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Conference Papers Year : 2020

SubRank: Subgraph Embeddings via a Subgraph Proximity Measure

Oana Balalau
Sagar Goyal
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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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Dates and versions

hal-03134181 , version 1 (08-02-2021)

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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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