We introduce a novel Bayesian framework for hybrid community discovery in graphs. Our
framework, HCDF (short for Hybrid Community Discovery Framework), can effectively …

Community and role discovery are key tasks in network analysis. The former unveils the
organization of a network, whereas the latter highlights the social functions of nodes. The …

The detection of communities in large social networks is receiving increasing attention in a
variety of research areas. Most existing community detection approaches focus on the …

As people accumulate hundreds of" friends" in social media, a flat list of connections
becomes unmanageable. Interfaces agnostic to social structure hinder the nuanced sharing …

Recent years have witnessed the tremendous development of social media, which attracts a
vast number of Internet users. The high-dimension content generated by these users …

In recent years, there is a fast proliferation of collaborative tagging (aka folksonomy) systems
in Web 2.0 communities. With the increasingly large amount of data, how to assist users in …

Topic models are used to group words in a text dataset into a set of relevant topics.
Unfortunately, when a few words frequently appear in a dataset, the topic groups identified …

We use the Enron email corpus to study relationships in a network by applying six different
measures of centrality. Our results came out of an in-semester undergraduate research …

Abstract Since the Semantic Social Network (SSN) is a new kind of complex networks, the
traditional community detection algorithms require giving the number of the communities …

Expertise retrieval, whose task is to suggest people with relevant expertise on the topic of
interest, has received increasing interest in recent years. One of the issues is that previous …