Abstract
We present a novel method of node clustering that is based on carrying out a physical simulation. We treat nodes of a graph as point-sized unit mass particles that interact with each other as well as the space (multi-dimensional) that they are present in through certain defined physical forces. As the configuration of the system evolves during the simulation, similar nodes coalesce while the dissimilar nodes separate out, thus allowing node clusters to emerge. We have experimented with this idea on graphs with up to 300 nodes and have found it to work well. Doing so also allowed us to solve problems of network community detection by utilizing existing density based clustering algorithms, which otherwise would not be possible.
The authors thank Dr. Manish Gupta (Professor, IIIT-B and Director, Google Research India) for his technical inputs as well as for providing partial financial support to the project through his Infosys Foundation chair professorship fund. The authors also acknowledge the financial support from Machine Intelligence and Robotics (MINRO) Center at IIIT Bangalore through a grant from the Department of ITBT &ST, Government of Karnataka.
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Kalra, K., Nikhila, K.N., Chakrabarti, S.K. (2023). Physics Simulation Based Approach to Node Clustering. In: Gyei-Kark, P., Jana, D.K., Panja, P., Abd Wahab, M.H. (eds) Engineering Mathematics and Computing. Studies in Computational Intelligence, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-19-2300-5_15
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