@article{liu_evolutionary_2017,
	title = {Evolutionary link community structure discovery in dynamic weighted networks},
	volume = {466},
	issn = {0378-4371},
	url = {http://www.sciencedirect.com/science/article/pii/S0378437116306446},
	doi = {10.1016/j.physa.2016.09.028},
	abstract = {Traditional community detection methods are often restricted in static network analysis. In fact, most of networks in real world obviously show dynamic characteristics with time passing. In this paper, we design a link community structure discovery algorithm in dynamic weighted networks, which can not only reveal the evolutionary link community structure, but also detect overlapping communities by mapping link communities to node communities. Meanwhile, our algorithm can also get the hierarchical structure of link communities by tuning a parameter. The proposed algorithm is based on weighted edge fitness and weighted partition density so as to determine whether to add a link to a community and whether to merge two communities to form a new link community. Experiments on both synthetic and real world networks demonstrate the proposed algorithm can detect evolutionary link community structure in dynamic weighted networks effectively.},
	urldate = {2017-02-22},
	journal = {Physica A: Statistical Mechanics and its Applications},
	author = {Liu, Qiang and Liu, Caihong and Wang, Jiajia and Wang, Xiang and Zhou, Bin and Zou, Peng},
	year = {2017},
	pages = {370--388},
}

{"_id":"er86RKTuB9nTyPhWB","bibbaseid":"liu-liu-wang-wang-zhou-zou-evolutionarylinkcommunitystructurediscoveryindynamicweightednetworks-2017","author_short":["Liu, Q.","Liu, C.","Wang, J.","Wang, X.","Zhou, B.","Zou, P."],"bibdata":{"bibtype":"article","type":"article","title":"Evolutionary link community structure discovery in dynamic weighted networks","volume":"466","issn":"0378-4371","url":"http://www.sciencedirect.com/science/article/pii/S0378437116306446","doi":"10.1016/j.physa.2016.09.028","abstract":"Traditional community detection methods are often restricted in static network analysis. In fact, most of networks in real world obviously show dynamic characteristics with time passing. In this paper, we design a link community structure discovery algorithm in dynamic weighted networks, which can not only reveal the evolutionary link community structure, but also detect overlapping communities by mapping link communities to node communities. Meanwhile, our algorithm can also get the hierarchical structure of link communities by tuning a parameter. The proposed algorithm is based on weighted edge fitness and weighted partition density so as to determine whether to add a link to a community and whether to merge two communities to form a new link community. Experiments on both synthetic and real world networks demonstrate the proposed algorithm can detect evolutionary link community structure in dynamic weighted networks effectively.","urldate":"2017-02-22","journal":"Physica A: Statistical Mechanics and its Applications","author":[{"propositions":[],"lastnames":["Liu"],"firstnames":["Qiang"],"suffixes":[]},{"propositions":[],"lastnames":["Liu"],"firstnames":["Caihong"],"suffixes":[]},{"propositions":[],"lastnames":["Wang"],"firstnames":["Jiajia"],"suffixes":[]},{"propositions":[],"lastnames":["Wang"],"firstnames":["Xiang"],"suffixes":[]},{"propositions":[],"lastnames":["Zhou"],"firstnames":["Bin"],"suffixes":[]},{"propositions":[],"lastnames":["Zou"],"firstnames":["Peng"],"suffixes":[]}],"year":"2017","pages":"370–388","bibtex":"@article{liu_evolutionary_2017,\n\ttitle = {Evolutionary link community structure discovery in dynamic weighted networks},\n\tvolume = {466},\n\tissn = {0378-4371},\n\turl = {http://www.sciencedirect.com/science/article/pii/S0378437116306446},\n\tdoi = {10.1016/j.physa.2016.09.028},\n\tabstract = {Traditional community detection methods are often restricted in static network analysis. In fact, most of networks in real world obviously show dynamic characteristics with time passing. In this paper, we design a link community structure discovery algorithm in dynamic weighted networks, which can not only reveal the evolutionary link community structure, but also detect overlapping communities by mapping link communities to node communities. Meanwhile, our algorithm can also get the hierarchical structure of link communities by tuning a parameter. The proposed algorithm is based on weighted edge fitness and weighted partition density so as to determine whether to add a link to a community and whether to merge two communities to form a new link community. Experiments on both synthetic and real world networks demonstrate the proposed algorithm can detect evolutionary link community structure in dynamic weighted networks effectively.},\n\turldate = {2017-02-22},\n\tjournal = {Physica A: Statistical Mechanics and its Applications},\n\tauthor = {Liu, Qiang and Liu, Caihong and Wang, Jiajia and Wang, Xiang and Zhou, Bin and Zou, Peng},\n\tyear = {2017},\n\tpages = {370--388},\n}\n\n","author_short":["Liu, Q.","Liu, C.","Wang, J.","Wang, X.","Zhou, B.","Zou, P."],"key":"liu_evolutionary_2017","id":"liu_evolutionary_2017","bibbaseid":"liu-liu-wang-wang-zhou-zou-evolutionarylinkcommunitystructurediscoveryindynamicweightednetworks-2017","role":"author","urls":{"Paper":"http://www.sciencedirect.com/science/article/pii/S0378437116306446"},"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/wybert","dataSources":["TJkbwzD8s2wCxBy6Y"],"keywords":[],"search_terms":["evolutionary","link","community","structure","discovery","dynamic","weighted","networks","liu","liu","wang","wang","zhou","zou"],"title":"Evolutionary link community structure discovery in dynamic weighted networks","year":2017}