@inbook {jin2011ant,
	title = {Ant colony optimization with Markov random walk for community detection in graphs},
	booktitle = {Advances in Knowledge Discovery and Data Mining},
	volume = {6635},
	year = {2011},
	pages = {123{\textendash}134},
	publisher = {Springer},
	organization = {Springer},
	abstract = {<p>Network clustering problem (NCP) is the problem associated to the detection of network community structures. Building on Markov random walks we address this problem with a new ant colony optimization strategy, named as ACOMRW, which improves prior results on the NCP problem and does not require knowledge of the number of communities present on a given network. The framework of ant colony optimization is taken as the basic framework in the ACOMRW algorithm. At each iteration, a Markov random walk model is taken as heuristic rule; all of the ants{\textquoteright} local solutions are aggregated to a global one through clustering ensemble, which then will be used to update a pheromone matrix. The strategy relies on the progressive strengthening of within-community links and the weakening of between-community links. Gradually this converges to a solution where the underlying community structure of the complex network will become clearly visible. The performance of algorithm ACOMRW was tested on a set of benchmark computer-generated networks, and as well on real-world network data sets. Experimental results confirm the validity and improvements met by this approach.</p>
},
	author = {Di Jin and Dayou Liu and Bo Yang and Carlos Baquero Moreno and Dongxiao He}
}