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SubRank: Subgraph Embeddings via a Subgraph Proximity Measure

Oana Balalau 1 Sagar Goyal 2
1 CEDAR - Rich Data Analytics at Cloud Scale
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], Inria Saclay - Ile de France
Abstract : Representation learning for graph data has gained a lot ofattention in recent years. However, state-of-the-art research is focusedmostly on node embeddings, with little effort dedicated to the closelyrelated task of computing subgraph embeddings. Subgraph embeddingshave many applications, such as community detection, cascade predic-tion, and question answering. In this work, we propose a subgraph to sub-graph proximity measure as a building block for a subgraph embeddingframework. Experiments on real-world datasets show that our approach,SubRank, outperforms state-of-the-art methods on several importantdata mining tasks.
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https://hal.inria.fr/hal-03134181
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Submitted on : Monday, February 8, 2021 - 10:52:48 AM
Last modification on : Saturday, May 1, 2021 - 3:38:41 AM
Long-term archiving on: : Sunday, May 9, 2021 - 6:24:04 PM

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Oana Balalau, Sagar Goyal. SubRank: Subgraph Embeddings via a Subgraph Proximity Measure. PAKDD 2020 - Pacific-Asia Conference on Knowledge Discovery and Data Mining, May 2020, Singapore, Singapore. pp.487-498, ⟨10.1007/978-3-030-47426-3_38⟩. ⟨hal-03134181⟩

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