Community analysis algorithm proposed by Clauset, Newman, and Moore (CNM algorithm) finds community structure in social networks. Unfortunately, CNM algorithm does not scale well and its use is practically limited to networks whose sizes are up to 500,000 nodes. The paper identifies that this inefficiency is caused from merging communities in unbalanced manner. The paper introduces three kinds of metrics (consolidation ratio) to control the process of community analysis trying to balance the sizes of the communities being merged. Three flavors of CNM algorithms are built incorporating those metrics. The proposed techniques are tested using data sets obtained from existing social networking service that hosts 5.5 million users. All the methods exhibit dramatic improvement of execution efficiency in comparison with the original CNM algorithm and shows high scalability. The fastest method processes a network with 1 million nodes in 5 minutes and a network with 4 million nodes in 35 minutes, respectively. Another one processes a network with 500,000 nodes in 50 minutes (7 times faster than the original algorithm), finds community structures that has improved modularity, and scales to a network with 5.5 million.

It is generally claimed that object-based models are very suitable for building distributed system architectures since object interactions follow the clientserver model. To cope with the complexity of today's distributed systems, however, we think that high-level linguistic mechanisms are needed to effectively structure, abstract and reuse object interactions. For example, the conventional object-oriented model does not provide high-level language mechanisms to model layered system architectures. Moreover, we consider the message passing model of the conventional object-oriented model as being too low-level because it can only specify object interactions that involve two partner objects at a time and its semantics cannot be extended easily. This paper introduces Abstract Communication Types (ACTs), which are objects that abstract interactions among objects. ACTs make it easier to model layered communication architectures, to enforce the invariant behavior among objects, to reduce the complexity of programs by hiding the interaction details in separate modules and to improve reusability through the application of objectoriented principles to ACT classes. We illustrate the concept of ACTs using the composition filters model.

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 bipartite 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.

There has been a surge of interest in community detection in homogeneous single-relational networks which contain only one type of nodes and edges. However, many real-world systems are naturally described as heterogeneous multi-relational networks which contain multiple types of nodes and edges. In this paper, we propose a new method for detecting communities in such networks. Our method is based on optimizing the composite modularity, which is a new modularity proposed for evaluating partitions of a heterogeneous multi-relational network into communities. Our method is parameter-free, scalable, and suitable for various networks with general structure. We demonstrate that it outperforms the state-of-the-art techniques in detecting preplanted communities in synthetic networks. Applied to a real-world Digg network, it successfully detects meaningful communities.