On the public citation graph
Subgraph mining in a large graph: A review
0 signed · 137 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
2 of the 112 references listed on this page are in the Retraction Watch database as retracted (export 2026-10-28): 10.1007/s12652-020-02291-6 (notice 2022-05-30), 10.1088/1742-6596/1916/1/012054 (notice 2022-02-23).
Dataset join, best-effort; this page does not check the publisher record. Retraction Watch data via Crossref Labs, used with credit; OpenCitations Index (CC0) and Meta (CC BY 4.0); dataset doi:10.7910/DVN/WT8VOA (CC BY 4.0).
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/sam.11277openalex-snapshot
FS3: A sampling based method for top‐k frequent subgraph mining
inferredcites →https://doi.org/10.1002/sam.11454openalex-snapshot
Vertex nomination via seeded graph matching
inferredcites →https://doi.org/10.1002/widm.1372openalex-snapshot
A survey of pattern mining in dynamic graphs
inferredcites →https://doi.org/10.1002/widm.1393openalex-snapshot
Dynamical algorithms for data mining and machine learning over dynamic graphs
inferredcites →https://doi.org/10.1002/widm.1405openalex-snapshot
Data stream analysis: Foundations, major tasks and tools
inferredcites →https://doi.org/10.1007/11941439_1openalex-snapshot
What Can We Do with Graph-Structured Data? – A Data Mining Perspective
inferredcites →https://doi.org/10.1007/978-3-030-31756-0openalex-snapshot
Deep Learning: Concepts and Architectures
inferredcites →https://doi.org/10.1007/978-3-030-47124-8_22openalex-snapshot
Modeling Objects and Processes in GIS by Fuzzy Temporal Graphs
inferredcites →https://doi.org/10.1007/978-3-319-17996-4_15openalex-snapshot
A Parallel Algorithm for Frequent Subgraph Mining
inferredcites →https://doi.org/10.1007/978-3-319-41920-6_4openalex-snapshot
A Closed Frequent Subgraph Mining Algorithm in Unique Edge Label Graphs
inferredcites →https://doi.org/10.1007/978-3-319-75193-1_2openalex-snapshot
Mining Generalized Closed Patterns from Multi-graph Collections
inferredcites →https://doi.org/10.1007/978-3-642-03644-6_27openalex-snapshot
MapReduce-Based Pattern Finding Algorithm Applied in Motif Detection for Prescription Compatibility Network
inferredcites →https://doi.org/10.1007/978-3-642-17316-5_51openalex-snapshot
TGP: Mining Top-K Frequent Closed Graph Pattern without Minimum Support
inferredcites →https://doi.org/10.1007/978-981-10-3223-3_31openalex-snapshot
Frequent SubGraph Mining Algorithms: Framework, Classification, Analysis, Comparisons
inferredcites →https://doi.org/10.1007/978-981-32-9042-6_17openalex-snapshot
GIS Based Road Connectivity Evaluation Using Graph Theory
inferredcites →https://doi.org/10.1007/s00010-020-00700-xopenalex-snapshot
Graphs in which all maximal bipartite subgraphs have the same order
inferredcites →https://doi.org/10.1007/s00607-015-0446-9openalex-snapshot
Distributed discovery of frequent subgraphs of a network using MapReduce
inferredcites →https://doi.org/10.1007/s10115-017-1104-7openalex-snapshot
Mining frequent subgraphs from tremendous amount of small graphs using MapReduce
inferredcites →https://doi.org/10.1007/s10115-017-1109-2openalex-snapshot
Mining exceptional closed patterns in attributed graphs
inferredcites →https://doi.org/10.1007/s10618-005-0003-9openalex-snapshot
Finding Frequent Patterns in a Large Sparse Graph*
inferredcites →https://doi.org/10.1007/s10791-011-9172-xopenalex-snapshot
Graph-based term weighting for information retrieval
inferredcites →https://doi.org/10.1007/s10940-019-09426-9openalex-snapshot
Dynamic Patterns of Terrorist Networks: Efficiency and Security in the Evolution of Eleven Islamic Extremist Attack Networks
