Search Results for author: Sarah S. Lam

Found 2 papers, 0 papers with code

RECS: Robust Graph Embedding Using Connection Subgraphs

no code implementations3 May 2018 Saba A. Al-Sayouri, Danai Koutra, Evangelos E. Papalexakis, Sarah S. Lam

Representation learning algorithms aim to preserve local and global network structure by identifying node neighborhood notions.

General Classification Graph Embedding +4

t-PINE: Tensor-based Predictable and Interpretable Node Embeddings

no code implementations3 May 2018 Saba A. Al-Sayouri, Ekta Gujral, Danai Koutra, Evangelos E. Papalexakis, Sarah S. Lam

Contrary to baseline methods, which generally learn explicit graph representations by solely using an adjacency matrix, t-PINE avails a multi-view information graph, the adjacency matrix represents the first view, and a nearest neighbor adjacency, computed over the node features, is the second view, in order to learn explicit and implicit node representations, using the Canonical Polyadic (a. k. a.

General Classification Link Prediction +3

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