摘要
In social tagging systems such as Delicious and Flickr,users collaboratively manage tags to annotate resources.Naturally,a social tagging system can be modeled as a （user,tag,resource） hypernetwork,where there are three different types of nodes,namely users,resources and tags,and each hyperedge has three end nodes,connecting a user,a resource and a tag that the user employs to annotate the resource.Then how can we automatically cluster related users,resources and tags,respectively？ This is a problem of community detection in a 3-partite,3-uniform hypernetwork.More generally,given a K-partite K-uniform （hyper）network,where each （hyper）edge is a K-tuple composed of nodes of K different types,how can we automatically detect communities for nodes of different types？ In this paper,by turning this problem into a problem of finding an efficient compression of the （hyper）network＇s structure,we propose a quality function for measuring the goodness of partitions of a K-partite K-uniform （hyper）network into communities,and develop a fast community detection method based on optimization.Our method overcomes the limitations of state of the art techniques and has several desired properties such as comprehensive,parameter-free,and scalable.We compare our method with existing methods in both synthetic and real-world datasets.
In social tagging systems such as Delicious and Flickr,users collaboratively manage tags to annotate resources.Naturally,a social tagging system can be modeled as a （user,tag,resource） hypernetwork,where there are three different types of nodes,namely users,resources and tags,and each hyperedge has three end nodes,connecting a user,a resource and a tag that the user employs to annotate the resource.Then how can we automatically cluster related users,resources and tags,respectively？ This is a problem of community detection in a 3-partite,3-uniform hypernetwork.More generally,given a K-partite K-uniform （hyper）network,where each （hyper）edge is a K-tuple composed of nodes of K different types,how can we automatically detect communities for nodes of different types？ In this paper,by turning this problem into a problem of finding an efficient compression of the （hyper）network＇s structure,we propose a quality function for measuring the goodness of partitions of a K-partite K-uniform （hyper）network into communities,and develop a fast community detection method based on optimization.Our method overcomes the limitations of state of the art techniques and has several desired properties such as comprehensive,parameter-free,and scalable.We compare our method with existing methods in both synthetic and real-world datasets.
基金
supported in part by JSPS Grant-in-Aid under Grant No.22300049 and IBM Ph.D.Fellowship