inferredcites →https://doi.org/10.1007/s10994-010-5215-6openalex-snapshot
Efficiently mining δ-tolerance closed frequent subgraphs
inferredcites →https://doi.org/10.1007/s11042-017-5553-5openalex-snapshot
A common subgraph correspondence mining framework for map search services
inferredcites →https://doi.org/10.1007/s11280-015-0365-xopenalex-snapshot
Graph vs. bag representation models for the topic classification of web documents
inferredcites →https://doi.org/10.1007/s12652-020-02291-6openalex-snapshot
RETRACTED ARTICLE: Normal discriminant deep convolution neural classification based web behavioral pattern mining for user identification
inferredcites →https://doi.org/10.1007/s13042-017-0655-yopenalex-snapshot
Uncertain maximal frequent subgraph mining algorithm based on adjacency matrix and weight
inferredcites →https://doi.org/10.1007/s13278-020-00682-3openalex-snapshot
Online social network trend discovery using frequent subgraph mining
inferredcites →https://doi.org/10.1016/j.asoc.2016.01.010openalex-snapshot
DiffNodesets: An efficient structure for fast mining frequent itemsets
inferredcites →https://doi.org/10.1016/j.comnet.2021.107865openalex-snapshot
Towards website domain name classification using graph based semi-supervised learning
inferredcites →https://doi.org/10.1016/j.cose.2020.102006openalex-snapshot
Efficient classification model of web news documents using machine learning algorithms for accurate information
inferredcites →https://doi.org/10.1016/j.dam.2020.06.003openalex-snapshot
On the extremal sizes of maximal graphs without ( k + 1 ) -connected subgraphs
inferredcites →https://doi.org/10.1016/j.dss.2020.113303openalex-snapshot
Fraud detection: A systematic literature review of graph-based anomaly detection approaches
inferredcites →https://doi.org/10.1016/j.engappai.2020.103539openalex-snapshot
Fast and scalable algorithms for mining subgraphs in a single large graph
inferredcites →https://doi.org/10.1016/j.eswa.2012.01.062openalex-snapshot
DBV-Miner: A Dynamic Bit-Vector approach for fast mining frequent closed itemsets
inferredcites →https://doi.org/10.1016/j.eswa.2015.04.048openalex-snapshot
An N-list-based algorithm for mining frequent closed patterns
inferredcites →https://doi.org/10.1016/j.eswa.2016.12.023openalex-snapshot
A novel approach for mining maximal frequent patterns
inferredcites →https://doi.org/10.1016/j.eswa.2017.10.039openalex-snapshot
A weighted N-list-based method for mining frequent weighted itemsets
inferredcites →https://doi.org/10.1016/j.eswa.2018.03.041openalex-snapshot
negFIN: An efficient algorithm for fast mining frequent itemsets
inferredcites →https://doi.org/10.1016/j.future.2016.06.034openalex-snapshot
A survey: Cyber-physical-social systems and their system-level design methodology
inferredcites →https://doi.org/10.1016/j.future.2020.01.042openalex-snapshot
Sampling informative patterns from large single networks
inferredcites →https://doi.org/10.1016/j.future.2020.12.010openalex-snapshot
AI-guided resource allocation and rescue decision system for medical applications
inferredcites →https://doi.org/10.1016/j.inffus.2019.02.004openalex-snapshot
Social network analysis-based conflict relationship investigation and conflict degree-based consensus reaching process for large scale decision making using sparse representation
inferredcites →https://doi.org/10.1016/j.ins.2018.05.044openalex-snapshot
Sparsity measure of a network graph: Gini index
inferredcites →https://doi.org/10.1016/j.ins.2019.01.036openalex-snapshot
Correct filtering for subgraph isomorphism search in compressed vertex-labeled graphs
inferredcites →https://doi.org/10.1016/j.ins.2019.12.010openalex-snapshot
