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MaNIACS : Approximate Mining of Frequent Subgraph Patterns through Sampling
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1006/jcss.2000.1741openalex-snapshot
Improved Bounds on the Sample Complexity of Learning
inferredcites →https://doi.org/10.1007/978-3-319-05401-8_11openalex-snapshot
Discovering Colored Network Motifs
inferredcites →https://doi.org/10.1007/978-3-642-33460-3_7openalex-snapshot
Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees
inferredcites →https://doi.org/10.1007/bf02546665openalex-snapshot
Kombinatorische Anzahlbestimmungen für Gruppen, Graphen und chemische Verbindungen
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/s10618-006-0044-8openalex-snapshot
Support measures for graph data*
inferredcites →https://doi.org/10.1007/s10618-016-0466-xopenalex-snapshot
A distributed approach for graph mining in massive networks
inferredcites →https://doi.org/10.1007/s10618-020-00686-9openalex-snapshot
Counting frequent patterns in large labeled graphs: a hypergraph-based approach
inferredcites →https://doi.org/10.1016/j.eswa.2018.04.010openalex-snapshot
Ap-FSM: A parallel algorithm for approximate frequent subgraph mining using Pregel
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.ins.2018.04.001openalex-snapshot
Mining frequent subgraphs in multigraphs
inferredcites →https://doi.org/10.1017/cbo9781107298019openalex-snapshot
Understanding Machine Learning
inferredcites →https://doi.org/10.1080/00401706.1999.10485951openalex-snapshot
Statistical Learning Theory
inferredcites →https://doi.org/10.1093/bioinformatics/bth436openalex-snapshot
Modeling interactome: scale-free or geometric?
inferredcites →https://doi.org/10.1093/bioinformatics/btx758openalex-snapshot
Efficiently counting all orbits of graphlets of any order in a graph using autogenerated equations
inferredcites →https://doi.org/10.1093/comjnl/bxv118openalex-snapshot
Frequent Subgraph Mining Based on Pregel
inferredcites →https://doi.org/10.1109/icdm.2003.1250900openalex-snapshot
Frequent sub-structure-based approaches for classifying chemical compounds
inferredcites →https://doi.org/10.1109/tcbb.2017.2756879openalex-snapshot
Discovery of Functional Motifs from the Interface Region of Oligomeric Proteins Using Frequent Subgraph Mining
inferredcites →https://doi.org/10.1109/tkde.2005.127openalex-snapshot
Frequent substructure-based approaches for classifying chemical compounds
inferredcites →https://doi.org/10.1145/1830252.1830274openalex-snapshot
Frequent subgraph mining on a single large graph using sampling techniques
inferredcites →https://doi.org/10.1145/1835804openalex-snapshot
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
inferredcites →https://doi.org/10.1145/2629564openalex-snapshot
Efficiently Estimating Motif Statistics of Large Networks
inferredcites →https://doi.org/10.1145/2629586openalex-snapshot
Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees
inferredcites →https://doi.org/10.1145/3336191.3371773openalex-snapshot
Efficiently Counting Vertex Orbits of All 5-vertex Subgraphs, by EVOKE
inferredcites →https://doi.org/10.1145/3442590openalex-snapshot
TipTap: Approximate Mining of Frequent k -Subgraph Patterns in Evolving Graphs
inferredcites →https://doi.org/10.1186/s12859-018-2483-9openalex-snapshot
Optimising orbit counting of arbitrary order by equation selection
inferredcites →https://doi.org/10.1186/s13040-018-0181-9openalex-snapshot
Grasping frequent subgraph mining for bioinformatics applications
inferredcites →https://doi.org/10.14778/2824032.2824048openalex-snapshot
Association rules with graph patterns
inferredcites →https://doi.org/10.2307/2314609openalex-snapshot
An Introduction to Probability Theory and Its Applications.
inferredcites →https://doi.org/10.4018/978-1-60566-010-3.ch259openalex-snapshot
Sampling Methods in Approximate Query Answering Systems
inferredcites →https://doi.org/10.48550/arxiv.1906.01599openalex-snapshot
Motivo: fast motif counting via succinct color coding and adaptive sampling
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s10791-025-09644-9openalex-snapshot
An association rule-based approach for frequent item mining of multi-stage access data
inferred← citeshttps://doi.org/10.1007/s10844-024-00866-9openalex-snapshot
Heuristic approaches for non-exhaustive pattern-based change detection in dynamic networks
inferred← citeshttps://doi.org/10.1007/s13278-023-01157-xopenalex-snapshot
An adaptive graph sampling framework for graph analytics
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.113956openalex-snapshot
Efficient mining of incremental high utility patterns with negative unit profits over all the accumulated stream data
inferred← citeshttps://doi.org/10.1016/j.neucom.2024.128369openalex-snapshot
Graph classification using high-difference-frequency subgraph embedding
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/10.1109/access.2024.3376477openalex-snapshot
Probabilistic Support Prediction: Fast Frequent Itemset Mining in Dense Data
inferred← citeshttps://doi.org/10.1109/access.2025.3602411openalex-snapshot
Subgraph-Aware Joint Modeling for Graph Classification via Efficient Frequent Pattern Mining and Fusion
inferred← citeshttps://doi.org/10.1109/tkde.2025.3544696openalex-snapshot
RayE-Sub: Countering Subgraph Degradation via Perfect Reconstruction
inferred← citeshttps://doi.org/10.14778/3712221.3712225openalex-snapshot
Efficient Top- k Frequent Subgraph Mining using Tight Upper and Lower Bounds
inferred← citeshttps://doi.org/10.14778/3819518.3819550openalex-snapshot
MDS-FSM: Coverage-Based Frequent Subgraph Mining in Single Graphs
inferred← citeshttps://doi.org/10.3390/math12223590openalex-snapshot
A Formal Fuzzy Concept-Based Approach for Association Rule Discovery with Optimized Time and Storage
inferred← citeshttps://doi.org/10.5753/etc.2026.23128openalex-snapshot
A Concentration Inequality for Multiple Means Based on a Polynomial-Time Computable Metric
inferred← citeshttps://doi.org/10.7717/peerj-cs.2334openalex-snapshot
Quick mining in dense data: applying probabilistic support prediction in depth-first order
inferred← citeshttps://doi.org/10.7717/peerj-cs.2361openalex-snapshot
Dynamic frequent subgraph mining algorithms over evolving graphs: a survey
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