A link-based similarity measure for scientific literature

Abstract

In this paper, we propose a new approach to measure sim-ilarities among academic papers based on their references. Our similarity measure uses both in-link and out-link by transforming in-link and out-link into undirected links.

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No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 19 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.