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Graph Mining [chapter]

Jan Ramon
2013 Encyclopedia of Systems Biology  
Network analysis, Learning from 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.  ... 
doi:10.1007/978-1-4419-9863-7_615 fatcat:2oah4qdrqvhahfdviu5wpomy5i

Graph mining

Christos Faloutsos
2008 Proceedings of the 8th ACM SIGCOMM conference on Internet measurement conference - IMC '08  
Faloutsos 21 Motivation Data 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?  ... 
doi:10.1145/1452520.1452521 dblp:conf/imc/Faloutsos08 fatcat:wllx44xa7bgofn3i7cqcrycpfe

Graph mining

Deepayan Chakrabarti, Christos Faloutsos
2006 ACM Computing Surveys  
Indeed, any M : N relation in database terminology can be represented as a 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.  ... 
doi:10.1145/1132952.1132954 fatcat:jbc67scdzbe4vj5o5fjerdy6xq

Graph Mining and Graph Kernels

Karsten Michael Borgwardt, Xifeng Yan
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 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  ... 
doi:10.1145/1401890.1551565 fatcat:xpx2t6gq4ngcvisvqblh4bnwb4

Predictive Graph Mining [chapter]

Andreas Karwath, Luc De Raedt
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] .  ... 
doi:10.1007/978-3-540-30214-8_1 fatcat:i4i4esud35ebtlctfb7yzoazrq

Large graph mining

Christos Faloutsos
2014 Proceedings of the 23rd international conference on World wide web - WWW '14  
We focus on three topics: (a) anomaly detection in large static 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.  ... 
doi:10.1145/2566486.2576889 dblp:conf/www/Faloutsos14 fatcat:p4wj5y2dm5csnag4pqpvu356vi

Big graph mining

U. Kang, Christos Faloutsos
2013 SIGKDD Explorations  
How do we find patterns and anomalies in very large 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.  ... 
doi:10.1145/2481244.2481249 fatcat:fzidqzmctndj3nxh2qw55txyuu

Visual Graph Mining [article]

Quanshi Zhang, Xuan Song, Ryosuke Shibasaki
2017 arXiv   pre-print
theoretical basis of 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.  ... 
arXiv:1708.03921v1 fatcat:jzhldngcl5gu5fgu5fjgvj42j4

Fair Graph Mining

Jian Kang, Hanghang Tong
2021 Proceedings of the 30th ACM International Conference on Information & Knowledge Management  
Fairness on 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.  ... 
doi:10.1145/3459637.3482030 fatcat:fzg6nb56cjcird7vfkcviqxtni

Mining billion-node graphs

Christos Faloutsos
2011 Proceedings of the fourth ACM international conference on Web search and data mining - WSDM '11  
Faloutsos (CMU) 88 PEGASUS: A Peta-Scale 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  ... 
doi:10.1145/1935826.1935837 dblp:conf/wsdm/Faloutsos11 fatcat:xho2gkxcyrel3go66pzgya4qnm

Taxonomy-superimposed graph mining

Ali Cakmak, Gultekin Ozsoyoglu
2008 Proceedings of the 11th international conference on Extending database technology Advances in database technology - EDBT '08  
Hence, standard 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  ... 
doi:10.1145/1353343.1353372 dblp:conf/edbt/CakmakO08 fatcat:nrgckfyiwjdapp7jwc6qzn7xmq

Graph Mining on Streams [chapter]

Linda L. Hill, Mehmet M. Dalkiliç, Brahim Medjahed, Mourad Ouzzani, Ahmed K. Elmagarmid, Joseph M. Hellerstein, Colin R. Reeves, Christopher B. Jones, Ross S. Purves, Michael F. Goodchild, Jayant Sharma, John Herring (+24 others)
2009 Encyclopedia of Database Systems  
Such a stream naturally defines an undirected, unweighted 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.  ... 
doi:10.1007/978-0-387-39940-9_184 fatcat:7lcaeiin7zehvef7ltistncg4m

Graph-based data mining

D.J. Cook, L.B. Holder
2000 IEEE Intelligent Systems and their Applications  
The substructure discovery method is the basis of Subdue, which performs data mining on databases represented as graphs.  ...  Two articles deal with text mining.  ... 
doi:10.1109/5254.850825 fatcat:uhmbej7osncgndxkc7rbyvvtmi

Taxonomy-superimposed graph mining

Ali Cakmak, Gultekin Ozsoyoglu
2008 Proceedings of the 11th international conference on Extending database technology Advances in database technology - EDBT '08  
Hence, standard 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  ... 
doi:10.1145/1352431.1352460 fatcat:xktyuctuq5cjzkjkqs4r5gocc4

Graph-based process mining [article]

Amin Jalali
2020 arXiv   pre-print
It defines an algorithm to compute Directly Follows 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.  ... 
arXiv:2007.09352v1 fatcat:5x53mwptjzf3xphtobscty3mjq
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