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Exploring Large Graph Mining: Tools, Techniques, and Applications for Practitioners

"Large Graph Mining: Power Tools and a Practitioner’s Guide" by Christos Faloutsos, Gary Miller, and Charalampos Tsourakakis offers a comprehensive overview of graph mining's significance and methodologies. The book covers various tasks including node importance, community detection, recommendations, subgraph connections, time-based mining, and influence propagation. It emphasizes real-world applications, illustrating the relevance of graph theory in diverse fields such as social networks, cybersecurity, and biological interactions. The authors provide practical tools, insights, and conclusions to navigate the complexities of large graph analysis.

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Exploring Large Graph Mining: Tools, Techniques, and Applications for Practitioners

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  1. Large Graph Mining:Power Tools and a Practitioner’s guide Christos Faloutsos Gary Miller Charalampos (Babis) Tsourakakis CMU Faloutsos, Miller, Tsourakakis

  2. Outline Introduction – Motivation Task 1: Node importance Task 2: Community detection Task 3: Recommendations Task 4: Connection sub-graphs Task 5: Mining graphs over time Task 6: Virus/influence propagation Task 7: Spectral graph theory Task 8: Tera/peta graph mining: hadoop Observations – patterns of real graphs Conclusions Faloutsos, Miller, Tsourakakis

  3. Graphs - why should we care? Internet Map [lumeta.com] Food Web [Martinez ’91] Protein Interactions [genomebiology.com] Friendship Network [Moody ’01] Faloutsos, Miller, Tsourakakis

  4. T1 D1 ... ... DN TM Graphs - why should we care? • IR: bi-partite graphs (doc-terms) • web: hyper-text graph • ... and more: Faloutsos, Miller, Tsourakakis

  5. Graphs - why should we care? • network of companies & board-of-directors members • ‘viral’ marketing • web-log (‘blog’) news propagation • computer network security: email/IP traffic and anomaly detection • .... • Any M:N relationship -> Graph • Any subject-verb-object construct: -> Graph Faloutsos, Miller, Tsourakakis

  6. Graphs and matrices • Closely related • Powerful tools from matrix algebra, for graph mining • (and, conversely, great problems for CS/Math to solve) Faloutsos, Miller, Tsourakakis

  7. 0 11 22 55 ... 5 0 6 7 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... John Peter Mary Nick ... Examples of Matrices: Graph - social network Peter Mary Nick John ... Faloutsos, Miller, Tsourakakis

  8. John Peter Mary Nick ... Examples of Matrices:Market basket • market basket as in Association Rules milk bread choc. wine ... Faloutsos, Miller, Tsourakakis

  9. Examples of Matrices:Documents and terms data mining classif. tree ... Paper#1 Paper#2 Paper#3 Paper#4 ... Faloutsos, Miller, Tsourakakis

  10. data mining classif. tree ... John Peter Mary Nick ... Examples of Matrices:Authors and terms Faloutsos, Miller, Tsourakakis

  11. Examples without networks • (or, at least, no straightforward network interpretation) Faloutsos, Miller, Tsourakakis

  12. John Peter Mary Nick ... Examples of Matrices:cloud of n-d points age chol# blood# .. ... Faloutsos, Miller, Tsourakakis

  13. Examples of Matrices:sensor-ids and time-ticks temp1 temp2 humid. pressure ... t1 t2 t3 t4 ... Faloutsos, Miller, Tsourakakis

  14. Outline Introduction – Motivation Task 1: Node importance Task 2: Community detection … Conclusions Faloutsos, Miller, Tsourakakis

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