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A survey of pattern mining in dynamic graphs
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Network Motif Discovery Using Subgraph Enumeration and Symmetry-Breaking
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HDB-Subdue: A Scalable Approach to Graph Mining
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Mining Graph Evolution Rules
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Cohesive Co-evolution Patterns in Dynamic Attributed Graphs
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Trend Mining in Dynamic Attributed Graphs
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Finding Frequent Patterns in a Large Sparse Graph*
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Mining Based Time-Series Sleeping Pattern Analysis for Life Big-Data
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Graph based knowledge discovery using MapReduce and SUBDUE algorithm
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Efficient incremental high utility pattern mining based on pre-large concept
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Supergraph based periodic pattern mining in dynamic social networks
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Speedup for a periodic subgraph miner
inferredcites →https://doi.org/10.1016/j.jisa.2017.11.002openalex-snapshot
Internet of Things: A survey on the security of IoT frameworks
inferredcites →https://doi.org/10.1016/j.knosys.2013.10.013openalex-snapshot
Efficient frequent pattern mining based on Linear Prefix tree
inferredcites →https://doi.org/10.1016/j.knosys.2017.12.029openalex-snapshot
Damped window based high average utility pattern mining over data streams
inferredcites →https://doi.org/10.1016/j.knosys.2019.06.005openalex-snapshot
Mining significant trend sequences in dynamic attributed graphs
inferredcites →https://doi.org/10.1016/j.knosys.2019.105241openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.vehcom.2017.01.002openalex-snapshot
VANet security challenges and solutions: A survey
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inferredcites →https://doi.org/10.1017/cbo9781316418321openalex-snapshot
Algorithms and Models for Network Data and Link Analysis
inferredcites →https://doi.org/10.1017/s0269888912000331openalex-snapshot
A survey of frequent subgraph mining algorithms
inferredcites →https://doi.org/10.1023/a:1009748302351openalex-snapshot
Discovery of Frequent Episodes in Event Sequences
inferredcites →https://doi.org/10.1038/srep04331openalex-snapshot
Correction: Corrigendum: Quantum Limit of Quality Factor in Silicon Micro and Nano Mechanical Resonators
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Revealing the Hidden Language of Complex Networks
inferredcites →https://doi.org/10.1093/bib/bbr033openalex-snapshot
Biological network motif detection: principles and practice
inferredcites →https://doi.org/10.1109/access.2016.2553681openalex-snapshot
A Survey on Trajectory Data Mining: Techniques and Applications
inferredcites →https://doi.org/10.1109/access.2019.2926752openalex-snapshot
A Survey of Measures for Network Motifs
inferredcites →https://doi.org/10.1109/asonam.2014.6921577openalex-snapshot
Triggering patterns of topology changes in dynamic graphs
inferredcites →https://doi.org/10.1109/bigdata.2014.7004359openalex-snapshot
FS3: A sampling based method for top-k frequent subgraph mining
inferredcites →https://doi.org/10.1109/cec.2010.5586400openalex-snapshot
A multiobjective variant of the Subdue graph mining algorithm based on the NSGA-II selection mechanism
inferredcites →https://doi.org/10.1109/dsaa.2015.7344810openalex-snapshot
A unifying model for representing time-varying graphs
inferredcites →https://doi.org/10.1109/icde.2018.00241openalex-snapshot
Incremental Frequent Subgraph Mining on Large Evolving Graphs
inferredcites →https://doi.org/10.1109/icdm.2002.1183885openalex-snapshot
Mining molecular fragments: finding relevant substructures of molecules
inferredcites →https://doi.org/10.1109/icdm.2002.1184038openalex-snapshot
gSpan: graph-based substructure pattern mining
inferredcites →https://doi.org/10.1109/icdm.2003.1250974openalex-snapshot
Efficient mining of frequent subgraphs in the presence of isomorphism
inferredcites →https://doi.org/10.1109/icdm.2004.10024openalex-snapshot
GREW-A Scalable Frequent Subgraph Discovery Algorithm
inferredcites →https://doi.org/10.1109/icdm.2004.10084openalex-snapshot
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
inferredcites →https://doi.org/10.1109/icdm.2006.124openalex-snapshot
Pattern Mining in Frequent Dynamic Subgraphs
inferredcites →https://doi.org/10.1109/icdm.2007.92openalex-snapshot
Trend Motif: A Graph Mining Approach for Analysis of Dynamic Complex Networks
inferredcites →https://doi.org/10.1109/icdm.2008.104openalex-snapshot
