%0 Conference Paper %1 epasto_is_2019 %A Epasto, Alessandro %A Perozzi, Bryan %B The World Wide Web Conference on - WWW '19 %C San Francisco, CA, USA %D 2019 %I ACM Press %K Embedding_Algorithm Ensemble_Learning Node_Embeddings %P 394--404 %R 10.1145/3308558.3313660 %T Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts %U http://dl.acm.org/citation.cfm?doid=3308558.3313660 %X Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph. But can nodes really be best described by a single vector representation? In this work, we propose a method for learning multiple representations of the nodes in a graph (e.g., the users of a social network). Based on a principled decomposition of the ego-network, each representation encodes the role of the node in a different local community in which the nodes participate. These representations allow for improved reconstruction of the nuanced relationships that occur in the graph – a phenomenon that we illustrate through state-of-the-art results on link prediction tasks on a variety of graphs, reducing the error by up to 90\%. In addition, we show that these embeddings allow for effective visual analysis of the learned community structure. %@ 978-1-4503-6674-8

@inproceedings{epasto_is_2019, abstract = {Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph. But can nodes really be best described by a single vector representation? In this work, we propose a method for learning multiple representations of the nodes in a graph (e.g., the users of a social network). Based on a principled decomposition of the ego-network, each representation encodes the role of the node in a different local community in which the nodes participate. These representations allow for improved reconstruction of the nuanced relationships that occur in the graph – a phenomenon that we illustrate through state-of-the-art results on link prediction tasks on a variety of graphs, reducing the error by up to 90\%. In addition, we show that these embeddings allow for effective visual analysis of the learned community structure.}, added-at = {2020-02-21T16:09:44.000+0100}, address = {San Francisco, CA, USA}, author = {Epasto, Alessandro and Perozzi, Bryan}, biburl = {https://www.bibsonomy.org/bibtex/2c10acc16d80ebab71087fcfea9f7954f/tschumacher}, booktitle = {The {World} {Wide} {Web} {Conference} on - {WWW} '19}, doi = {10.1145/3308558.3313660}, file = {Epasto, Perozzi - Is a Single Embedding Enough ~ Learning Node Representations that Capture Multiple Social Contexts.pdf:C\:\\Users\\Admin\\Documents\\Research\\_Paperbase\\Graph Embeddings\\Epasto, Perozzi - Is a Single Embedding Enough ~ Learning Node Representations that Capture Multiple Social Contexts.pdf:application/pdf}, interhash = {b645e8eb0740a608b57b5bfd93ac8bd5}, intrahash = {c10acc16d80ebab71087fcfea9f7954f}, isbn = {978-1-4503-6674-8}, keywords = {Embedding_Algorithm Ensemble_Learning Node_Embeddings}, language = {en}, pages = {394--404}, publisher = {ACM Press}, shorttitle = {Is a {Single} {Embedding} {Enough}?}, timestamp = {2020-02-21T16:09:44.000+0100}, title = {Is a {Single} {Embedding} {Enough}? {Learning} {Node} {Representations} that {Capture} {Multiple} {Social} {Contexts}}, url = {http://dl.acm.org/citation.cfm?doid=3308558.3313660}, urldate = {2019-12-10}, year = 2019 }