Jun 9, 2023 · I am trying to visualize graph nodes using node2vec embedding. The node2vec embeddings has lengths of 50~100 dimensions. I have two plans: use umap to project node2vec …

Nov 23, 2024 · I am trying to test enhancing the prediction capabilities of traditional node-similarity algorithms, like the Jaccard Coefficient or Adamic Adar, with graph embeddings, like the Node2Vec.

Oct 13, 2020 · Is there a way to have a "smart initialization" with node2vec, i.e., to start the algorithm with an embedding that is not random but precomputed in a certain way?

Aug 20, 2023 · I would like to predict new links using node embeddings and cosine similarity, but I am unsure how to split the data set into training and testing, and how to evaluate new links. This is my …

May 24, 2021 · Use a single Node2Vec run to learn the embedding vectors for all of the nodes in all networks, both labeled and unlabeled. Train a supervised machine learning algorithm using the …

Jun 30, 2017 · I recently came across the terms Word2Vec, Sentence2Vec and Doc2Vec and kind of confused as I am new to vector semantics. Can someone please elaborate the differences in these …

Nov 10, 2023 · I can turn my product and geography tree into (distinct) graphs, then use node2vec to turn these two graphs into vectors. I don’t then understand how to combine these vectors and also …

Seems the easiest way to do this in pytorch geometric is to use an autoencoder model. In the examples folder there is an autoencoder.py which demonstrates its use. The gist of it is that it takes in a single …

Jun 13, 2022 · Interesting idea, why not try it. There is a similar method called node2vec. Your might also have heard about the old Netflix competition where a simple gradient-based matrix factorization …

Here's a link to a post on Medium explaining it - basically, Node2Vec generates random walks on the graph (with hyper-parameters relating to walk length, etc), and embeds nodes in walks the same way …