Bipartite network projection and personal recommendation.

By Tao Zhou, Jie Ren, Matús Medo, Yi-Cheng Zhang. Physical review. E, Statistical, nonlinear, and soft matter physics, 2007

993 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/94724fb11dd7581bd0e511977683cdc46cb2a7e9

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In Atomic and Molecular Physics, and Optics

In Statistical and Nonlinear Physics

Citations in the corpus

Embedding Logical Queries on Knowledge Graphs (2018, background): “Link prediction [25], recommender systems [48], and knowledge base completion [28] are all instances of this common task, where the goal is to predict unobserved edges between nodes in a graph using an observed set of training edges.”
Recommender Systems (2012, methodology): “To calculate the structural similarity between users or objects, we generally project the user–object bipartite network which contains the complete information about the system into a monopartite user–user or object–object network (for more information on this aspect of similarity see [103]).”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, methodology): “DT-Hybrid Domain tuned-hybrid An extended NBI technique that incorporates domain-based knowledge such as drug similarities and target similarities [209] (also look [195, 210, 210, 211, 211??”
The statistical physics of real-world networks (2018, background): “[79] T.”
Link Prediction in Complex Networks: A Survey (2010, background): “In addition, the RA index for bipartite networks can be applied in personalized recommendation with higher accuracy than the classical collaborative filtering [65].”
Knowledge Graph Embedding: A Survey of Approaches and Applications (2017, background): “The path reliability Rðpjh; tÞ can be calculated by a networkbased resource-allocation mechanism [96], and the loss ‘ðp; rÞ is defined as”
ProjE: Embedding Projection for Knowledge Graph Completion (2016, methodology): “Then PTransE uses PCRA [37] to select input paths within a given length constraint.”
Modeling Relation Paths for Representation Learning of Knowledge Bases (2015, methodology): “Resource allocation over networks was originally proposed for personalized recommendation (Zhou et al., 2007), and has been successfully used in information retrieval for measuring relatedness between two objects (Lü and Zhou, 2011).”
Solving the apparent diversity-accuracy dilemma of recommender systems (2008, methodology): “HeatS is a variant on an earlier probabilistic spreading (ProbS) algorithm introduced by our group (25), which redistributes resources in a manner akin to a random-walk process.”
Vital nodes identification in complex networks (2016, methodology): “Following the written relations, the score of each publication is distributed to all its co-authors, which follows the mass diffusion process [349, 350].”