{
 "id": "graphs-datasets/IMDB-BINARY",
 "owner": "graphs-datasets",
 "name": "IMDB-BINARY",
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 "last_modified": "2023-02-07T16:39:00.000Z",
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 "tasks": [
  "graph-ml"
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 "citation": "@inproceedings{10.1145/2783258.2783417,\nauthor = {Yanardag, Pinar and Vishwanathan, S.V.N.},\ntitle = {Deep Graph Kernels},\nyear = {2015},\nisbn = {9781450336642},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nurl = {https://doi.org/10.1145/2783258.2783417},\ndoi = {10.1145/2783258.2783417},\nabstract = {In this paper, we present Deep Graph Kernels, a unified framework to learn latent representations of sub-structures for graphs, inspired by latest advancements in language modeling and deep learning. Our framework leverages the dependency information between sub-structures by learning their latent representations. We demonstrate instances of our framework on three popular graph kernels, namely Graphlet kernels, Weisfeiler-Lehman subtree kernels, and Shortest-Path graph kernels. Our experiments on several benchmark datasets show that Deep Graph Kernels achieve significant improvements in classification accuracy over state-of-the-art graph kernels.},\nbooktitle = {Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},\npages = {1365–1374},\nnumpages = {10},\nkeywords = {collaboration networks, bioinformatics, r-convolution kernels, graph kernels, structured data, deep learning, social networks, string kernels},\nlocation = {Sydney, NSW, Australia},\nseries = {KDD '15}\n}",
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