Graph-Powered machine learning is becoming an important trend in Artificial Intelligence, transcending a lot of other techniques. Using graphs as 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 learn over them, creating Knowledge Graphs; (iii) improving computation performances and quality. The talk will discuss these advantages and present applications in the context of recommendation engines and natural language processing.
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
18. STORING DATA SOURCES: TENSOR
GraphAware®
Simple Recommendation
f: User x Item -> Relevance Score
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
Context Aware Recommendation
f: User x Item x Context1 x Context2 x Context3 -> 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
28. 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
29. 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
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. 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®