Mining Periodic Behavior in Dynamic Social Networks
inferredcites →https://doi.org/10.1109/icdm.2008.106openalex-snapshot
A Fast Method to Mine Frequent Subsequences from Graph Sequence Data
inferredcites →https://doi.org/10.1109/icdm.2009.99openalex-snapshot
Constraint-Based Pattern Mining in Dynamic Graphs
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Mining Heavy Subgraphs in Time-Evolving Networks
inferredcites →https://doi.org/10.1109/icdm.2016.0158openalex-snapshot
Detecting Change Processes in Dynamic Networks by Frequent Graph Evolution Rule Mining
inferredcites →https://doi.org/10.1109/icdmw.2011.55openalex-snapshot
Correlation and Contrast Link Formation Patterns in a Time Evolving Graph
inferredcites →https://doi.org/10.1109/icsc.2018.00013openalex-snapshot
Analysing the Evolution of Knowledge Graphs for the Purpose of Change Verification
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Knowledge Acquisition from Multimedia Documents Using Evolving Ontologies
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Edge Computing: Vision and Challenges
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ScaleMine: Scalable Parallel Frequent Subgraph Mining in a Single Large Graph
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Learning Shapelet Patterns from Network-Based Time Series
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Mining sequential patterns by pattern-growth: the PrefixSpan approach
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An Iterative MapReduce Based Frequent Subgraph Mining Algorithm
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A Two-Phase Algorithm for Differentially Private Frequent Subgraph Mining
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Network Motifs: Simple Building Blocks of Complex Networks
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Efficient Substructure Discovery from Large Semi-structured Data
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Mining Frequent Graph Sequence Patterns Induced by Vertices
inferredcites →https://doi.org/10.1145/1014052.1014134openalex-snapshot
A quickstart in frequent structure mining can make a difference
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A taxonomy of sequential pattern mining algorithms
inferredcites →https://doi.org/10.1145/1830252.1830272openalex-snapshot
Frequent subgraph discovery in dynamic networks
inferredcites →https://doi.org/10.1145/1871437.1871468openalex-snapshot
Mining interesting link formation rules in social networks
inferredcites →https://doi.org/10.1145/2431211.2431218openalex-snapshot
Sequential pattern mining -- approaches and algorithms
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Finding time period-based most frequent path in big trajectory data
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The Complexity of Mining Maximal Frequent Subgraphs
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Algorithms for Mining the Coevolving Relational Motifs in Dynamic Networks
inferredcites →https://doi.org/10.1145/2790798.2790799openalex-snapshot
Frequent Subgraph Mining from Streams of Uncertain Data
inferredcites →https://doi.org/10.1145/3007818.3007839openalex-snapshot
Top- k frequent induced subgraph mining using sampling
inferredcites →https://doi.org/10.1145/3035918.3035936openalex-snapshot
Flexible and Feasible Support Measures for Mining Frequent Patterns in Large Labeled Graphs
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Community Discovery in Dynamic Networks
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Graph Summarization Methods and Applications
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Identifying Top- k Nodes in Social Networks
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A Weight-Based Approach: Frequent Graph Pattern Mining with Length-Decreasing Support Constraints Using Weighted Smallest Valid Extension
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Kavosh: a new algorithm for finding network motifs
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MODA: An efficient algorithm for network motif discovery in biological networks
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An in-depth comparison of subgraph isomorphism algorithms in graph databases
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GREWA Scalable Frequent Subgraph Discovery Algorithm
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The Smallest Valid Extension-Based Efficient, Rare Graph Pattern Mining, Considering Length-Decreasing Support Constraints and Symmetry Characteristics of Graphs
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Frequent Subtree Mining - An Overview
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FP-GraphMiner-A Fast Frequent Pattern Mining Algorithm for Network Graphs
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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