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Found 3 papers, 3 papers with code
DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs
However, recent advances mostly focus on learning node embeddings as deterministic "vectors" for static graphs yet disregarding the key graph temporal dynamics and the evolving uncertainties associated with node embedding in the latent space.
A Hybrid Classification Approach using Topic Modeling and Graph Convolution Networks
Text classification has become a key operation in various natural language processing tasks.
Seq2Seq and Joint Learning Based Unix Command Line Prediction System
Experimental methods state that our model has achieved accuracy surpassing mixture of other techniques and adaptive command line interface mechanism as acclaimed in the past.
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