problem, LICAP adopts a novel sampling strategy called top nodes preferred hierarchical sampling to first group all interested nodes into a top bin and a non-top bin based on node importance scores, and then divide the nodes within top bin into several finer bins also based on the scores.
Fossil Image Identification using Deep Learning Ensembles of Data Augmented Multiviews
This framework is designed for general fossil identification and it is expected to see applications to other fossil datasets in future work.
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack
Despite the progress, applying DGL to real-world applications faces a series of reliability threats including inherent noise, distribution shift, and adversarial attacks.
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery.
