Graph-Powered Machine Learning
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Meetup in Prague (CZ), 31st May 2018 Abstract: Graph-based machine learning is becoming an important trend in Artificial Intelligence, transcending other techniques and technologies. Using graphs as a basic representation of data for ML purposes has several advantages: (i) the data is already modeled for further analysis, explicitly representing connections and relationships between “things” and concepts; (ii) graphs can easily combine multiple sources into a single graph representation and iteratively enhance/learn over them, creating Knowledge Graphs; (iii) improving computation performance and quality. The talk will discuss these advantages and present applications in the context of recommendation engines and natural language processing. Bio: Dr. Alessandro Negro (https://twitter.com/alessandronegro?lang=en) is Chief Scientist at GraphAware. He has been a long-time member of the graph community and is the main author of the original Neo4j-based recommendation engine. At GraphAware, Alessandro specializes in recommendation engines, graph-aided search, and NLP.
![WHAT IS MACHINE LEARNING?
GraphAware®
[Machine Learning is the] field of study that gives
computers the ability to learn without being explicitly
programmed.
— Arthur Samuel, 1959](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Graph-Powered Machine Learning
- 1. GraphAware® GRAPH POWERED MACHINE LEARNING Alessandro Negro, Chief Scientist @ GraphAware graphaware.com @graph_aware, @AlessandroNegro
- 2. WHAT IS MACHINE LEARNING? GraphAware® [Machine Learning is the] field of study that gives computers the ability to learn without being explicitly programmed. — Arthur Samuel, 1959
- 3. MACHINE LEARNING CHALLENGES The Source of Truth Performance Model Storing Real Time
- 4. THE SOURCE OF TRUTH GraphAware® “Even the best learning algorithm on wrong data produces wrong results” — Alessandro Negro, 2015
- 5. THE SOURCE OF TRUTH GraphAware® Source: Michele Banko and Eric Brill Scaling to Very Very Large Corpora for Natural Language Disambiguation - 2001
- 6. Predictive accuracy Training Performance Prediction Performance PERFOMANCE GraphAware®
- 7. Store the results of the training phase Provide multiple access pattern Mix models The size depends on the algorithm STORING THE MODEL GraphAware®
- 8. LEARN FAST PREDICT FAST REAL TIME
- 9. WHAT IS A GRAPH? GraphAware®
- 10. WHAT IS A GRAPH? GraphAware® G = (V, E)
- 11. WHAT IS A GRAPH? GraphAware®
- 12. WHAT IS A GRAPH? GraphAware®
- 14. WHY NEO4J? GraphAware® It is a proper graph database
- 15. WHY NEO4J? GraphAware® It is a proper graph database It is a proper database
- 16. GraphAware®
- 17. GraphAware®
- 18. STORING DATA SOURCES: TENSOR GraphAware®
- 19. STORING DATA SOURCES: TENSOR GraphAware® Simple Recommendation f: User x Item -> Relevance Score
- 20. STORING DATA SOURCES: TENSOR GraphAware® Simple Recommendation f: User x Item -> Relevance Score
- 21. STORING DATA SOURCES: TENSOR GraphAware® Simple Recommendation f: User x Item -> Relevance Score Context Aware Recommendation f: User x Item x Context1 x Context2 x Context3 -> Relevance Score
- 22. STORING DATA SOURCES: TENSOR GraphAware® Simple Recommendation f: User x Item -> Relevance Score Context Aware Recommendation f: User x Item x Context1 x Context2 x Context3 -> Relevance Score
- 23. STORING DATA SOURCES: TENSOR GraphAware® Simple Recommendation f: User x Item -> Relevance Score Context Aware Recommendation f: User x Item x Context1 x Context2 x Context3 -> Relevance Score
- 24. STORING DATA SOURCES: KNOWLEDGE GRAPH GraphAware®
- 25. GraphAware®
- 26. GraphAware®
- 27. Some graph-theoretical algorithms that are relevant to machine learning processes: ‣ Random Walk ‣ Page Rank ‣ Graph Matching ‣ Shortest Path ‣ Depth-First Graph Traversal ‣ Breadth-First Graph Traversal ‣ Minimum Spanning Tree ‣ Graph Clustering GRAPH-BASED ML ALGORITHMS GraphAware®
- 28. pressure information system telemetry spacecraft power critical ground command performance component drive connector standard software operation testing environmental flight damage spare subsystem orbiter posit flight software control instrument propellant flight project program resource equipment fluid re condition analysis servicemaintenance device electrical circuit area accident number management risk implem significant personnel exploration rover facility human effect event life lack loss potential review ground system engineering different configuration control configuration logistics datum NASA ground processing processing safety verification risk management part t launch pipe shuttle leak lesson load environment space check line source training factor material due practice capability battery flight hardware government assembly figure evm space shuttle surface operational thermal installation many set one inspection propulsion system actuator impact flight system launch vehicle flow current leakage shuttle program digital mishap propulsion engine complete NASA program fault attitude science flight operation mission operation independent investigation JPL fire procedure torque incident mars space system general critical hardware fault protection mer user system engineering expertisespace flight manager project manager mitigation Pegasus actuator system amp capability legacy actuator system output control system different manager many review purpose appropriate f integration important ground testing computer flight equipment independent review space hardware software development ssme technical content value management aerodynamic load aerodynamic ppe deceased successful implementation formal mishap investigation lax GRAPH-BASED ML ALGORITHMS: PAGE RANK GraphAware® Keywords Extraction Rada Mihalcea, Paul Tarau. 2004. TextRank: Bringing Order into Texts. Proceedings of EMNLP 2004, pages 404–411, Barcelona, Spain. Association for Computational Linguistics. http://www.aclweb.org/anthology/W04-3252.
