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Graph Mining
[chapter]

2013
*
Encyclopedia of Systems Biology
*

Network analysis, Learning from

doi:10.1007/978-1-4419-9863-7_615
fatcat:2oah4qdrqvhahfdviu5wpomy5i
*graph*structured data. Definition*Graph**mining*is the study of how to perform data*mining*and machine learning on data represented with*graphs*. ... One can distinguish between on the one hand transactional*graph**mining*, where a database of separate, independent*graphs*is considered (such as databases of molecules and databases of images), and on the ... Transactional*graph**mining*methods*Graph**mining*methods cover the whole range of methods from data*mining*and machine learning. ...##
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Graph mining

2008
*
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement conference - IMC '08
*

Faloutsos
21
Motivation
Data

doi:10.1145/1452520.1452521
dblp:conf/imc/Faloutsos08
fatcat:wllx44xa7bgofn3i7cqcrycpfe
*mining*: ~ find patterns (rules, outliers) • Problem#1: How do real*graphs*look like? • Problem#2: How do they evolve? ... projects (Virus propagation, e-bay fraud detection) • Conclusions Motivation Data*mining*: ~ find patterns (rules, outliers) • Problem#1: How do real*graphs*look like? ...##
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Graph mining

2006
*
ACM Computing Surveys
*

Indeed, any M : N relation in database terminology can be represented as a

doi:10.1145/1132952.1132954
fatcat:jbc67scdzbe4vj5o5fjerdy6xq
*graph*. A lot of these questions boil down to the following: "How can we generate synthetic but realistic*graphs*?" ... Further, we briefly describe recent advances on some related and interesting*graph*problems. ... Here, we only point out differences from*Graph**Mining*. ...##
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Graph Mining and Graph Kernels

2008
*
Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08
*

Various groups within the KDD community have begun to study the task of data

doi:10.1145/1401890.1551565
fatcat:xpx2t6gq4ngcvisvqblh4bnwb4
*mining*on*graphs*, including researchers from database-oriented*graph**mining*, and researchers from kernel machine learning. ... The goal of this tutorial is (i) to introduce newcomers to the field of*graph**mining*, (ii) to introduce people with database background to*graph**mining*using kernel machines, (iii) to introduce people ...*Graph**Mining*• Frequent*graph*pattern*mining*• Contrast*graph*pattern*mining** • Constrained*graph*pattern*mining*• Optimal*graph*pattern*mining** •*Graph**mining*in single*graphs** •*Graph*pattern summarization ...##
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Predictive Graph Mining
[chapter]

2004
*
Lecture Notes in Computer Science
*

*Graph*

*mining*approaches are extremely popular and effective in molecular databases. ... Even though SMIREP is focused on SMILES, its principles are also applicable to

*graph*

*mining*problems in other domains. SMIREP is experimentally evaluated on two benchmark databases. ... Hence we are convinced, that SMIREP can also be applied to other types of predictive

*graph*

*mining*, e.g. in the

*mining*of messenger RNAs [20] . ...

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Large graph mining

2014
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Proceedings of the 23rd international conference on World wide web - WWW '14
*

We focus on three topics: (a) anomaly detection in large static

doi:10.1145/2566486.2576889
dblp:conf/www/Faloutsos14
fatcat:p4wj5y2dm5csnag4pqpvu356vi
*graphs*(b) patterns and anomalies in large time-evolving*graphs*and (c) cascades and immunization. ... Given a large*graph*, like who-calls-whom, or who-likes-whom, what behavior is normal and what should be surprising, possibly due to fraudulent activity? How do*graphs*evolve over time? ... We conclude with some open research questions for*graph**mining*. (2010) , nineteen "best paper" awards (including two "test of time" awards), and four teaching awards. ...##
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Big graph mining

2013
*
SIGKDD Explorations
*

How do we find patterns and anomalies in very large

doi:10.1145/2481244.2481249
fatcat:fzidqzmctndj3nxh2qw55txyuu
*graphs*with billions of nodes and edges? How to*mine*such big*graphs*efficiently? ... In this paper we describe Pegasus, a big*graph**mining*system built on top of MapReduce, a modern distributed data processing platform. ... The first is the design of scalable*graph**mining*algorithms in MapReduce. ...##
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Visual Graph Mining
[article]

