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Supporting the Discovery of Relevant Topological Patterns in Attributed Graphs

Julien Salotti, Marc Plantevit, Celine Robardet, Jean-Francois Boulicaut
2012 2012 IEEE 12th International Conference on Data Mining Workshops  
We propose TopGraphVisualizer, a tool to support the discovery of relevant topological patterns in attributed graphs.  ...  A topological pattern is defined as a set of vertex attributes and topological properties (i.e., properties that characterize the role of a vertex within a graph) that strongly co-vary over the vertices  ...  ACKNOWLEDGEMENTS This work is partly supported by the ANR (French Research National Agency) funded project FOSTER ANR-2010-COSI-012-02.  ... 
doi:10.1109/icdmw.2012.38 dblp:conf/icdm/SalottiPRB12 fatcat:b3bwb5fnufe7pfgieva77vjzem

Temporal Patterns Discovery of Evolving Graphs for Graph Neural Network (GNN)-based Anomaly Detection in Heterogeneous Networks

Jongmo Kim, Kunyoung Kim, Gi-Yoon Jeon, Mye M. Sohn
2022 Journal of Internet Services and Information Security  
This paper proposes a new method named evolving-graph generation framework to simultaneously solve the complexity and dynamic nature of the attribute networks that can occur in graph-based anomaly detection  ...  We show that the proposed framework outperforms state-of-the-art methods in the accuracy and stability of training with the trend of decreasing train loss.  ...  Acknowledgements Temporal patterns discovery of evolving graphs for GNN-based anomaly detection J. Kim, K. Kim, G. Jeon, and M.  ... 
doi:10.22667/jisis.2022.02.28.072 dblp:journals/jisis/KimKJS22 fatcat:uncpemjenbgavni65vrnhzszzy

What effects topological changes in dynamic graphs?

Mehdi Kaytoue, Yoann Pitarch, Marc Plantevit, Céline Robardet
2015 Social Network Analysis and Mining  
To describe the dynamics taking place in networks that structurally change over time, we propose an approach to search for vertex attributes whose value changes impact the topology of the graph.  ...  We formalize the triggering pattern discovery problem as a method jointly rooted in sequence mining and graph analysis.  ...  In [27] , the authors propose to mine the graph topology of a large attributed graph by finding regularities among vertex descriptors that are of two types: vertex attributes and topological properties  ... 
doi:10.1007/s13278-015-0294-9 fatcat:fjlzvtmoyzespb6chauerwf2cy

Spatial association discovery process using frequent subgraph mining

Giovanni Daián Rottoli, Hernán Merlino
2020 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
Spatial associations are one of the most relevant kinds of patterns used by business intelligence regarding spatial data.  ...  situation, particularly for small and medium-size projects to guide the useful pattern discovery process.  ...  ACKNOWLEDGEMENT The research presented in this paper was partially funded by the PhD Scholarship Program to reinforce R&D&I areas (2016-2020) of the Universidad Tecnológica Nacional and the Research Project  ... 
doi:10.12928/telkomnika.v18i4.13858 fatcat:zoony5coifdvnkd6ukcwynrgly

Scalable graph exploration and visualization: Sensemaking challenges and opportunities

Robert Pienta, James Abello, Minsuk Kahng, Duen Horng Chau
2015 2015 International Conference on Big Data and Smart Computing (BIGCOMP)  
We ground our discussion in sensemaking research; we propose a new graph sensemaking hierarchy that categorizes tools and techniques based on how they operate on the graph data (e.g., local vs global).  ...  Different from existing surveys, our investigation highlights approaches that have strong potential in handling large graphs, algorithmically, visually, or interactively; we also explicitly connect relevant  ...  Structural & Topological Navigation Topological navigation uses the graphs structure to show and hide portions of the graph based on the connections between nodes.  ... 
doi:10.1109/35021bigcomp.2015.7072812 dblp:conf/bigcomp/PientaAKC15 fatcat:7fk36dc4wnc7niog5ej7hz6rpe

Knowledge Discovery in Spatial Databases [chapter]

Martin Ester, Hans-Peter Kriegel, Jörg Sander
1999 Informatik aktuell  
The major difference between knowledge discovery in relational databases and in spatial databases is that attributes of the neighbors of some object of interest may have an influence on the object itself  ...  Knowledge discovery in databases (KDD) is the process of discovering valid, novel and potentially useful patterns from large databases.  ...  Introduction Knowledge discovery in databases (KDD) has been defined as the process of discovering valid, novel, and potentially useful patterns from data [9] .  ... 
doi:10.1007/978-3-642-60243-6_1 dblp:conf/dagm/EsterKS99 fatcat:vtrvgb7wpjgszjoslr2cwsmxdy

Analyzing Multi-level Spatial Association Rules Through a Graph-Based Visualization [chapter]

Annalisa Appice, Paolo Buono
2005 Lecture Notes in Computer Science  
In this paper we present a graph-based visualization that supports data miners in the analysis of multi-level spatial association rules discovered by ARES and takes advantage from hierarchies describing  ...  Association rules discovery is a fundamental task in spatial data mining where data are naturally described at multiple levels of granularity.  ...  The same rule is highlighted in the graph G 15 by filtering with respect Fig. 3 . Visualizing the graph of spatial association rules using ARVis to increasing value of support.  ... 
doi:10.1007/11504894_63 fatcat:cyktgqqfg5frpnsru6d64j7ib4

