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Found 9 papers, 6 papers with code
Neural Subgraph Matching
Subgraph matching is the problem of determining the presence and location(s) of a given query graph in a large target graph.
Graph Representation Ensemble Learning
In this work, we introduce the problem of graph representation ensemble learning and provide a first of its kind framework to aggregate multiple graph embedding methods efficiently.
ArduCode: Predictive Framework for Automation Engineering
We show that machine learning can be leveraged to assist the automation engineer in classifying automation, finding similar code snippets, and reasoning about the hardware selection of sensors and actuators.
Benchmarks for Graph Embedding Evaluation
We use the comparisons on our 100 benchmark graphs to define GFS-score, that can be applied to any embedding method to quantify its performance.
Tracking Temporal Evolution of Graphs using Non-Timestamped Data
Datasets to study the temporal evolution of graphs are scarce.
Social and Information Networks
Pykg2vec: A Python Library for Knowledge Graph Embedding
Python library for knowledge graph embedding and representation learning.
DynamicGEM: A Library for Dynamic Graph Embedding Methods
DynamicGEM is an open-source Python library for learning node representations of dynamic graphs.
dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning
Capturing such evolution is key to predicting the properties of unseen networks.
Ranked #5 on
Dynamic Link Prediction
on Enron Emails
Future Automation Engineering using Structural Graph Convolutional Neural Networks
The digitalization of automation engineering generates large quantities of engineering data that is interlinked in knowledge graphs.