2017
*
arXiv
*
pre-print

theoretical basis of

arXiv:1708.03921v1
fatcat:jzhldngcl5gu5fgu5fjgvj42j4
*graph**mining*designed for tabular data. ... In this study, we redefine the visual subgraph pattern that encodes all of these challenges in a general way, and propose an approximate but efficient solution to*graph**mining*. ... Introduction*Graph**mining*is a classical field in data*mining*. To ease the*mining*process, pioneering techniques generally*mined*tabular data in which*graphs*contain distinct node and edge labels. ...##
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Fair Graph Mining

2021
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Proceedings of the 30th ACM International Conference on Information & Knowledge Management
*

Fairness on

doi:10.1145/3459637.3482030
fatcat:fzg6nb56cjcird7vfkcviqxtni
*graph**mining*aims to develop strategies in order to mitigate bias introduced/amplified during the*mining*process. ...*mining*, and (2) future directions in studying algorithmic fairness on*graphs*. ... His research interest is in large scale data*mining*for*graphs*and multimedia. ...##
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Mining billion-node graphs

2011
*
Proceedings of the fourth ACM international conference on Web search and data mining - WSDM '11
*

Faloutsos (CMU)
88
PEGASUS: A Peta-Scale

doi:10.1145/1935826.1935837
dblp:conf/wsdm/Faloutsos11
fatcat:xho2gkxcyrel3go66pzgya4qnm
*Graph**Mining*System -Implementation and Observations. U Kang, Charalampos E. Tsourakakis, and Christos Faloutsos. (ICDM) 2009, Miami, Florida, USA. ... Faloutsos (CMU) 21 Outline • Introduction -Motivation • Problem#1: Patterns in*graphs*-Static*graphs*• degree, diameter, eigen, • triangles • cliques -Weighted*graphs*-Time evolving*graphs*...##
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Taxonomy-superimposed graph mining

2008
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Proceedings of the 11th international conference on Extending database technology Advances in database technology - EDBT '08
*

Hence, standard

doi:10.1145/1353343.1353372
dblp:conf/edbt/CakmakO08
fatcat:nrgckfyiwjdapp7jwc6qzn7xmq
*graph**mining*techniques are not directly applicable. ... In this paper, we present Taxogram, a taxonomy-superimposed*graph**mining*algorithm that can efficiently discover frequent*graph*structures in a database of taxonomy-superimposed*graphs*. ... The synthetic*graph*generator expects a label taxonomy, maximum node and edge ...##
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Graph Mining on Streams
[chapter]

2009
*
Encyclopedia of Database Systems
*

Such a stream naturally defines an undirected, unweighted

doi:10.1007/978-0-387-39940-9_184
fatcat:7lcaeiin7zehvef7ltistncg4m
*graph**Graph**mining*on streams is concerned with estimating properties of G, or finding patterns within G, given the usual constraints of the data-stream ... SYNONYMS*Graph*Streams; Semi-Streaming Model DEFINITION Consider a data stream A = a 1 , a 2 , . . . , a m where each data item a k ∈ [n] × [n]. ... Multi-Pass Models: It is common in*graph**mining*to consider algorithms that may take more than one pass over the stream. ...##
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Graph-based data mining

2000
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IEEE Intelligent Systems and their Applications
*

The substructure discovery method is the basis of Subdue, which performs data

doi:10.1109/5254.850825
fatcat:uhmbej7osncgndxkc7rbyvvtmi
*mining*on databases represented as*graphs*. ... Two articles deal with text*mining*. ...##
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Taxonomy-superimposed graph mining

2008
*
Proceedings of the 11th international conference on Extending database technology Advances in database technology - EDBT '08
*

Hence, standard

doi:10.1145/1352431.1352460
fatcat:xktyuctuq5cjzkjkqs4r5gocc4
*graph**mining*techniques are not directly applicable. ... In this paper, we present Taxogram, a taxonomy-superimposed*graph**mining*algorithm that can efficiently discover frequent*graph*structures in a database of taxonomy-superimposed*graphs*. ... The synthetic*graph*generator expects a label taxonomy, maximum node and edge ...##
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Graph-based process mining
[article]

2020
*
arXiv
*
pre-print

It defines an algorithm to compute Directly Follows

arXiv:2007.09352v1
fatcat:5x53mwptjzf3xphtobscty3mjq
*Graph*(DFG) inside the*graph*database, which shifts the heavy computation parts of process*mining*into the*graph*database. ... Calculating DFG in*graph*databases enables leveraging the*graph*databases' horizontal and vertical scaling capabilities in favor of applying process*mining*on a large scale. ...*Graph*database There are different attempts to use*graph*databases with process*mining*. ...
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