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Mining frequent neighborhood patterns in a large labeled graph

Jialong Han, Ji-Rong Wen
2013 Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13  
This paper targets mining patterns in the single-graph setting. We resolve the "DCP-intuitiveness" dilemma by shifting the mining target from frequent subgraphs to frequent neighborhoods.  ...  Experiments on real-life datasets display the feasibility of our algorithms on relatively large graphs, as well as the capability of mining interesting knowledge that is not discovered in prior works.  ...  RELATED WORK Frequent Subgraph Mining The frequent subgraph mining problem is well-investigated by the literatures under the graph-transaction setting.  ... 
doi:10.1145/2505515.2505530 dblp:conf/cikm/HanW13 fatcat:wlmggp5jezho3b2hj7z3uqinim

Frequent subgraph pattern mining on uncertain graph data

Zhaonian Zou, Jianzhong Li, Hong Gao, Shuo Zhang
2009 Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09  
This paper investigates the problem of mining frequent subgraph patterns from uncertain graph data.  ...  An approximate mining algorithm is proposed to find an approximate set of frequent subgraph patterns by allowing an error tolerance on the expected supports of the discovered subgraph patterns.  ...  To the best of our knowledge, there is no literature to date on mining frequent subgraph patterns from uncertain graph data. This paper is the first one to investigate this problem.  ... 
doi:10.1145/1645953.1646028 dblp:conf/cikm/ZouLGZ09 fatcat:oqeaypns5rga7f2nsogb7am2ey

Mining Frequent Patterns in Evolving Graphs

Cigdem Aslay, Muhammad Anis Uddin Nasir, Gianmarco De Francisci Morales, Aristides Gionis
2018 Proceedings of the 27th ACM International Conference on Information and Knowledge Management - CIKM '18  
Given a labeled graph, the frequent-subgraph mining (FSM) problem asks to nd all the k-vertex subgraphs that appear with frequency greater than a given threshold.  ...  For each streaming se ing, we propose algorithms that can extract a high-quality approximation of the frequent k-vertex subgraphs for a given threshold, at any given time instance, with high probability  ...  Apart from the exact mining algorithms, a line of work focused on approximate mining of frequent subgraphs in a given static graph.  ... 
doi:10.1145/3269206.3271772 dblp:conf/cikm/AslayNMG18 fatcat:uayx53rpmjcivitqlg5wzewdaa

A conversation with Professor Jianzhong Li

Jianzhong Li
2012 SIGKDD Explorations  
He has also served on the editorial boards for distinguished journals, including Knowledge and Data Engineering, and refereed papers for varied journals and proceedings.  ...  He has authored three books and published more than 200 papers in refereed journals and conference proceedings, such as VLDB Journal, Algorithmica, IEEE Transactions on Knowledge and Data Engineering,  ...  and the problem of mining frequent subgraph patterns is formally stated on uncertain graph data under the probabilistic semantics on the basis of ϕ-frequent probability.  ... 
doi:10.1145/2207243.2207258 fatcat:xflqiph5o5bnxaybhs22sfdy3i

A Survey on Assorted Approaches to Graph Data Mining

D. Kavitha, B.V. Manikyala Rao, V.Kishore Babu
2011 International Journal of Computer Applications  
These are used to extract patterns, trends, classes, and clusters from graphs.  ...  We investigate recurring patterns in real-world graphs, to gain a deeper understanding of their structure.  ...  Among various kinds of graph patterns, frequent substructures are very basic ones that can be discovered in a set of graphs.  ... 
doi:10.5120/1806-2294 fatcat:nmup6n2fkzbqxcc67af6u4dnui

Mining closed relational graphs with connectivity constraints

Xifeng Yan, X. Jasmine Zhou, Jiawei Han
2005 Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining - KDD '05  
Since frequent graph mining usually generates too many patterns, it is more appealing to mine closed frequent graphs only [4] .  ...  Through our study, a new research area in frequent graph mining is exposed, where the previous algorithms on single graph mining should be re-examined for pattern discovery in multiple relational graphs  ... 
doi:10.1145/1081870.1081908 dblp:conf/kdd/YanZH05 fatcat:ozvmwpxaujgttixnsfq6blb6eu

cgSpan: Pattern Mining in Conceptual Graphs

Adam Faci
2021 arXiv   pre-print
Conceptual Graphs (CGs) are a graph-based knowledge representation formalism. In this paper we propose cgSpan a CG frequent pattern mining algorithm.  ...  rules, applying them during the pattern mining process.  ...  cgSpan: a CG Frequent Pattern Mining Algorithm cgSpan is the first frequent pattern mining method running on Conceptual Graphs, to the best of our knowledge.  ... 
arXiv:2110.15058v1 fatcat:ddgdc7phlfgytfhwa77egwxaam

Mining Summaries for Knowledge Graph Search

Qi Song, Yinghui Wu, Xin Luna Dong
2016 2016 IEEE 16th International Conference on Data Mining (ICDM)  
Kalnis.GRAMI: frequent subgraph and pattern mining in a single large graph.  ...  graph summarization 5/11 Informativeness Difference approximation algorithm approxDis: • Mining frequent patterns based on d-similarity • Calculate pair-wise score and select top score pairs  ... 
doi:10.1109/icdm.2016.0162 dblp:conf/icdm/SongWD16 fatcat:xnmpqxkgyrdkjkh6hlzkvsttaq