- 29. pressure information system telemetry spacecraft power critical ground command performance component drive connector standard software operation testing environmental flight damage spare subsystem orbiter posit flight software control instrument propellant flight project program resource equipment fluid re condition analysis servicemaintenance device electrical circuit area accident number management risk implem significant personnel exploration rover facility human effect event life lack loss potential review ground system engineering different configuration control configuration logistics datum NASA ground processing processing safety verification risk management part t launch pipe shuttle leak lesson load environment space check line source training factor material due practice capability battery flight hardware government assembly figure evm space shuttle surface operational thermal installation many set one inspection propulsion system actuator impact flight system launch vehicle flow current leakage shuttle program digital mishap propulsion engine complete NASA program fault attitude science flight operation mission operation independent investigation JPL fire procedure torque incident mars space system general critical hardware fault protection mer user system engineering expertisespace flight manager project manager mitigation Pegasus actuator system amp capability legacy actuator system output control system different manager many review purpose appropriate f integration important ground testing computer flight equipment independent review space hardware software development ssme technical content value management aerodynamic load aerodynamic ppe deceased successful implementation formal mishap investigation lax GRAPH-BASED ML ALGORITHMS: PAGE RANK GraphAware® Keywords Extraction Rada Mihalcea, Paul Tarau. 2004. TextRank: Bringing Order into Texts. Proceedings of EMNLP 2004, pages 404–411, Barcelona, Spain. Association for Computational Linguistics. http://www.aclweb.org/anthology/W04-3252.
- 30. GRAPH-BASED ML ALGORITHMS: GRAPH CLUSTERING GraphAware® Continuous Cellular Tower Data Analysis Eagle N., Quinn J.A., Clauset A. (2009) Methodologies for Continuous Cellular Tower Data Analysis. In: Tokuda H., Beigl M., Friday A., Brush A.J.B., Tobe Y. (eds) Pervasive Computing. Pervasive 2009. Lecture Notes in Computer Science, vol 5538. Springer, Berlin, Heidelberg
- 31. GRAPH-BASED ML ALGORITHMS: GRAPH CLUSTERING GraphAware® Continuous Cellular Tower Data Analysis Eagle N., Quinn J.A., Clauset A. (2009) Methodologies for Continuous Cellular Tower Data Analysis. In: Tokuda H., Beigl M., Friday A., Brush A.J.B., Tobe Y. (eds) Pervasive Computing. Pervasive 2009. Lecture Notes in Computer Science, vol 5538. Springer, Berlin, Heidelberg
- 32. GRAPH-BASED ML ALGORITHMS: GRAPH CLUSTERING GraphAware® Continuous Cellular Tower Data Analysis Eagle N., Quinn J.A., Clauset A. (2009) Methodologies for Continuous Cellular Tower Data Analysis. In: Tokuda H., Beigl M., Friday A., Brush A.J.B., Tobe Y. (eds) Pervasive Computing. Pervasive 2009. Lecture Notes in Computer Science, vol 5538. Springer, Berlin, Heidelberg
- 33. The results of machine learning process can be stored in a graph as well. Some examples are: ‣ Similarity (k-Nearest Neighbors) ‣ Cluster ‣ Spanning Tree ‣ Decision Tree ‣ Random forest ‣ Markov Chain STORING MODELS GraphAware®
- 37. STORING MODELS GraphAware® K-Nearest Neighbors Markov Chain Decision Tree
- 38. GraphAware®
- 39. GraphAware®
- 43. GRAPH VISUALIZATION GraphAware® pressure information system telemetry spacecraft mission power critical objective ground command performance component protection drive connector field standard software operation testing environmental flight damage spare subsystem orbiter position flight software control project instrument propellant flight project development program hardware resource equipment effort fluid requirement failure condition analysis servicemaintenance mission success success temperature contamination activity device electrical circuit area center accident number management risk issue implementation planning significant personnel exploration rover facility reliability human effect event relationship life vehicle constellation program constellation team member lack loss potential change requirement development organization process review ground system engineering different configuration control oversight contractor configuration logistics datum cxp contract knowledge NASA scope proposal ground processing processing safety verification risk management tool part technical rocket launch pipe shuttle leak lesson load environment space check interference agreement funding line source training human factor factor material due practice capability NASA project new prime prime contractor battery flight hardware government assembly figure evm subject authority space shuttle surface operational phase thermal complex measure key major configuration management personnel 880 pmn international space station appropriate program disposition strong relationship high performance team installation many set problem one inspection propulsion system approach project management magnetic actuator impact flight system launch vehicle flow current leakage shuttle program technology digital mishap propulsion engine operating complete NASA program fault mode attitude science flight operation mission operation independent investigation JPL fire procedure torque incident catastrophic failure value potential problem mars space system general participant critical hardware downlink constraint inheritance culture fault protection mer project reserve reserve qualification mission failure user system engineering bull domain expertise industry private industry space flight financial manager project manager mitigation Pegasus actuator system amp capability legacy actuator system output dryden aerospike rocket aerospike nozzle aerospike aerospike rocket nozzle control system team member different manager many review purpose appropriate project development formulation integration important ground testing computer potential failure flight equipment mission life independent review space hardware software development ssme failure mode osp program osp technical content financial system value management aerodynamic load aerodynamic ppe cowl in-lb deceased successful implementation formal mishap investigation lax
- 44. ‣ Think bigger ‣ Think about Data Flow ‣ Think about graphs ‣ Think … Think about us CONCLUSION GraphAware®