Survey on Social Community Detection <s> From bipartite graphs to overlapping communities, hypergraphs (B2) <s> Social scientists have shown an increasing interest in understanding the structure of knowledge communities, and particularly the organization of"epistemic communities", that is groups of agents sharing common knowledge concerns. However, most existing approaches are based only on either social relationships or semantic similarity, while there has been roughly no attempt to link social and semantic aspects. In this paper, we introduce a formal framework addressing this issue and propose a method based on Galois lattices (or concept lattices) for categorizing epistemic communities in an automated and hierarchically structured fashion. Suggesting that our process allows us to rebuild a whole community structure and taxonomy, and notably fields and subfields gathering a certain proportion of agents, we eventually apply it to empirical data to exhibit these alleged structural properties, and successfully compare our results with categories spontaneously given by domain experts. <s> BIB001 </s> Survey on Social Community Detection <s> From bipartite graphs to overlapping communities, hypergraphs (B2) <s> This paper aims at discovering community structure in rich media social networks, through analysis of time-varying, multi-relational data. Community structure represents the latent social context of user actions. It has important applications in information tasks such as search and recommendation. Social media has several unique challenges. (a) In social media, the context of user actions is constantly changing and co-evolving; hence the social context contains time-evolving multi-dimensional relations. (b) The social context is determined by the available system features and is unique in each social media website. In this paper we propose MetaFac (MetaGraph Factorization), a framework that extracts community structures from various social contexts and interactions. Our work has three key contributions: (1) metagraph, a novel relational hypergraph representation for modeling multi-relational and multi-dimensional social data; (2) an efficient factorization method for community extraction on a given metagraph; (3) an on-line method to handle time-varying relations through incremental metagraph factorization. Extensive experiments on real-world social data collected from the Digg social media website suggest that our technique is scalable and is able to extract meaningful communities based on the social media contexts. We illustrate the usefulness of our framework through prediction tasks. We outperform baseline methods (including aspect model and tensor analysis) by an order of magnitude. <s> BIB002
In this class, inputs are bipartite graphs, while outputs are either hypergraphs or lists of node groups representing communities that may or may not overlap. One survey  describes this case, in citing BIB002 . By defining "Epistemic communities" in BIB001 Roth depicts a partial example of this class and then takes it one step further with Galois hierarchies (see below).