Abstract
Extraction of information from graph data is essential since most data in the real world are dynamic, large, and without fixed structure, unlike images. Graph neural networks (GNNs) harness the power of graphs, efficiently examining graph data and help make inferences from complex data structures, making them an invaluable tool in domains like social network analysis. GNNs have shown promise in solving combinatorial optimization problems which involve extracting a good candidate solution by scanning over the search space. The vastness of the search space often renders the optimal solution search a difficult task. Many previous works demonstrate the use of GNNs in solving problems like graph coloring and MaxCut problem. In this paper, we analyze the ability of different GNN architectures to tackle the MaxCut problem, posed as a Quadratic Unconstrained Binary Optimization (QUBO) problem, minimizing the loss function based on the QUBO objective.
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References
Fan W, Ma Y, Li Q, He Y, Zhao E, Tang J, Yin D (2019) Graph neural networks for social recommendation. In: Proceedings of the World Wide Web conference (WWW) 2019
Ying R, He R, Chen K, Eksombatchai P, Hamilton WL, Leskovec J (2018) Graph convolutional neural networks for web-scale recommender systems.
Fout A, Aspuru-Guzik A, Radford A (2017) Protein interface prediction using graph convolutional networks. In: Advances in neural information processing systems (NeurIPS)
Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, MacNair CR, French S, Carfrae LA, Bloom-Ackermann Z, Tran VM, Chiappino-Pepe A, Badran AH, Andrews IW, Chory EJ, Church GM, Brown ED, Jaakkola TS, Barzilay R, Collins JJ. A deep learning approach to antibiotic discovery. In: Proceedings of the 30th ACM international conference on information & knowledge management
She J, Wong D, Lange O, Hester T, Perez L, Nunkesser M, Lee S, Guo X, Wiltshire B, Battaglia PW, Gupta V, Li A, Xu Z, Li Y, Veličković P (2021) ETA prediction with graph neural networks in Google maps. In: Proceedings of the 30th ACM international conference on information & knowledge management, Oct 2021
Jain A, Liu I, Sarda A, Molino P (2019) Food discovery with Uber eats: using graph learning to power recommendations
Schuetz MJ, Brubaker JK, Katzgraber HG (2022) Combinatorial optimization with physics-inspired graph neural networks. Nat Mach Intell 4:367–377
Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Proc Neural Inf Process Syst (NeurIPS) 2016:3844–3852
Du J, Zhang S, Wu G, Moura JM, Kar S (2017) Topology adaptive graph convolutional networks. ArXiv /abs/1710.1037
Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in neural information processing systems (NeurIPS), pp 1024–1033
Kipf T, Welling M (2016) Semi-supervised classification with graph convolutional networks. ArXiv /abs/1609.02907
Thekumparampil KK (2018) Attention-based graph neural network for semi-supervised learning. ArXiv abs/1803.03735
Wang Y, Sun Y, Liu Z, Sarma SE, Bronstein MM, Solomon JM (2018) Dynamic graph CNN for learning on point clouds. ACM Trans Graph 38(5)
Wu F, Zhang T, Fifty C, Yu T, Weinberger KQ (2019) Simplifying graph convolutional networks. In: International conference on machine learning (ICML)
Barahona F, Grötschel M, Jünger M, Reinelt G (1988) An application of combinatorial optimization to statistical physics and circuit layout design. Oper Res
Sun C, Sun Y, Dai R (2018) ADMM for combinatorial graph problems
Glover F, Kochenberger G, Du Y (2018) A tutorial on formulating and using QUBO models. ArXiv /abs/1811.11538
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Hameed, H., Ramanujan, A. (2024). Analysis of Various GNNs in Solving MaxCut Problem. In: Gunjan, V.K., Zurada, J.M. (eds) Proceedings of 4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. ICMISC 2023. Lecture Notes in Networks and Systems, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-99-9442-7_43
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DOI: https://doi.org/10.1007/978-981-99-9442-7_43
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