Abstract Information extracted from social network services promise to improve the accuracy
of recommender systems in various domains. Against this background, community detection …

We study the problem of approximating the number of k-cliques in a graph when given query
access to the graph. We consider the standard query model for general graphs via (1) …

Random walk is widely applied to sample large-scale graphs due to its simplicity of
implementation and solid theoretical foundations of bias analysis. However, its …

A network with n nodes contains O (n 2) possible links. Even for networks of modest size, it is
often difficult to evaluate all pairwise possibilities for links in a meaningful way. Further, even …

We present MaNIACS, a sampling-based randomized algorithm for computing high-quality
approximations of the collection of the subgraph patterns that are frequent in a single, large …

A knowledge graph (KG) manages large-scale and real-world facts as a big graph in a
schema-flexible manner. Aggregate query is a fundamental query over KGs, eg,“what is the …

The degree distribution is one of the most fundamental properties used in the analysis of
massive graphs. There is a large literature on graph sampling, where the goal is to estimate …

Given query access to an undirected graph G, we consider the problem of computing a (1±ε)-
approximation of the number of k-cliques in G. The standard query model for general graphs …

The study of a large real world network in terms of graph sample representation constitutes a
very powerful and useful tool in several domains of network analysis. This is the motivation …

“[A] llain Gersten, Hopfen, und Wasser”—1516 Reinheitsgebot We present Bavarian, a
collection of sampling-based algorithms for approximating the Betweenness Centrality (BC) …