Survey on Social Community Detection <s> From bipartite graphs to partitions (A2) <s> The investigation of community structures in networks is an important issue in many domains and disciplines. There have been considerable recent interest algorithms for finding communities in networks. In this paper we present a method of detecting community structure based on hypergraph model. The hypergraph model maps the relationship in the original data into a hypergraph. A hyperedge represents a relationship among subsets of data and the weight of the hyperedge reflects the strength of this affinity. We assign the density of a hyperedge to its weight. We present and illustrate the results of experiments on the Enron data set. These experiments demonstrate that our approach is applicable and effective. <s> BIB001 </s> Survey on Social Community Detection <s> From bipartite graphs to partitions (A2) <s> Bipartite networks can represent various kinds of structures, dynamics, and interaction patterns found in social activities. M. E. J. Newman proposed a measure by which you can quantitatively evaluate the quality of network division, but his work is only applicable to uniform networks. This article extends his work and proposes a new modularity measure that can be applied to bipartite networks as well. Unlike the biparitite modularity measures previously proposed, the new measure acknowledges the fact that each individual in the society has more than just one aspect, and can thus be used to extract multi-faceted community structures from bipartite networks. The mathematical properties of the proposal is examined and compared with previous work. Empirical evaluation is conducted by using a data set synthesized from an artificial model and a real-life data set found in the field of ethnography. <s> BIB002 </s> Survey on Social Community Detection <s> From bipartite graphs to partitions (A2) <s> Online social media such as delicious and digg are represented as tripartite networks whose vertices are users, tags, and resources. Detecting communities from such tripartite networks is practically important. Modularity is often used as the criteria for evaluating the goodness of network divisions into communities. Although Newman-Girvan modularity is popular for unipartite networks, it is not suitable for n-partite networks. For bipartite networks, Barber, Guimera, Murata and Suzuki define bipartite modularities. For tripartite networks, Neubauer defines tripartite modularity which extends Murata's bipartite modularity. However, Neubauer's tripartite modularity still uses projections and it will lose information that original tripartite networks have. This paper proposes new tripartite modularity for tripartite networks that do not use projections. Experimental results show that better community structures can be detected by optimizing our tripartite modularity. <s> BIB003
In this class, the inputs are bipartite graphs, representing for example individuals sharing common properties (photos, tags, etc.). The output contains a list of communities from the initial graph. Each node belongs to just one community. No surveys directly describe this particular case; however, BIB002 ], which is based on work performed by Murata BIB003 , adapted a new modularity measure for bipartite graphs in order to build separate communities. Another effort in BIB001 uses cluster-type local density to extract communities from bipartite graphs modeled as hypergraphs..