ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2017
Local Higher-Order Graph Clustering.
Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed.
- Targeted spreader identification via lexicographic core decomposition.Scientific reports · 2026Article
- Local graph estimation with pathwise false discovery control.Nature communications · 2026Article
- Powers of magnetic graph matrix: Fourier spectrum, walk compression, and applications.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Enhanced graph coevolution network for social network analysis using assimilation modified emotional algorithm.Scientific reports · 2026Article
- Missing Value Imputation in Relational Data using Variational Inference.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- Two antagonistic objectives for one multi-scale graph clustering framework.Scientific reports · 2025Article
- Identification of Important Nodes Based on Local Effective Distance-Integrated Gravity Model.Entropy (Basel, Switzerland) · 2025Article
- Measuring group fairness in community detection.PloS one · 2025Article
- Iterative embedding and reweighting of complex networks reveals community structure.Scientific reports · 2024Article
- Nonlinear bias toward complex contagion in uncertain transmission settings.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- The simpliciality of higher-order networks.EPJ data science · 2024Article
- Exploring the landscape of dismantling strategies based on the community structure of networks.Scientific reports · 2023Article
- From single-neuron dynamics to higher-order circuit motifs in control and pathological brain networks.The Journal of physiology · 2023Article
- A Two-Phase Feature Selection Method for Identifying Influential Spreaders of Disease Epidemics in Complex Networks.Entropy (Basel, Switzerland) · 2023Article
- A mechanics model based on information entropy for identifying influencers in complex networks.Applied intelligence (Dordrecht, Netherlands) · 2023Article
- A new clustering method based on multipartite networks.PeerJ. Computer science · 2023Article
- Impact of second-order network motif on online social networks.The Journal of supercomputing · 2022Article
- Article
- Positively Correlated Samples Save Pooled Testing Costs.IEEE transactions on network science and engineering · 2021Article
- An efficient and effective method to identify significantly perturbed subnetworks in cancer.Nature computational science · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Local graph clustering methods aim to find a cluster of nodes by exploring a small region of the graph. These methods are attractive because they enable targeted clustering around a given seed node and are faster than traditional global graph clustering methods because their runtime does not depend on the size of the input graph. However, current local graph partitioning methods are not designed to account for the higher-order structures crucial to the network, nor can they effectively handle directed networks. Here we introduce a new class of local graph clustering methods that address these issues by incorporating higher-order network information captured by small subgraphs, also called network motifs. We develop the Motif-based Approximate Personalized PageRank (MAPPR) algorithm that finds clusters containing a seed node with minimal
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.