How to find these communities is an important research work. Recently, community discovery are mainly categorized to HITS algorithm, bipartite cores algorithm and maximum flow/minimum cut framework. In this paper, we ...How to find these communities is an important research work. Recently, community discovery are mainly categorized to HITS algorithm, bipartite cores algorithm and maximum flow/minimum cut framework. In this paper, we proposed a new method to extract communities. The MCL algorithm, which is short for the Markov Cluster Algorithm, a fast and scalable unsupervised cluster algorithm is used to extract communities. By putting mirror deleting procedure behind graph clustering, we decrease comparing cost considerably. After MCL and mirror deletion, we use community member select algorithm to produce the sets of community candidates. The experiment and results show the new method works effectively and properly.展开更多
In this paper, we improve the trawling and point out some communities missed by trawling. We use the DBG （Dense Bipartite Graph） to identify a structure of a potential community instead of CBG （Complete Bipartite G...In this paper, we improve the trawling and point out some communities missed by trawling. We use the DBG （Dense Bipartite Graph） to identify a structure of a potential community instead of CBG （Complete Bipartite Graph）. Based on DBG, we proposed a new method based on edge removal to extract cores from a web graph. Moreover, we improve the crawler to save only potential pages as fans of a core and save a lot of disk storage space. To evaluate the set of cores whether or not belong to a community, the statistics of term frequency is used. In the paper, the dataset of experiment were crawled under domain ＂.cn＂. The result show that the our algorithm works properly and some new cores can be found by our method.展开更多
基金Supported bythe 211 Project of Ministry of Educa-tion of China
文摘How to find these communities is an important research work. Recently, community discovery are mainly categorized to HITS algorithm, bipartite cores algorithm and maximum flow/minimum cut framework. In this paper, we proposed a new method to extract communities. The MCL algorithm, which is short for the Markov Cluster Algorithm, a fast and scalable unsupervised cluster algorithm is used to extract communities. By putting mirror deleting procedure behind graph clustering, we decrease comparing cost considerably. After MCL and mirror deletion, we use community member select algorithm to produce the sets of community candidates. The experiment and results show the new method works effectively and properly.
基金Supported by the Natural Science Fund of Renmin Uni-versity of China (30207108)
文摘In this paper, we improve the trawling and point out some communities missed by trawling. We use the DBG （Dense Bipartite Graph） to identify a structure of a potential community instead of CBG （Complete Bipartite Graph）. Based on DBG, we proposed a new method based on edge removal to extract cores from a web graph. Moreover, we improve the crawler to save only potential pages as fans of a core and save a lot of disk storage space. To evaluate the set of cores whether or not belong to a community, the statistics of term frequency is used. In the paper, the dataset of experiment were crawled under domain ＂.cn＂. The result show that the our algorithm works properly and some new cores can be found by our method.
