Making graph neural networks safe for hard optimization problems, accepted at an IEEE conference

Graph neural networks can score which nodes matter in an optimization problem, but their raw outputs often break the rules the solution has to follow. Manomay studied this on minimum vertex cover, a classic hard problem, and found raw network outputs were valid in only 38.67% of cases. His pipeline repairs every uncovered edge and then prunes redundant vertices, restoring 100% validity and cutting the solution from 24.87 to 21.90 vertices, with a mean optimality gap of 2.75% against exact solutions.

FIELDAI & Optimization

RESULTFirst-author paper accepted at an IEEE conference

VENUEIEEE, 2026

Manomay Jain, YRI FellowIEEE

BEFORE THE FELLOWSHIP

A 12th grader at Modern School, Barakhamba Road in Delhi and an international math olympiad medalist who wanted to do real research in AI.

AFTER

First author of a rigorous study on repairing graph neural network outputs for the minimum vertex cover problem, accepted at an IEEE conference with minor revisions.

THE LEDGER

01First-author paper accepted at an IEEE conference

02Restored feasibility from 38.67% to 100%

03Mean optimality gap of 2.75% against exact mixed-integer solutions

04Evaluated on 570 graphs plus 135 larger graphs for generalization

05Accepted with minor revisions

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