Novelty Paper Recommendation Using Citation Authority Diffusion

Abstract

Survey of academic literature or papers should be considered with both relevance and importance of references. Authors cite related references by considering integrity and novelty. However, the state-of-art publicly academic search engines and services can only recommend related papers of a certain topic. It shows to manually evaluate the novelty of the recommended papers is necessary. In this paper, we propose a citation-network-based methodology, namely Citation Authority Diffusion (CAD), to rapidly mine the limited key papers of a topic, and measure the novelty on literature survey. A defined Authority Matrix (AM) is used to standardize duplication rate of authors and to describe the authority relation between the citing and the cited papers. Based on AM, our CAD methodology leverages the Belief Propagation to diffuse the authority among the citation network. Therefore, CAD transforms the converged citation network to a novelty paper list to researchers. The experimental results show CAD can mine more novelty papers by using real-world cases.

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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 — 15 citations exist upstream.

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