I. INTRODUCTION
A. GOAL
B. CONTRIBUTION
C. RELATED WORK
II. WHY LEARN TO SOLVE COMBINATORIAL OPTIMIZATION PROBLEMS?
A. CHALLENGES FACED BY TRADITIONAL APPROACHES
B. MOTIVATION FOR MACHINE LEARNING
III. SPECIALIZED, CONTEMPORARY MACHINE LEARNING METHODS
A. ATTENTION MECHANISMS
B. GRAPH NEURAL NETWORKS
C. (DEEP) REINFORCEMENT LEARNING
IV. LEARNING TO SOLVE COMBINATORIAL OPTIMIZATION PROBLEMS ON GRAPHS
A. ATTENTION MECHANISMS: POINTER NETWORKS AND TRANSFORMER ARCHITECTURE
B. GRAPH NEURAL NETWORKS
V. PERFORMANCE
A. TRAVELLING SALESMAN PROBLEM
B. VEHICULAR ROUTING PROBLEM
C. MAXIMUM CUT
D. MINIMUM VERTEX COVER
E. MAXIMAL INDEPENDENT SET AND MAXIMAL CLIQUE
F. MAXIMUM COVERAGE PROBLEM
G. SATISFIABILITY
H. GRAPH COLORING
I. DISCUSSION
VI. NETWORKING APPLICATIONS
A. ROUTING
B. SCHEDULING
C. RESOURCE ALLOCATION
D. DISCUSSION
VII. SUMMARY AND OUTLOOK