Mining weighted subgraphs in a single large graph
inferredcites →https://doi.org/10.1016/j.ipm.2017.12.003openalex-snapshot
Mining user interests over active topics on social networks
inferredcites →https://doi.org/10.1016/j.is.2013.08.005openalex-snapshot
Density-based data partitioning strategy to approximate large-scale subgraph mining
inferredcites →https://doi.org/10.1016/j.knosys.2014.04.040openalex-snapshot
Mining maximal frequent patterns in a single graph using inexact matching
inferredcites →https://doi.org/10.1016/j.knosys.2018.03.022openalex-snapshot
Graph embedding techniques, applications, and performance: A survey
inferredcites →https://doi.org/10.1016/j.knosys.2019.105107openalex-snapshot
F-Mapper: A Fuzzy Mapper clustering algorithm
inferredcites →https://doi.org/10.1016/j.patcog.2021.108334openalex-snapshot
Graph Clustering via Variational Graph Embedding
inferredcites →https://doi.org/10.1016/j.procs.2015.03.198openalex-snapshot
Frequent Subgraph Mining Algorithms – A Survey
inferredcites →https://doi.org/10.1016/j.procs.2015.08.446openalex-snapshot
Knowledge Representation through Graphs
inferredcites →https://doi.org/10.1017/cbo9781107279667.013openalex-snapshot
Introduction to algorithms
inferredcites →https://doi.org/10.1017/s0269888912000331openalex-snapshot
A survey of frequent subgraph mining algorithms
inferredcites →https://doi.org/10.1021/acs.jcim.0c00459openalex-snapshot
ChemGrapher: Optical Graph Recognition of Chemical Compounds by Deep Learning
inferredcites →https://doi.org/10.1023/b:dami.0000005258.31418.83openalex-snapshot
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
inferredcites →https://doi.org/10.1088/1742-6596/1916/1/012054openalex-snapshot
Frequent Pattern Mining Using Db-Scan Algorithm.
inferredcites →https://doi.org/10.1109/access.2018.2799953openalex-snapshot
An Efficient Local Search for the Maximum Edge Weighted Clique Problem
inferredcites →https://doi.org/10.1109/access.2020.3017486openalex-snapshot
A Combination of Frequent Pattern Mining and Graph Traversal Approaches for Aspect Elicitation in Customer Reviews
inferredcites →https://doi.org/10.1109/access.2021.3073494openalex-snapshot
An Efficient Subgraph Isomorphism Solver for Large Graphs
inferredcites →https://doi.org/10.1109/access.2021.3133666openalex-snapshot
A Method for Closed Frequent Subgraph Mining in a Single Large Graph
inferredcites →https://doi.org/10.1109/bdcloud.2018.00171openalex-snapshot
A Novel Graph Analytic Approach to Monitor Terrorist Networks
inferredcites →https://doi.org/10.1109/bigdata.2013.6691633openalex-snapshot
Efficiently extracting frequent subgraphs using MapReduce
inferredcites →https://doi.org/10.1109/bigdata.2016.7840995openalex-snapshot
Parallel graph mining with dynamic load balancing
inferredcites →https://doi.org/10.1109/hpcc.and.euc.2013.339openalex-snapshot
Applying Graph theory to the Internet of Things
inferredcites →https://doi.org/10.1109/icde.2009.133openalex-snapshot
GraphSig: A Scalable Approach to Mining Significant Subgraphs in Large Graph Databases
inferredcites →https://doi.org/10.1109/icde.2014.6816705openalex-snapshot
Large-scale frequent subgraph mining in MapReduce
inferredcites →https://doi.org/10.1109/icdm.2002.1184038openalex-snapshot
gSpan: graph-based substructure pattern mining
inferredcites →https://doi.org/10.1109/icdm.2007.36openalex-snapshot
gApprox: Mining Frequent Approximate Patterns from a Massive Network
inferredcites →https://doi.org/10.1109/icpr48806.2021.9412531openalex-snapshot
Epitomic Variational Graph Autoencoder
inferredcites →https://doi.org/10.1109/sc.2016.60openalex-snapshot
ScaleMine: Scalable Parallel Frequent Subgraph Mining in a Single Large Graph
