Open AccessPosted Content
Personalized PageRank with Node-dependent Restart
TL;DR: This work introduces two generalizations of Personalized PageRank with node-dependent restart and shows that both generalizations have an elegant expression connecting the so-called direct and reverse PersonalizedPageRank that yield a symmetry property of these Personalization PageRanks.
read more
Abstract: Personalized PageRank is an algorithm to classify the improtance of web pages on a user-dependent basis. We introduce two generalizations of Personalized PageRank with node-dependent restart. The first generalization is based on the proportion of visits to nodes before the restart, whereas the second generalization is based on the probability of visited node just before the restart. In the original case of constant restart probability, the two measures coincide. We discuss interesting particular cases of restart probabilities and restart distributions. We show that the both generalizations of Personalized PageRank have an elegant expression connecting the so-called direct and reverse Personalized PageRanks that yield a symmetry property of these Personalized PageRanks.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Similarities on graphs: Kernels versus proximity measures
TL;DR: Analytically study proximity and distance properties of various kernels and similarity measures on graphs to understand the mathematical nature of such measures and can potentially be useful for recommending the adoption of specific similarity measures in data analysis.
Kernels on Graphs as Proximity Measures
Konstantin Avrachenkov,Pavel Chebotarev,Dmytro Rubanov +2 more
- 15 Jun 2017
TL;DR: It is observed that normalized heat-type similarity measures with log modification generally perform the best and this can potentially be useful for recommending the adoption of one or another similarity measure in a machine learning method.
Random walks on complex networks with multiple resetting nodes: a renewal approach
TL;DR: This paper studies discrete-time random walks on complex networks with multiple resetting nodes and derives exact expressions of the occupation probability of the walker in each node and mean first-passage time between arbitrary two nodes.
16
A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia
Fei Liu,Xiangkang Jiang,Jingyuan Yang,Jiawei Tao,Maoguang Zhang +4 more
TL;DR: A multi-target drug discovery method by the example of therapeutic hypothermia that can effectively avoid inhibiting beneficial proteins while inhibiting harmful proteins and has potential in precision medicine for its high compatibility with bioinformatics and promotes the development of pharmacogenomics and bioinfo-pharmacology.
4
BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs
TL;DR: BatchSampler as discussed by the authors proposes to improve contrastive learning by sampling mini-batches from the input data, which can be directly plugged into existing Contrastive Learning models in vision, language, and graphs.
References
•Proceedings Article
The PageRank Citation Ranking : Bringing Order to the Web
Lawrence Page,Sergey Brin,Rajeev Motwani,Terry Winograd +3 more
- 11 Nov 1999
TL;DR: This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them, and shows how to efficiently compute PageRank for large numbers of pages.
16.4K
Topic-sensitive PageRank
Taher H. Haveliwala
- 07 May 2002
TL;DR: A set of PageRank vectors are proposed, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic, and are shown to generate more accurate rankings than with a single, generic PageRank vector.
Trust-aware recommender systems
Paolo Massa,Paolo Avesani +1 more
- 19 Oct 2007
TL;DR: This work proposes to replace the step of finding similar users with the use of a trust metric, an algorithm able to propagate trust over the trust network and to estimate a trust weight that can be used in place of the similarity weight.
Co-authorship networks in the digital library research community
TL;DR: In this paper, the authors examined the state of the DL domain after a decade of activity by applying social network analysis to the co-authorship network of the past ACM, IEEE, and joint ACM/IEEE digital library conferences.
953