Knowledge Discovery in Spatial Databases [chapter]

Martin Ester, Hans-Peter Kriegel, Jörg Sander
1999 Lecture Notes in Computer Science  
The major difference between knowledge discovery in relational databases and in spatial databases is that attributes of the neighbors of some object of interest may have an influence on the object itself  ...  Knowledge discovery in databases (KDD) is the process of discovering valid, novel and potentially useful patterns from large databases.  ...  Introduction Knowledge discovery in databases (KDD) has been defined as the process of discovering valid, novel, and potentially useful patterns from data [9] .  ... 
doi:10.1007/3-540-48238-5_5 fatcat:vdgxesmc3fhnxb3gdtiyfzy2oa

Structural correlation pattern mining for large graphs

Arlei Silva, Wagner Meira, Mohammed J. Zaki
2010 Proceedings of the Eighth Workshop on Mining and Learning with Graphs - MLG '10  
In this paper we define the Structural Correlation Pattern (SCP) mining problem, which consists of determining correlations among vertex attributes and dense components in an undirected graph.  ...  Vertex attributes play an important role in several real-life graphs and SCPs help to understand how they relate to the associated graph topology.  ...  One example of a relevant topology pattern is a densely connected set of vertices in a graph.  ... 
doi:10.1145/1830252.1830268 dblp:conf/mlg/SilvaMZ10 fatcat:gmjrko7csjfybn6tlpl5tlwtn4

Introduction to the special issue on link mining

Lise Getoor, Christopher P. Diehl
2005 SIGKDD Explorations  
These range from capturing descriptive patterns in the link structure and predicting emerging links to discovery of clusters or communities based on attributes and link structure.  ...  The next paper by Sun et al. investigates the problems of relevance search and anomaly detection in bipartite graphs.  ... 
doi:10.1145/1117454.1117455 fatcat:7gz6oxo535eypnphkfwb6nippe

Mining Graph Topological Patterns: Finding Covariations among Vertex Descriptors

Adriana Prado, Marc Plantevit, Celine Robardet, Jean-Francois Boulicaut
2013 IEEE Transactions on Knowledge and Data Engineering  
An efficient algorithm that combines searching and pruning strategies in the identification of the most relevant topological patterns is presented.  ...  In this article, we propose to mine the graph topology of a large attributed graph by finding regularities among vertex descriptors.  ...  Existing methods that support the discovery of local patterns in graphs mainly focus on the topological structure of the patterns, by extracting specific subgraphs while ignoring the vertex properties  ... 
doi:10.1109/tkde.2012.154 fatcat:2b3odgo4efdjfic4eq7mg3tlpi

Mining Attribute-structure Correlated Patterns in Large Attributed Graphs [article]

Arlei Silva, Wagner Meira Jr., Mohammed J. Zaki
2012 arXiv   pre-print
In this work, we study the correlation between attribute sets and the occurrence of dense subgraphs in large attributed graphs, a task we call structural correlation pattern mining.  ...  An efficient algorithm that combines search and pruning strategies in the identification of the most relevant structural correlation patterns is presented.  ...  Acknowledgements: This work was supported by CNPQ, CAPES, Fapemig, FINEP, InWeb, NSF award EMT-0829835, and NIH award 1R01EB0080161.  ... 
arXiv:1201.6568v1 fatcat:hmayyup6kbftriupfjoycw64n4

Mining attribute-structure correlated patterns in large attributed graphs

Arlei Silva, Wagner Meira, Mohammed J. Zaki
2012 Proceedings of the VLDB Endowment  
In this work, we study the correlation between attribute sets and the occurrence of dense subgraphs in large attributed graphs, a task we call structural correlation pattern mining.  ...  An efficient algorithm that combines search and pruning strategies in the identification of the most relevant structural correlation patterns is presented.  ...  Acknowledgements: This work was supported by CNPQ, CAPES, Fapemig, FINEP, InWeb, NSF award EMT-0829835, and NIH award 1R01EB0080161.  ... 
doi:10.14778/2140436.2140443 fatcat:edg6x4qdsjg7fhnj75hjeqyir4

Multiplex Graph Association Rules for Link Prediction [article]

Michele Coscia, Michael Szell
2020 arXiv   pre-print
We derive such rules by identifying all frequent patterns in a network via multiplex graph mining, and then score each unobserved link's likelihood by finding the occurrences of each rule in the original  ...  Link prediction is the branch of network analysis allowing us to forecast the future status of a network: which new connections are the most likely to appear in the future?  ...  In this setting, the frequency (or support) is the number of graphs in the database containing the pattern.  ... 
arXiv:2008.08351v1 fatcat:kkdh56qo4fhxhbjpdbmnbxkd7m

Graph BI & Analytics: Current State and Future Challenges [chapter]

Amine Ghrab, Oscar Romero, Salim Jouili, Sabri Skhiri
2018 Lecture Notes in Computer Science  
This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs.  ...  We survey the topics of graph modeling, management, processing and analysis in graph warehouses.  ...  These techniques are relevant in the BI context as they reveal interesting properties about the topology and the connectivity between business entities.  ... 
doi:10.1007/978-3-319-98539-8_1 fatcat:56fgfclobveifcbkwvbyejt5pi
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