We give algorithms for finding graph clusters and drawing graphs, highlighting local community structure within the context of a larger network. For a given graph G, we use the per...
Large scale real-world network data such as social and information networks are ubiquitous. The study of such social and information networks seeks to find patterns and explain th...
We give an improved algorithm for computing personalized PageRank vectors with tight error bounds which can be as small as (n-p ) for any fixed positive integer p. The improved Pag...
We introduce a new geometric, rank-based model for the link structure of on-line social networks (OSNs). In the geo-protean (GEO-P) model for OSNs nodes are identified with points ...
Abstract. Analysis of aggregate and individual Web requests shows that PageRank is a poor predictor of traffic. We use empirical data to characterize properties of Web traffic not ...
Abstract. Motivated by social network data mining problems such as link prediction and collaborative filtering, significant research effort has been devoted to computing topologica...
Pooya Esfandiar, Francesco Bonchi, David F. Gleich...
We study the effect of information overload on user engagement in an asymmetric social network like Twitter. We introduce simple game-theoretic models that capture rate competition...
Christian Borgs, Jennifer T. Chayes, Brian Karrer,...