inferredcites →https://doi.org/10.1109/tbdata.2021.3072001openalex-snapshot
Frequent Subgraph Mining Algorithms in Static and Temporal Graph-Transaction Settings: A Survey
inferredcites →https://doi.org/10.1109/tetci.2021.3067017openalex-snapshot
Frequent Pattern Mining in Big Social Graphs
inferredcites →https://doi.org/10.1109/tfuzz.2020.3014662openalex-snapshot
SFCM: A Fuzzy Clustering Algorithm of Extracting the Shape Information of Data
inferredcites →https://doi.org/10.1109/tkde.2005.60openalex-snapshot
Efficient algorithms for mining closed itemsets and their lattice structure
inferredcites →https://doi.org/10.1109/tkde.2014.2345408openalex-snapshot
An Iterative MapReduce Based Frequent Subgraph Mining Algorithm
inferredcites →https://doi.org/10.1109/tkde.2018.2828086openalex-snapshot
Scalable Interactive Dynamic Graph Clustering on Multicore CPUs
inferredcites →https://doi.org/10.1109/tkde.2018.2830336openalex-snapshot
Efficient Mining of Frequent Patterns on Uncertain Graphs
inferredcites →https://doi.org/10.1111/2041-210x.13499openalex-snapshot
Estimating heritability of social phenotypes from social networks
inferredcites →https://doi.org/10.1137/18m1210459openalex-snapshot
Finding Cliques in Social Networks: A New Distribution-Free Model
inferredcites →https://doi.org/10.1142/7731openalex-snapshot
Graph Classification and Clustering Based on Vector Space Embedding
inferredcites →https://doi.org/10.1145/1081870.1081948openalex-snapshot
A fast kernel-based multilevel algorithm for graph clustering
inferredcites →https://doi.org/10.1145/2020408.2020501openalex-snapshot
Mining frequent closed graphs on evolving data streams
inferredcites →https://doi.org/10.1145/2382936.2383055openalex-snapshot
An iterative MapReduce approach to frequent subgraph mining in biological datasets
inferredcites →https://doi.org/10.1145/2629550openalex-snapshot
The Complexity of Mining Maximal Frequent Subgraphs
inferredcites →https://doi.org/10.1145/335191.335372openalex-snapshot
Mining frequent patterns without candidate generation
inferredcites →https://doi.org/10.1186/s12859-019-2837-yopenalex-snapshot
A linear delay algorithm for enumerating all connected induced subgraphs
inferredcites →https://doi.org/10.1186/s13040-018-0181-9openalex-snapshot
Grasping frequent subgraph mining for bioinformatics applications
inferredcites →https://doi.org/10.1186/s13040-021-00250-1openalex-snapshot
RASMA: a reverse search algorithm for mining maximal frequent subgraphs
inferredcites →https://doi.org/10.13164/mendel.2021.2.090openalex-snapshot
CCGraMi: An Effective Method for Mining Frequent Subgraphs in a Single Large Graph
inferredcites →https://doi.org/10.14778/2535568.2448946openalex-snapshot
An in-depth comparison of subgraph isomorphism algorithms in graph databases
inferredcites →https://doi.org/10.1515/cait-2017-0001openalex-snapshot
An Optimization of Closed Frequent Subgraph Mining Algorithm
inferredcites →https://doi.org/10.1515/comp-2018-0018openalex-snapshot
A qualitative survey on frequent subgraph mining
inferredcites →https://doi.org/10.21236/ada439436openalex-snapshot
GREWA Scalable Frequent Subgraph Discovery Algorithm
inferredcites →https://doi.org/10.26438/ijcse/v6i10.722729openalex-snapshot
Application of Graph Theory in Social Media
inferredcites →https://doi.org/10.3389/fdata.2021.680535openalex-snapshot
Deep Graph Mapper: Seeing Graphs Through the Neural Lens
inferredcites →https://doi.org/10.3390/app8020230openalex-snapshot
A Parallel Approach for Frequent Subgraph Mining in a Single Large Graph Using Spark
inferredcites →https://doi.org/10.34028/iajit/18/2/9openalex-snapshot
Parallelization of Frequent Itemset Mining Methods with FP-tree: An Experiment with PrePost+ Algorithm
