@inproceedings {28440,
	title = {Incremental Clustering Algorithms for Massive Dynamic Graphs},
	journal = {International Conference on Data Mining Workshops (ICDMW)},
	year = {2021},
	month = {12/2021},
	pages = {360-369},
	publisher = {IEEE},
	address = {Auckland, New Zealand },
	abstract = {We consider the problem of incremental graph clustering where the graph to be clustered is given as a sequence of disjoint subsets of the edge set. The problem appears when dealing with graphs that are created over time, such as online social networks where new users appear continuously, or protein interaction networks when new proteins are discovered. For very large graphs, it is computationally too expensive to repeatedly apply standard clustering algorithms. Instead, algorithms whose time complexity only depends on the size of the incoming subset of edges in every step are needed. At the same time, such algorithms should find clusterings whose quality is close to that produced by offline algorithms. In this paper, we discuss the computational model and present an incremental clustering algorithm. We test the algorithm performance and quality on a wide variety of instances. Our results show that the algorithm far outperforms offline algorithms while retaining a large fraction of their clustering quality.},
	isbn = {978-1-6654-2427-1},
	issn = {2375-9259 },
	doi = {10.1109/ICDMW53433.2021.00051},
	url = {https://ieeexplore.ieee.org/abstract/document/9679843},
	author = {Langguth, Johannes and Tumanis, Aigars and Azad, Ariful}
}
