%0 Conference Paper %1 grover_node2vec:_2016 %A Grover, Aditya %A Leskovec, Jure %B Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16 %C San Francisco, California, USA %D 2016 %I ACM Press %K Embedding_Algorithm Neural_Embedding Node_Embeddings Random_Walks Skip-Gram %P 855--864 %R 10.1145/2939672.2939754 %T node2vec: Scalable Feature Learning for Networks %U http://dl.acm.org/citation.cfm?doid=2939672.2939754 %X Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. %@ 978-1-4503-4232-2

@inproceedings{grover_node2vec:_2016, abstract = {Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks.}, added-at = {2020-02-21T16:09:44.000+0100}, address = {San Francisco, California, USA}, author = {Grover, Aditya and Leskovec, Jure}, biburl = {https://www.bibsonomy.org/bibtex/255e2ab850a4228f9fb22248de54a6169/tschumacher}, booktitle = {Proceedings of the 22nd {ACM} {SIGKDD} {International} {Conference} on {Knowledge} {Discovery} and {Data} {Mining} - {KDD} '16}, doi = {10.1145/2939672.2939754}, file = {Grover, Leskovec - node2vec ~Scalable Feature Learning for Networks.pdf:C\:\\Users\\Admin\\Documents\\Research\\_Paperbase\\Graph Embeddings\\Grover, Leskovec - node2vec ~Scalable Feature Learning for Networks.pdf:application/pdf}, interhash = {ca9e06fe185612d3492f8d54d5ee752b}, intrahash = {55e2ab850a4228f9fb22248de54a6169}, isbn = {978-1-4503-4232-2}, keywords = {Embedding_Algorithm Neural_Embedding Node_Embeddings Random_Walks Skip-Gram}, language = {en}, pages = {855--864}, publisher = {ACM Press}, shorttitle = {node2vec}, timestamp = {2020-02-21T16:09:44.000+0100}, title = {node2vec: {Scalable} {Feature} {Learning} for {Networks}}, url = {http://dl.acm.org/citation.cfm?doid=2939672.2939754}, urldate = {2019-12-10}, year = 2016 }