Effective Temporal Graph Learning via Personalized PageRank [PDF]
EntropyGraph representation learning aims to map nodes or edges within a graph using low-dimensional vectors, while preserving as much topological information as possible.
Longlong Lin, Ziyu Liao, Tao Liu
exaly +4 more sources
HedgeRank: Heterogeneity-Aware, Energy-Efficient Partitioning of Personalized PageRank at the Edge [PDF]
Micromachines, 2023 Personalized PageRank (PPR) is a widely used graph processing algorithm used to calculate the importance of source nodes in a graph. Generally, PPR is executed by using a high-performance microprocessor of a server, but it needs to be executed on edge ...
Young-Ho Gong
doaj +2 more sources
PPR-SSM: personalized PageRank and semantic similarity measures for entity linking [PDF]
BMC Bioinformatics, 2019 Background Biomedical literature concerns a wide range of concepts, requiring controlled vocabularies to maintain a consistent terminology across different research groups.
Andre Lamurias +2 more
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Toward Efficient Hub-Less Real Time Personalized PageRank
IEEE Access, 2017 In the era of big data, reduced models capable of reducing big data graph to estimate personalized PageRank are limited. Personalized PageRank is a page rank calculation where random jumps are only allowed to a subset of start nodes.
Justin Zhan, Matin Pirouz, Justin Zhan
exaly +3 more sources
Monte Carlo Based Personalized PageRank on Dynamic Networks
International Journal of Distributed Sensor Networks, 2013 In large-scale networks, the structure of the underlying network changes frequently, and thus the power iteration method for Personalized PageRank computation cannot deal with this kind of dynamic network efficiently.
Zhang Junchao +3 more
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A Survey on Personalized PageRank Computation Algorithms
IEEE Access, 2019 Personalized PageRank (PPR) is an important variation of PageRank, which is a widely applied popularity measure for Web search. Unlike the original PageRank, PPR is a node proximity measure that represents the degree of closeness among multiple nodes ...
Sungchan Park
exaly +3 more sources
E-commerce recommender system design based on web information extraction and sentiment analysis. [PDF]
PLoS ONEThe research proposes an e-commerce recommendation system based on web page information extraction and sentiment analysis. Through the improved S-PageRank algorithm and the dynamic topic library generation strategy, the precision rate of cross-platform ...
Jinfeng Feng
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User preference modeling for movie recommendations based on deep learning [PDF]
Scientific ReportsCurrent movie recommendation systems often struggle to capture complex user preferences and dynamics, primarily relying on content-based or collaborative filtering techniques. This research introduces a novel deep learning-powered method to enhance movie
Yang Gao, Hong Zheng, Haonan Cui
doaj +2 more sources
Contrastive unlearning via representation editing for graph neural networks [PDF]
Scientific ReportsGraph unlearning (GU) aims to eliminate the influence of specific nodes, edges, or features from a trained graph neural network (GNN), and is of great importance for privacy protection and data quality management.
Zhifan Huang +4 more
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ID-GBA: Subgraph Extension With Information Distance Guilt by Association in Complex Networks [PDF]
IEEE AccessHere, we introduce the ID-GBA (Information Distance Guilt By Association) method to expand highly connected sets of nodes by deploying a novel algorithm for subgraph extension based on the guilt-by-association principle and information distance.
Predrag Obradovic +3 more
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