Exploring Communities in Large Profiled Graphs

C-Explorer: Browsing Communities in Large Graphs

Community retrieval (CR) algorithms, which enable the extraction of subgraphs from large social networks (e.g., Facebook and Twitter), have attracted tremendous interest. Various CR solutions, such as k-core and CODICIL, have been proposed to obtain graphs whose vertices are closely related. In this paper, we propose the C-Explorer system to assist users in extracting, visualizing, and analyzin...

Exploring Large Graphs in 3D Hyperbolic Space

graph is a simple, powerful, elegant abstraction with broad applicability in computer science and many related fields. Algorithms that operate on graphs see heavy use in both theoretical and practical contexts. Graphs have a very natural visual representation as nodes and connecting links arranged in space. Seeing this structure explicitly can aid tasks in many domains. Many people automaticall...

Exploring and Making Sense of Large Graphs

Graphs naturally represent information ranging from links between webpages, to friendships in social networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect a...

Random Overlapping Communities: Approximating Motif Densities of Large Graphs

A wide variety of complex networks (social, biological, information etc.) exhibit local clustering with substantial variation in the clustering coefficient (the probability of neighbors being connected). Existing models of large graphs capture power law degree distributions (BarabásiAlbert) and small-world properties (Watts-Strogatz), but only limited clustering behavior. We introduce a general...

Communities in Graphs