A New Web Usage Mining Approach for Website Recommendations Using Concept Hierarchy and Website Graph

T. Vijaya Kumar, H. S. Guruprasad, Bharath Kumar K. M., Irfan Baig, Kiran Babu S.
2014 International Journal of Computer and Electrical Engineering  
Along with the server access log file, we incorporate Website knowledge (i.e., Concept hierarchy and Website Graph) into the web usage mining phases. This incorporation can lead to superior patterns.  ...  Index Terms-Concept based website graph, concept hierarchy, web mining, web usage mining, website graph.  ...  It involves usual steps of Web Usage Mining such as Data Gathering, Preprocessing, Pattern Discovery, and Pattern Analysis. Our model incorporates website knowledge in web usage mining techniques.  ... 
doi:10.7763/ijcee.2014.v6.796 fatcat:wnqo7evkgbeqdft7ld3pdcjbpi

Information retrieval and knowledge discovery on the semantic web of traditional chinese medicine

Zhaohui Wu, Tong Yu, Huajun Chen, Xiaohong Jiang, Yi Feng, Yuxin Mao, Heng Wang, Jingming Tang, Chunying Zhou
2008 Proceeding of the 17th international conference on World Wide Web - WWW '08  
mining methodology is implemented for discovering and interpreting interesting patterns from this network.  ...  relational databases to the Semantic Web layer for query and search across database boundaries; the first global herb-drug interaction network is mapped through semantic integration, and the semantic graph  ...  We presented an in-use Semantic Web platform supporting large-scale database integration, information retrieval, and knowledge discovery for Traditional Chinese Medicine domain.  ... 
doi:10.1145/1367497.1367668 dblp:conf/www/WuYCJFMWTZ08 fatcat:csq7geprxzdgtmsv3yhkxd22gu

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.  ...  Social and biological networks have led to a huge interest in data analysis on graphs.  ...  Graph MiningFrequent 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

A Comparative Study Of Frequent Subgraph Mining Algorithms

K Lakshmi
2012 International Journal of Advanced Information Technology  
Mining patterns from graph databases is challenging since graph related operations, such as subgraph testing, generally have higher time complexity than the corresponding operations on itemsets, sequences  ...  In this paper we present a detailed survey on frequent subgraph mining algorithms, which are used for knowledge discovery in complex objects and also propose a frame work for classification of these algorithms  ...  approximate network patterns, which is the key for many knowledge discovery applications on structural data, but also enriches the library of graph mining methodologies by introducing several novel techniques  ... 
doi:10.5121/ijitcs.2012.2203 fatcat:qpsc44az4nhafmuekhbngp2eci

Mining Substructures in Protein Data

Fedja Hadzic, Tharam S. Dillon, Amandeep S. Sidhu, Elizabeth Chang, Henry Tan
2006 Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)  
Obtaining the set of frequent induced subtrees from the Prions database can potentially reveal some useful knowledge.  ...  In this work we apply the algorithm to the Prions database in order to extract the frequently occurring patterns, which in this case are of induced subtree type.  ...  Frequent pattern analysis has been a focused theme of study in data mining, and a lot of algorithms and methods have been developed for mining frequent patterns, sequential patterns and structural patterns  ... 
doi:10.1109/icdmw.2006.114 dblp:conf/icdm/HadzicDSCT06 fatcat:7ebzkdbbnvg2pd7uiqhxhbbqai

Indexing and mining topological patterns for drug discovery

Sayan Ranu, Ambuj K. Singh
2012 Proceedings of the 15th International Conference on Extending Database Technology - EDBT '12  
As a result, the problem of indexing and mining structural patterns map to indexing and mining patterns from graph and 3D geometric databases.  ...  Next, we will introduce the problem of mining frequent subgraph patterns along with some of their limitations that ignited the interest in the problem of mining statistically significant subgraph patterns  ...  The tutorial will highlight one key weakness in frequent subgraph patterns that resulted in much enthusiasm towards solving the problem of mining statistically significant subgraph patterns.  ... 
doi:10.1145/2247596.2247666 dblp:conf/edbt/RanuS12 fatcat:eik3d5yoxnauzjeukeb6uhdm2u

Discovering Complex Knowledge in Massive Building Operational Data Using Graph Mining for Building Energy Management

Cheng Fan, Mengjie Song, Fu Xiao, Xue Xue
2019 Energy Procedia  
., graph generation based on building operational data and knowledge discovery from graph data.  ...  ., graph generation based on building operational data and knowledge discovery from graph data.  ...  Frequent subgraph mining (FSM) is one of the most essential graph mining techniques. It mainly works on undirected graphs with labelled vertices and edges.  ... 
doi:10.1016/j.egypro.2019.01.378 fatcat:a2e4e7eirbddjmu726qzltaxym
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