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-21743-2_6openalex-snapshot
Frequent Closed Subgraph Mining: A Multi-thread Approach
inferred← citeshttps://doi.org/10.1007/978-3-031-41620-0_3openalex-snapshot
Event Abstraction for Partial Order Patterns
inferred← citeshttps://doi.org/10.1007/978-3-031-70381-2_9openalex-snapshot
GraphRPM: Risk Pattern Mining on Industrial Large Attributed Graphs
inferred← citeshttps://doi.org/10.1007/s10489-023-04501-yopenalex-snapshot
A novel approach to discover frequent weighted subgraphs using the average measure
inferred← citeshttps://doi.org/10.1007/s40009-026-02043-8openalex-snapshot
Optimized Automated Machine Learning Framework for Hyperparameter Tuning
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121816openalex-snapshot
DeepDense: Enabling node embedding to dense subgraph mining
inferred← citeshttps://doi.org/10.1016/j.ins.2025.122670openalex-snapshot
Reinforced Heterogeneous Graphlet Design for Knowledge Graph Representation Learning
inferred← citeshttps://doi.org/10.1016/j.knosys.2024.111671openalex-snapshot
Using dynamic knowledge graphs to detect emerging communities of knowledge
inferred← citeshttps://doi.org/10.1080/01969722.2022.2162740openalex-snapshot
Mining Association Rules from a Single Large Graph
inferred← citeshttps://doi.org/10.1080/24751839.2025.2500132openalex-snapshot
An efficient method for mining frequent weighted subgraphs based on weighted edges
inferred← citeshttps://doi.org/10.1093/bib/bbae565openalex-snapshot
Prototype-based contrastive substructure identification for molecular property prediction
inferred← citeshttps://doi.org/10.1109/access.2024.3376477openalex-snapshot
Probabilistic Support Prediction: Fast Frequent Itemset Mining in Dense Data
inferred← citeshttps://doi.org/10.1109/bigdata55660.2022.10020828openalex-snapshot
A Genetic Algorithm For Boolean Semiring Matrix Factorization With Applications To Graph Mining
inferred← citeshttps://doi.org/10.1109/bigdata66926.2025.11402214openalex-snapshot
Functional Programming Meets Pinecone: Recommending Graph Structured Documents
inferred← citeshttps://doi.org/10.1109/icbda65366.2025.11210900openalex-snapshot
Mining Frequent Subgraphs in Temporal Quadruple Knowledge Graphs
inferred← citeshttps://doi.org/10.1145/3711896.3736966openalex-snapshot
FLEXIS: FLEXible Frequent Subgraph Mining using Maximal Independent Sets
inferred← citeshttps://doi.org/10.1145/3729427openalex-snapshot
Learning to Reduce the Scale of Large Graphs: A Comprehensive Survey
inferred← citeshttps://doi.org/10.21203/rs.3.rs-2294036/v1openalex-snapshot
Sufficient instance networks for computing support in graph databases
inferred← citeshttps://doi.org/10.21817/indjcse/2022/v13i3/221303098openalex-snapshot
DAN EMPIRICAL ANALYSIS OF GRAPH ALGORITHM IN CONTEXT OF FREQUENT SUBGRAPH MINING ON GIRAPH SYSTEM
inferred← citeshttps://doi.org/10.32657/10356/176240openalex-snapshot
Efficient algorithms for subgraph counting and enumeration on large graphs
inferred← citeshttps://doi.org/10.3390/electronics11131931openalex-snapshot
A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social Networks
inferred← citeshttps://doi.org/10.4018/ijitsa.327004openalex-snapshot
Discovery of User Groups Densely Connecting Virtual and Physical Worlds in Event-Based Social Networks
inferred← citeshttps://doi.org/10.70675/da7b772aza572z4b78z89c4zbce8fd9f8095openalex-snapshot
Anomaly Detection in Graph Streams
inferred← citeshttps://doi.org/10.7717/peerj-cs.2334openalex-snapshot
Quick mining in dense data: applying probabilistic support prediction in depth-first order
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.